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Summary of entries of Methods for bigframes.
bigframes.bigquery.approx_top_count
approx_top_count( series: bigframes.series.Series, number: int ) -> bigframes.series.SeriesReturns the approximate top elements of expression as an array of STRUCTs.
See more: bigframes.bigquery.approx_top_count
bigframes.bigquery.array_agg
array_agg( obj: groupby.SeriesGroupBy | groupby.DataFrameGroupBy, ) -> series.Series | dataframe.DataFrameGroup data and create arrays from selected columns, omitting NULLs to avoid BigQuery errors (NULLs not allowed in arrays).
See more: bigframes.bigquery.array_agg
bigframes.bigquery.array_length
array_length(series: bigframes.series.Series) -> bigframes.series.SeriesCompute the length of each array element in the Series.
See more: bigframes.bigquery.array_length
bigframes.bigquery.array_to_string
array_to_string( series: bigframes.series.Series, delimiter: str ) -> bigframes.series.SeriesConverts array elements within a Series into delimited strings.
See more: bigframes.bigquery.array_to_string
bigframes.bigquery.create_vector_index
create_vector_index( table_id: str, column_name: str, *, replace: bool = False, index_name: Optional[str] = None, distance_type="cosine", stored_column_names: Collection[str] = (), index_type: str = "ivf", ivf_options: Optional[Mapping] = None, tree_ah_options: Optional[Mapping] = None, session: Optional[bigframes.session.Session] = None ) -> NoneCreates a new vector index on a column of a table.
See more: bigframes.bigquery.create_vector_index
bigframes.bigquery.json_extract
json_extract( input: bigframes.series.Series, json_path: str ) -> bigframes.series.SeriesExtracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.
See more: bigframes.bigquery.json_extract
bigframes.bigquery.json_extract_array
json_extract_array( input: bigframes.series.Series, json_path: str = "$" ) -> bigframes.series.SeriesExtracts a JSON array and converts it to a SQL array of JSON-formatted STRING or JSON values.
See more: bigframes.bigquery.json_extract_array
bigframes.bigquery.json_extract_string_array
json_extract_string_array( input: bigframes.series.Series, json_path: str = "$", value_dtype: typing.Optional[ typing.Union[ pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], ] ] = None, ) -> bigframes.series.SeriesExtracts a JSON array and converts it to a SQL array of STRING values.
bigframes.bigquery.json_query
json_query( input: bigframes.series.Series, json_path: str ) -> bigframes.series.SeriesExtracts a JSON value and converts it to a SQL JSON-formatted STRING or JSON value.
See more: bigframes.bigquery.json_query
bigframes.bigquery.json_query_array
json_query_array( input: bigframes.series.Series, json_path: str = "$" ) -> bigframes.series.SeriesExtracts a JSON array and converts it to a SQL array of JSON-formatted STRING or JSON values.
See more: bigframes.bigquery.json_query_array
bigframes.bigquery.json_set
json_set( input: bigframes.series.Series, json_path_value_pairs: typing.Sequence[typing.Tuple[str, typing.Any]], ) -> bigframes.series.SeriesProduces a new JSON value within a Series by inserting or replacing values at specified paths.
See more: bigframes.bigquery.json_set
bigframes.bigquery.json_value
json_value( input: bigframes.series.Series, json_path: str = "$" ) -> bigframes.series.SeriesExtracts a JSON scalar value and converts it to a SQL STRING value.
See more: bigframes.bigquery.json_value
bigframes.bigquery.json_value_array
json_value_array( input: bigframes.series.Series, json_path: str = "$" ) -> bigframes.series.SeriesExtracts a JSON array of scalar values and converts it to a SQL ARRAY<STRING> value.
See more: bigframes.bigquery.json_value_array
bigframes.bigquery.parse_json
parse_json(input: bigframes.series.Series) -> bigframes.series.SeriesConverts a series with a JSON-formatted STRING value to a JSON value.
See more: bigframes.bigquery.parse_json
bigframes.bigquery.sql_scalar
sql_scalar( sql_template: str, columns: typing.Sequence[bigframes.series.Series] ) -> bigframes.series.SeriesCreate a Series from a SQL template.
See more: bigframes.bigquery.sql_scalar
bigframes.bigquery.st_area
st_area( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], ) -> bigframes.series.SeriesReturns the area in square meters covered by the polygons in the input GEOGRAPHY.
See more: bigframes.bigquery.st_area
bigframes.bigquery.st_buffer
st_buffer( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], buffer_radius: float, num_seg_quarter_circle: float = 8.0, use_spheroid: bool = False, ) -> bigframes.series.SeriesComputes a GEOGRAPHY that represents all points whose distance from the input GEOGRAPHY is less than or equal to distance meters.
See more: bigframes.bigquery.st_buffer
bigframes.bigquery.st_centroid
st_centroid( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], ) -> bigframes.series.SeriesComputes the geometric centroid of a GEOGRAPHY type.
See more: bigframes.bigquery.st_centroid
bigframes.bigquery.st_convexhull
st_convexhull( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], ) -> bigframes.series.SeriesComputes the convex hull of a GEOGRAPHY type.
See more: bigframes.bigquery.st_convexhull
bigframes.bigquery.st_difference
st_difference( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], other: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries, shapely.geometry.base.BaseGeometry, ], ) -> bigframes.series.SeriesReturns a GEOGRAPHY that represents the point set difference of geography_1 and geography_2.
See more: bigframes.bigquery.st_difference
bigframes.bigquery.st_distance
st_distance( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], other: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries, shapely.geometry.base.BaseGeometry, ], *, use_spheroid: bool = False ) -> bigframes.series.SeriesReturns the shortest distance in meters between two non-empty GEOGRAPHY objects.
See more: bigframes.bigquery.st_distance
bigframes.bigquery.st_intersection
st_intersection( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], other: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries, shapely.geometry.base.BaseGeometry, ], ) -> bigframes.series.SeriesReturns a GEOGRAPHY that represents the point set intersection of the two input GEOGRAPHYs.
See more: bigframes.bigquery.st_intersection
bigframes.bigquery.st_isclosed
st_isclosed( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], ) -> bigframes.series.SeriesReturns TRUE for a non-empty Geography, where each element in the Geography has an empty boundary.
See more: bigframes.bigquery.st_isclosed
bigframes.bigquery.st_length
st_length( series: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], *, use_spheroid: bool = False ) -> bigframes.series.SeriesReturns the total length in meters of the lines in the input GEOGRAPHY.
See more: bigframes.bigquery.st_length
bigframes.bigquery.st_regionstats
st_regionstats( geography: typing.Union[ bigframes.series.Series, bigframes.geopandas.geoseries.GeoSeries ], raster_id: str, band: typing.Optional[str] = None, include: typing.Optional[str] = None, options: typing.Optional[typing.Mapping[str, typing.Union[str, int, float]]] = None, ) -> bigframes.series.SeriesReturns statistics summarizing the pixel values of the raster image referenced by raster_id that intersect with geography.
See more: bigframes.bigquery.st_regionstats
bigframes.bigquery.st_simplify
st_simplify( geography: bigframes.series.Series, tolerance_meters: float ) -> bigframes.series.SeriesReturns a simplified version of the input geography.
See more: bigframes.bigquery.st_simplify
bigframes.bigquery.struct
struct(value: dataframe.DataFrame) -> series.SeriesTakes a DataFrame and converts it into a Series of structs with each struct entry corresponding to a DataFrame row and each struct field corresponding to a DataFrame column.
See more: bigframes.bigquery.struct
bigframes.bigquery.to_json
to_json(input: bigframes.series.Series) -> bigframes.series.SeriesConverts a series with a JSON value to a JSON-formatted STRING value.
See more: bigframes.bigquery.to_json
bigframes.bigquery.to_json_string
to_json_string(input: bigframes.series.Series) -> bigframes.series.SeriesConverts a series to a JSON-formatted STRING value.
See more: bigframes.bigquery.to_json_string
bigframes.bigquery.unix_micros
unix_micros(input: bigframes.series.Series) -> bigframes.series.SeriesConverts a timestmap series to unix epoch microseconds.
See more: bigframes.bigquery.unix_micros
bigframes.bigquery.unix_millis
unix_millis(input: bigframes.series.Series) -> bigframes.series.SeriesConverts a timestmap series to unix epoch milliseconds.
See more: bigframes.bigquery.unix_millis
bigframes.bigquery.unix_seconds
unix_seconds(input: bigframes.series.Series) -> bigframes.series.SeriesConverts a timestmap series to unix epoch seconds.
See more: bigframes.bigquery.unix_seconds
bigframes.bigquery.vector_search
vector_search( base_table: str, column_to_search: str, query: Union[dataframe.DataFrame, series.Series], *, query_column_to_search: Optional[str] = None, top_k: Optional[int] = None, distance_type: Optional[Literal["euclidean", "cosine", "dot_product"]] = None, fraction_lists_to_search: Optional[float] = None, use_brute_force: Optional[bool] = None, allow_large_results: Optional[bool] = None ) -> dataframe.DataFrameConduct vector search which searches embeddings to find semantically similar entities.
See more: bigframes.bigquery.vector_search
bigframes.bigquery._operations.ai.classify
classify( input: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], categories: tuple[str, ...] | list[str], *, connection_id: str | None = None ) -> bigframes.series.SeriesClassifies a given input into one of the specified categories.
bigframes.bigquery._operations.ai.forecast
forecast( df: bigframes.dataframe.DataFrame | pandas.core.frame.DataFrame, *, data_col: str, timestamp_col: str, model: str = "TimesFM 2.0", id_cols: typing.Optional[typing.Iterable[str]] = None, horizon: int = 10, confidence_level: float = 0.95, context_window: int | None = None ) -> bigframes.dataframe.DataFrameForecast time series at future horizon.
bigframes.bigquery._operations.ai.generate
generate( prompt: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], *, connection_id: str | None = None, endpoint: str | None = None, request_type: typing.Literal["dedicated", "shared", "unspecified"] = "unspecified", model_params: typing.Optional[typing.Mapping[typing.Any, typing.Any]] = None, output_schema: typing.Optional[typing.Mapping[str, str]] = None ) -> bigframes.series.SeriesReturns the AI analysis based on the prompt, which can be any combination of text and unstructured data.
bigframes.bigquery._operations.ai.generate_bool
generate_bool( prompt: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], *, connection_id: str | None = None, endpoint: str | None = None, request_type: typing.Literal["dedicated", "shared", "unspecified"] = "unspecified", model_params: typing.Optional[typing.Mapping[typing.Any, typing.Any]] = None ) -> bigframes.series.SeriesReturns the AI analysis based on the prompt, which can be any combination of text and unstructured data.
bigframes.bigquery._operations.ai.generate_double
generate_double( prompt: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], *, connection_id: str | None = None, endpoint: str | None = None, request_type: typing.Literal["dedicated", "shared", "unspecified"] = "unspecified", model_params: typing.Optional[typing.Mapping[typing.Any, typing.Any]] = None ) -> bigframes.series.SeriesReturns the AI analysis based on the prompt, which can be any combination of text and unstructured data.
bigframes.bigquery._operations.ai.generate_int
generate_int( prompt: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], *, connection_id: str | None = None, endpoint: str | None = None, request_type: typing.Literal["dedicated", "shared", "unspecified"] = "unspecified", model_params: typing.Optional[typing.Mapping[typing.Any, typing.Any]] = None ) -> bigframes.series.SeriesReturns the AI analysis based on the prompt, which can be any combination of text and unstructured data.
bigframes.bigquery._operations.ai.if_
if_( prompt: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], *, connection_id: str | None = None ) -> bigframes.series.SeriesEvaluates the prompt to True or False.
See more: bigframes.bigquery.operations.ai.if
bigframes.bigquery._operations.ai.score
score( prompt: typing.Union[ str, bigframes.series.Series, pandas.core.series.Series, typing.List[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series] ], typing.Tuple[ typing.Union[str, bigframes.series.Series, pandas.core.series.Series], ... ], ], *, connection_id: str | None = None ) -> bigframes.series.SeriesComputes a score based on rubrics described in natural language.
See more: bigframes.bigquery._operations.ai.score
bigframes.exceptions.format_message
format_message(message: str, fill: bool = True)Formats a warning message with ANSI color codes for the warning color.
See more: bigframes.exceptions.format_message
bigframes.ml.metrics.accuracy_score
accuracy_score( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, normalize=True ) -> floatAccuracy classification score.
See more: bigframes.ml.metrics.accuracy_score
bigframes.ml.metrics.auc
auc( x: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> floatCompute Area Under the Curve (AUC) using the trapezoidal rule.
See more: bigframes.ml.metrics.auc
bigframes.ml.metrics.confusion_matrix
confusion_matrix( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> pandas.core.frame.DataFrameCompute confusion matrix to evaluate the accuracy of a classification.
See more: bigframes.ml.metrics.confusion_matrix
bigframes.ml.metrics.f1_score
f1_score( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, average: typing.Optional[str] = "binary" ) -> pandas.core.series.SeriesCompute the F1 score, also known as balanced F-score or F-measure.
See more: bigframes.ml.metrics.f1_score
bigframes.ml.metrics.mean_absolute_error
mean_absolute_error( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> floatMean absolute error regression loss.
See more: bigframes.ml.metrics.mean_absolute_error
bigframes.ml.metrics.mean_squared_error
mean_squared_error( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> floatMean squared error regression loss.
See more: bigframes.ml.metrics.mean_squared_error
bigframes.ml.metrics.precision_score
precision_score( y_true: bigframes.dataframe.DataFrame | bigframes.series.Series, y_pred: bigframes.dataframe.DataFrame | bigframes.series.Series, *, pos_label: int | float | bool | str = 1, average: typing.Optional[typing.Literal["binary"]] = "binary" ) -> pandas.core.series.Series | floatCompute the precision.
See more: bigframes.ml.metrics.precision_score
bigframes.ml.metrics.r2_score
r2_score( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, force_finite=True ) -> float:math:R^2 (coefficient of determination) regression score function.
See more: bigframes.ml.metrics.r2_score
bigframes.ml.metrics.recall_score
recall_score( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_pred: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, average: typing.Optional[str] = "binary" ) -> pandas.core.series.SeriesCompute the recall.
See more: bigframes.ml.metrics.recall_score
bigframes.ml.metrics.roc_auc_score
roc_auc_score( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_score: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> floatCompute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
See more: bigframes.ml.metrics.roc_auc_score
bigframes.ml.metrics.roc_curve
roc_curve( y_true: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y_score: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, drop_intermediate: bool = True ) -> typing.Tuple[ bigframes.series.Series, bigframes.series.Series, bigframes.series.Series ]Compute Receiver operating characteristic (ROC).
See more: bigframes.ml.metrics.roc_curve
bigframes.ml.metrics.pairwise.paired_cosine_distances
paired_cosine_distances( X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], Y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> bigframes.dataframe.DataFrameCompute the paired cosine distances between X and Y.
See more: bigframes.ml.metrics.pairwise.paired_cosine_distances
bigframes.ml.metrics.pairwise.paired_euclidean_distances
paired_euclidean_distances( X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], Y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> bigframes.dataframe.DataFrameCompute the paired euclidean distances between X and Y.
See more: bigframes.ml.metrics.pairwise.paired_euclidean_distances
bigframes.ml.metrics.pairwise.paired_manhattan_distance
paired_manhattan_distance( X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], Y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ) -> bigframes.dataframe.DataFrameCompute the L1 distances between the vectors in X and Y.
See more: bigframes.ml.metrics.pairwise.paired_manhattan_distance
bigframes.ml.model_selection.cross_validate
cross_validate( estimator, X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, *, cv: typing.Optional[typing.Union[int, bigframes.ml.model_selection.KFold]] = None ) -> dict[str, list]Evaluate metric(s) by cross-validation and also record fit/score times.
bigframes.ml.model_selection.train_test_split
train_test_split( *arrays: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], test_size: typing.Optional[float] = None, train_size: typing.Optional[float] = None, random_state: typing.Optional[int] = None, stratify: typing.Optional[bigframes.series.Series] = None, shuffle: bool = True ) -> typing.List[typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]Splits dataframes or series into random train and test subsets.
bigframes.pandas.clean_up_by_session_id
clean_up_by_session_id( session_id: str, location: typing.Optional[str] = None, project: typing.Optional[str] = None, ) -> NoneSearches through BigQuery tables and routines and deletes the ones created during the session with the given session id.
See more: bigframes.pandas.clean_up_by_session_id
bigframes.pandas.close_session
close_session()Start a fresh session the next time a function requires a session.
See more: bigframes.pandas.close_session
bigframes.pandas.concat
concat( objs: typing.Iterable[ typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series] ], *, axis: typing.Union[str, int] = 0, join: typing.Literal["inner", "outer"] = "outer", ignore_index: bool = False ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Concatenate BigQuery DataFrames objects along a particular axis.
See more: bigframes.pandas.concat
bigframes.pandas.cut
cut( x, bins: typing.Union[ int, pandas.core.indexes.interval.IntervalIndex, typing.Iterable ], *, right: typing.Optional[bool] = True, labels: typing.Optional[typing.Union[typing.Iterable[str], bool]] = None, session: typing.Optional[bigframes.session.Session] = None ) -> bigframes.series.SeriesBin values into discrete intervals.
See more: bigframes.pandas.cut
bigframes.pandas.deploy_remote_function
deploy_remote_function(func, **kwargs)Orchestrates the creation of a BigQuery remote function that deploys immediately.
See more: bigframes.pandas.deploy_remote_function
bigframes.pandas.deploy_udf
deploy_udf(func, **kwargs)Orchestrates the creation of a BigQuery UDF that deploys immediately.
See more: bigframes.pandas.deploy_udf
bigframes.pandas.from_glob_path
from_glob_path( path: str, *, connection: typing.Optional[str] = None, name: typing.Optional[str] = None ) -> bigframes.dataframe.DataFrameCreate a BigFrames DataFrame that contains a BigFrames Blob column from a global wildcard path.
See more: bigframes.pandas.from_glob_path
bigframes.pandas.get_default_session_id
get_default_session_id() -> strGets the session id that is used whenever a custom session has not been provided.
See more: bigframes.pandas.get_default_session_id
bigframes.pandas.get_dummies
get_dummies( data: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], prefix: typing.Optional[typing.Union[typing.List, dict, str]] = None, prefix_sep: typing.Optional[typing.Union[typing.List, dict, str]] = "_", dummy_na: bool = False, columns: typing.Optional[typing.List] = None, drop_first: bool = False, dtype: typing.Any = None, ) -> bigframes.dataframe.DataFrameConvert categorical variable into dummy/indicator variables.
See more: bigframes.pandas.get_dummies
bigframes.pandas.get_global_session
get_global_session()Gets the global session.
See more: bigframes.pandas.get_global_session
bigframes.pandas.merge
merge( left: bigframes.dataframe.DataFrame, right: bigframes.dataframe.DataFrame, how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner", on: typing.Optional[ typing.Union[typing.Sequence[typing.Hashable], typing.Hashable] ] = None, *, left_on: typing.Optional[ typing.Union[typing.Sequence[typing.Hashable], typing.Hashable] ] = None, right_on: typing.Optional[ typing.Union[typing.Sequence[typing.Hashable], typing.Hashable] ] = None, left_index: bool = False, right_index: bool = False, sort: bool = False, suffixes: tuple[str, str] = ("_x", "_y") ) -> bigframes.dataframe.DataFrameMerge DataFrame objects with a database-style join.
See more: bigframes.pandas.merge
bigframes.pandas.qcut
qcut( x: bigframes.series.Series, q: typing.Union[int, typing.Sequence[float]], *, labels: typing.Optional[bool] = None, duplicates: typing.Literal["drop", "error"] = "error" ) -> bigframes.series.SeriesQuantile-based discretization function.
See more: bigframes.pandas.qcut
bigframes.pandas.read_arrow
read_arrow(pa_table: pyarrow.lib.Table) -> bigframes.dataframe.DataFrameLoad a PyArrow Table to a BigQuery DataFrames DataFrame.
See more: bigframes.pandas.read_arrow
bigframes.pandas.read_csv
read_csv( filepath_or_buffer: typing.Union[str, typing.IO[bytes]], *, sep: typing.Optional[str] = ",", header: typing.Optional[int] = 0, names: typing.Optional[ typing.Union[ typing.MutableSequence[typing.Any], numpy.ndarray[typing.Any, typing.Any], typing.Tuple[typing.Any, ...], range, ] ] = None, index_col: typing.Optional[ typing.Union[ int, str, typing.Sequence[typing.Union[str, int]], bigframes.enums.DefaultIndexKind, typing.Literal[False], ] ] = None, usecols: typing.Optional[ typing.Union[ typing.MutableSequence[str], typing.Tuple[str, ...], typing.Sequence[int], pandas.core.series.Series, pandas.core.indexes.base.Index, numpy.ndarray[typing.Any, typing.Any], typing.Callable[[typing.Any], bool], ] ] = None, dtype: typing.Optional[typing.Dict] = None, engine: typing.Optional[ typing.Literal["c", "python", "pyarrow", "python-fwf", "bigquery"] ] = None, encoding: typing.Optional[str] = None, write_engine: typing.Literal[ "default", "bigquery_inline", "bigquery_load", "bigquery_streaming", "bigquery_write", "_deferred", ] = "default", **kwargs ) -> bigframes.dataframe.DataFrameLoads data from a comma-separated values (csv) file into a DataFrame.
See more: bigframes.pandas.read_csv
bigframes.pandas.read_gbq
read_gbq( query_or_table: str, *, index_col: typing.Union[ typing.Iterable[str], str, bigframes.enums.DefaultIndexKind ] = (), columns: typing.Iterable[str] = (), configuration: typing.Optional[typing.Dict] = None, max_results: typing.Optional[int] = None, filters: typing.Union[ typing.Iterable[ typing.Tuple[ str, typing.Literal[ "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE" ], typing.Any, ] ], typing.Iterable[ typing.Iterable[ typing.Tuple[ str, typing.Literal[ "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE" ], typing.Any, ] ] ], ] = (), use_cache: typing.Optional[bool] = None, col_order: typing.Iterable[str] = (), dry_run: bool = False, allow_large_results: typing.Optional[bool] = None ) -> bigframes.dataframe.DataFrame | pandas.core.series.SeriesLoads a DataFrame from BigQuery.
See more: bigframes.pandas.read_gbq
bigframes.pandas.read_gbq_function
read_gbq_function(function_name: str, is_row_processor: bool = False)Loads a BigQuery function from BigQuery.
See more: bigframes.pandas.read_gbq_function
bigframes.pandas.read_gbq_model
read_gbq_model(model_name: str)Loads a BigQuery ML model from BigQuery.
See more: bigframes.pandas.read_gbq_model
bigframes.pandas.read_gbq_object_table
read_gbq_object_table( object_table: str, *, name: typing.Optional[str] = None ) -> bigframes.dataframe.DataFrameRead an existing object table to create a BigFrames Blob DataFrame.
See more: bigframes.pandas.read_gbq_object_table
bigframes.pandas.read_gbq_query
read_gbq_query( query: str, *, index_col: typing.Union[ typing.Iterable[str], str, bigframes.enums.DefaultIndexKind ] = (), columns: typing.Iterable[str] = (), configuration: typing.Optional[typing.Dict] = None, max_results: typing.Optional[int] = None, use_cache: typing.Optional[bool] = None, col_order: typing.Iterable[str] = (), filters: typing.Union[ typing.Iterable[ typing.Tuple[ str, typing.Literal[ "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE" ], typing.Any, ] ], typing.Iterable[ typing.Iterable[ typing.Tuple[ str, typing.Literal[ "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE" ], typing.Any, ] ] ], ] = (), dry_run: bool = False, allow_large_results: typing.Optional[bool] = None ) -> bigframes.dataframe.DataFrame | pandas.core.series.SeriesTurn a SQL query into a DataFrame.
See more: bigframes.pandas.read_gbq_query
bigframes.pandas.read_gbq_table
read_gbq_table( query: str, *, index_col: typing.Union[ typing.Iterable[str], str, bigframes.enums.DefaultIndexKind ] = (), columns: typing.Iterable[str] = (), max_results: typing.Optional[int] = None, filters: typing.Union[ typing.Iterable[ typing.Tuple[ str, typing.Literal[ "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE" ], typing.Any, ] ], typing.Iterable[ typing.Iterable[ typing.Tuple[ str, typing.Literal[ "in", "not in", "<",><=", "="=" ,"="" "!=", ">=", ">", "LIKE" ], typing.Any, ] ] ], ] = (), use_cache: bool = True, col_order: typing.Iterable[str] = (), dry_run: bool = False ) -> bigframes.dataframe.DataFrame | pandas.core.series.SeriesTurn a BigQuery table into a DataFrame.
See more: bigframes.pandas.read_gbq_table
bigframes.pandas.read_json
read_json( path_or_buf: typing.Union[str, typing.IO[bytes]], *, orient: typing.Literal[ "split", "records", "index", "columns", "values", "table" ] = "columns", dtype: typing.Optional[typing.Dict] = None, encoding: typing.Optional[str] = None, lines: bool = False, engine: typing.Literal["ujson", "pyarrow", "bigquery"] = "ujson", write_engine: typing.Literal[ "default", "bigquery_inline", "bigquery_load", "bigquery_streaming", "bigquery_write", "_deferred", ] = "default", **kwargs ) -> bigframes.dataframe.DataFrameConvert a JSON string to DataFrame object.
See more: bigframes.pandas.read_json
bigframes.pandas.read_pandas
read_pandas( pandas_dataframe: typing.Union[ pandas.core.frame.DataFrame, pandas.core.series.Series, pandas.core.indexes.base.Index, ], *, write_engine: typing.Literal[ "default", "bigquery_inline", "bigquery_load", "bigquery_streaming", "bigquery_write", "_deferred", ] = "default" )Loads DataFrame from a pandas DataFrame.
See more: bigframes.pandas.read_pandas
bigframes.pandas.read_parquet
read_parquet( path: typing.Union[str, typing.IO[bytes]], *, engine: str = "auto", write_engine: typing.Literal[ "default", "bigquery_inline", "bigquery_load", "bigquery_streaming", "bigquery_write", "_deferred", ] = "default" ) -> bigframes.dataframe.DataFrameLoad a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.
See more: bigframes.pandas.read_parquet
bigframes.pandas.read_pickle
read_pickle( filepath_or_buffer: FilePath | ReadPickleBuffer, compression: CompressionOptions = "infer", storage_options: StorageOptions = None, *, write_engine: constants.WriteEngineType = "default" )Load pickled BigFrames object (or any object) from file.
See more: bigframes.pandas.read_pickle
bigframes.pandas.remote_function
remote_function( input_types: typing.Union[None, type, typing.Sequence[type]] = None, output_type: typing.Optional[type] = None, dataset: typing.Optional[str] = None, *, bigquery_connection: typing.Optional[str] = None, reuse: bool = True, name: typing.Optional[str] = None, packages: typing.Optional[typing.Sequence[str]] = None, cloud_function_service_account: str, cloud_function_kms_key_name: typing.Optional[str] = None, cloud_function_docker_repository: typing.Optional[str] = None, max_batching_rows: typing.Optional[int] = 1000, cloud_function_timeout: typing.Optional[int] = 600, cloud_function_max_instances: typing.Optional[int] = None, cloud_function_vpc_connector: typing.Optional[str] = None, cloud_function_vpc_connector_egress_settings: typing.Optional[ typing.Literal["all", "private-ranges-only", "unspecified"] ] = None, cloud_function_memory_mib: typing.Optional[int] = 1024, cloud_function_ingress_settings: typing.Literal[ "all", "internal-only", "internal-and-gclb" ] = "internal-only", cloud_build_service_account: typing.Optional[str] = None )Decorator to turn a user defined function into a BigQuery remote function.
See more: bigframes.pandas.remote_function
bigframes.pandas.reset_session
reset_session()Start a fresh session the next time a function requires a session.
See more: bigframes.pandas.reset_session
bigframes.pandas.to_datetime
This function converts a scalar, array-like or Series to a datetime object.
See more: bigframes.pandas.to_datetime
bigframes.pandas.to_timedelta
to_timedelta( arg, unit: typing.Optional[ typing.Literal[ "W", "w", "D", "d", "days", "day", "hours", "hour", "hr", "h", "m", "minute", "min", "minutes", "s", "seconds", "sec", "second", "ms", "milliseconds", "millisecond", "milli", "millis", "us", "microseconds", "microsecond", "µs", "micro", "micros", ] ] = None, *, session: typing.Optional[bigframes.session.Session] = None )Converts a scalar or Series to a timedelta object.
See more: bigframes.pandas.to_timedelta
bigframes.pandas.udf
udf( *, input_types: typing.Union[None, type, typing.Sequence[type]] = None, output_type: typing.Optional[type] = None, dataset: str, bigquery_connection: typing.Optional[str] = None, name: str, packages: typing.Optional[typing.Sequence[str]] = None, max_batching_rows: typing.Optional[int] = None, container_cpu: typing.Optional[float] = None, container_memory: typing.Optional[str] = None )Decorator to turn a Python user defined function (udf) into a BigQuery managed user-defined function.
See more: bigframes.pandas.udf
bigframes.streaming.read_gbq_table
read_gbq_table(table: str) -> bigframes.streaming.dataframe.StreamingDataFrameTurn a BigQuery table into a StreamingDataFrame.
See more: bigframes.streaming.read_gbq_table
bigframes._config.Options.reset
reset() -> bigframes._config.OptionsReset the option settings to defaults.
See more: bigframes._config.Options.reset
bigframes._config.compute_options.ComputeOptions.assign_extra_query_labels
assign_extra_query_labels(**kwargs: typing.Any) -> NoneAssigns additional custom labels for query configuration.
See more: bigframes._config.compute_options.ComputeOptions.assign_extra_query_labels
bigframes._config.sampling_options.SamplingOptions.with_disabled
with_disabled() -> bigframes._config.sampling_options.SamplingOptionsConfigures whether to disable downsampling .
See more: bigframes._config.sampling_options.SamplingOptions.with_disabled
bigframes._config.sampling_options.SamplingOptions.with_max_download_size
with_max_download_size( max_rows: typing.Optional[int], ) -> bigframes._config.sampling_options.SamplingOptionsConfigures the maximum download size for data sampling in MB .
See more: bigframes._config.sampling_options.SamplingOptions.with_max_download_size
bigframes._config.sampling_options.SamplingOptions.with_method
with_method( method: typing.Literal["head", "uniform"], ) -> bigframes._config.sampling_options.SamplingOptionsConfigures the downsampling algorithms to be chosen from .
See more: bigframes._config.sampling_options.SamplingOptions.with_method
bigframes._config.sampling_options.SamplingOptions.with_random_state
with_random_state( state: typing.Optional[int], ) -> bigframes._config.sampling_options.SamplingOptionsConfigures the seed for the uniform downsampling algorithm .
See more: bigframes._config.sampling_options.SamplingOptions.with_random_state
bigframes.core.groupby.DataFrameGroupBy.agg
agg( func=None, **kwargs ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Aggregate using one or more operations.
bigframes.core.groupby.DataFrameGroupBy.aggregate
aggregate( func=None, **kwargs ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Aggregate using one or more operations.
bigframes.core.groupby.DataFrameGroupBy.all
all() -> bigframes.dataframe.DataFrameReturn True if all values in the group are true, else False.
bigframes.core.groupby.DataFrameGroupBy.any
any() -> bigframes.dataframe.DataFrameReturn True if any value in the group is true, else False.
bigframes.core.groupby.DataFrameGroupBy.corr
corr(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameCompute pairwise correlation of columns, excluding NA/null values.
bigframes.core.groupby.DataFrameGroupBy.count
count() -> bigframes.dataframe.DataFrameCompute count of group, excluding missing values.
bigframes.core.groupby.DataFrameGroupBy.cov
cov(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameCompute pairwise covariance of columns, excluding NA/null values.
bigframes.core.groupby.DataFrameGroupBy.cumcount
cumcount(ascending: bool = True) -> bigframes.series.SeriesNumber each item in each group from 0 to the length of that group - 1.
bigframes.core.groupby.DataFrameGroupBy.cummax
cummax( *args, numeric_only: bool = False, **kwargs ) -> bigframes.dataframe.DataFrameCumulative max for each group.
bigframes.core.groupby.DataFrameGroupBy.cummin
cummin( *args, numeric_only: bool = False, **kwargs ) -> bigframes.dataframe.DataFrameCumulative min for each group.
bigframes.core.groupby.DataFrameGroupBy.cumprod
cumprod(*args, **kwargs) -> bigframes.dataframe.DataFrameCumulative product for each group.
bigframes.core.groupby.DataFrameGroupBy.cumsum
cumsum( *args, numeric_only: bool = False, **kwargs ) -> bigframes.dataframe.DataFrameCumulative sum for each group.
bigframes.core.groupby.DataFrameGroupBy.describe
describe(include: typing.Union[None, typing.Literal["all"]] = None)Generate descriptive statistics.
bigframes.core.groupby.DataFrameGroupBy.diff
diff(periods=1) -> bigframes.series.SeriesFirst discrete difference of element.
bigframes.core.groupby.DataFrameGroupBy.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvides expanding functionality.
bigframes.core.groupby.DataFrameGroupBy.first
first( numeric_only: bool = False, min_count: int = -1 ) -> bigframes.dataframe.DataFrameCompute the first entry of each column within each group.
bigframes.core.groupby.DataFrameGroupBy.head
head(n: int = 5) -> bigframes.dataframe.DataFrameReturn last first n rows of each group.
bigframes.core.groupby.DataFrameGroupBy.kurt
kurt(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameReturn unbiased kurtosis over requested axis.
bigframes.core.groupby.DataFrameGroupBy.kurtosis
kurtosis(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameReturn unbiased kurtosis over requested axis.
bigframes.core.groupby.DataFrameGroupBy.last
last( numeric_only: bool = False, min_count: int = -1 ) -> bigframes.dataframe.DataFrameCompute the last entry of each column within each group.
bigframes.core.groupby.DataFrameGroupBy.max
max(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrameCompute max of group values.
bigframes.core.groupby.DataFrameGroupBy.mean
mean(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrameCompute mean of groups, excluding missing values.
bigframes.core.groupby.DataFrameGroupBy.median
median( numeric_only: bool = False, *, exact: bool = True ) -> bigframes.dataframe.DataFrameCompute median of groups, excluding missing values.
bigframes.core.groupby.DataFrameGroupBy.min
min(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrameCompute min of group values.
bigframes.core.groupby.DataFrameGroupBy.nunique
nunique() -> bigframes.dataframe.DataFrameReturn DataFrame with counts of unique elements in each position.
bigframes.core.groupby.DataFrameGroupBy.prod
prod(numeric_only: bool = False, min_count: int = 0)Compute prod of group values.
bigframes.core.groupby.DataFrameGroupBy.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False ) -> bigframes.dataframe.DataFrameReturn group values at the given quantile, a la numpy.percentile.
bigframes.core.groupby.DataFrameGroupBy.rank
rank( method="average", ascending: bool = True, na_option: str = "keep", pct: bool = False ) -> bigframes.dataframe.DataFrameProvide the rank of values within each group.
bigframes.core.groupby.DataFrameGroupBy.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods=None, on: str | None = None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowReturns a rolling grouper, providing rolling functionality per group.
bigframes.core.groupby.DataFrameGroupBy.shift
shift(periods=1) -> bigframes.series.SeriesShift each group by periods observations.
bigframes.core.groupby.DataFrameGroupBy.size
size() -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Compute group sizes.
bigframes.core.groupby.DataFrameGroupBy.skew
skew(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameReturn unbiased skew within groups.
bigframes.core.groupby.DataFrameGroupBy.std
std(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameCompute standard deviation of groups, excluding missing values.
bigframes.core.groupby.DataFrameGroupBy.sum
sum(numeric_only: bool = False, *args) -> bigframes.dataframe.DataFrameCompute sum of group values.
bigframes.core.groupby.DataFrameGroupBy.value_counts
value_counts( subset: typing.Optional[typing.Sequence[typing.Hashable]] = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True, ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Return a Series or DataFrame containing counts of unique rows.
See more: bigframes.core.groupby.DataFrameGroupBy.value_counts
bigframes.core.groupby.DataFrameGroupBy.var
var(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameCompute variance of groups, excluding missing values.
bigframes.core.groupby.SeriesGroupBy.agg
agg( func=None, ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Aggregate using one or more operations.
See more: bigframes.core.groupby.SeriesGroupBy.agg
bigframes.core.groupby.SeriesGroupBy.aggregate
aggregate( func=None, ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Aggregate using one or more operations.
bigframes.core.groupby.SeriesGroupBy.all
all() -> bigframes.series.SeriesReturn True if all values in the group are true, else False.
See more: bigframes.core.groupby.SeriesGroupBy.all
bigframes.core.groupby.SeriesGroupBy.any
any() -> bigframes.series.SeriesReturn True if any value in the group is true, else False.
See more: bigframes.core.groupby.SeriesGroupBy.any
bigframes.core.groupby.SeriesGroupBy.count
count() -> bigframes.series.SeriesCompute count of group, excluding missing values.
bigframes.core.groupby.SeriesGroupBy.cumcount
cumcount(*args, **kwargs) -> bigframes.series.SeriesNumber each item in each group from 0 to the length of that group - 1.
bigframes.core.groupby.SeriesGroupBy.cummax
cummax(*args, **kwargs) -> bigframes.series.SeriesCumulative max for each group.
bigframes.core.groupby.SeriesGroupBy.cummin
cummin(*args, **kwargs) -> bigframes.series.SeriesCumulative min for each group.
bigframes.core.groupby.SeriesGroupBy.cumprod
cumprod(*args, **kwargs) -> bigframes.series.SeriesCumulative product for each group.
bigframes.core.groupby.SeriesGroupBy.cumsum
cumsum(*args, **kwargs) -> bigframes.series.SeriesCumulative sum for each group.
bigframes.core.groupby.SeriesGroupBy.describe
describe(include: typing.Union[None, typing.Literal["all"]] = None)Generate descriptive statistics.
bigframes.core.groupby.SeriesGroupBy.diff
diff(periods=1) -> bigframes.series.SeriesFirst discrete difference of element.
bigframes.core.groupby.SeriesGroupBy.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvides expanding functionality.
bigframes.core.groupby.SeriesGroupBy.first
first(numeric_only: bool = False, min_count: int = -1) -> bigframes.series.SeriesCompute the first entry of each column within each group.
bigframes.core.groupby.SeriesGroupBy.head
head(n: int = 5) -> bigframes.series.SeriesReturn last first n rows of each group.
bigframes.core.groupby.SeriesGroupBy.kurt
kurt(*args, **kwargs) -> bigframes.series.SeriesReturn unbiased kurtosis over requested axis.
bigframes.core.groupby.SeriesGroupBy.kurtosis
kurtosis(*args, **kwargs) -> bigframes.series.SeriesReturn unbiased kurtosis over requested axis.
bigframes.core.groupby.SeriesGroupBy.last
last(numeric_only: bool = False, min_count: int = -1) -> bigframes.series.SeriesCompute the last entry of each column within each group.
bigframes.core.groupby.SeriesGroupBy.max
max(*args) -> bigframes.series.SeriesCompute max of group values.
See more: bigframes.core.groupby.SeriesGroupBy.max
bigframes.core.groupby.SeriesGroupBy.mean
mean(*args) -> bigframes.series.SeriesCompute mean of groups, excluding missing values.
bigframes.core.groupby.SeriesGroupBy.median
median(*args, exact: bool = True, **kwargs) -> bigframes.series.SeriesCompute median of groups, excluding missing values.
bigframes.core.groupby.SeriesGroupBy.min
min(*args) -> bigframes.series.SeriesCompute min of group values.
See more: bigframes.core.groupby.SeriesGroupBy.min
bigframes.core.groupby.SeriesGroupBy.nunique
nunique() -> bigframes.series.SeriesReturn number of unique elements in the group.
bigframes.core.groupby.SeriesGroupBy.prod
prod(*args) -> bigframes.series.SeriesCompute prod of group values.
bigframes.core.groupby.SeriesGroupBy.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn group values at the given quantile, a la numpy.percentile.
bigframes.core.groupby.SeriesGroupBy.rank
rank( method="average", ascending: bool = True, na_option: str = "keep", pct: bool = False ) -> bigframes.series.SeriesProvide the rank of values within each group.
bigframes.core.groupby.SeriesGroupBy.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods=None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowReturns a rolling grouper, providing rolling functionality per group.
bigframes.core.groupby.SeriesGroupBy.shift
shift(periods=1) -> bigframes.series.SeriesShift index by desired number of periods.
bigframes.core.groupby.SeriesGroupBy.size
size() -> bigframes.series.SeriesCompute group sizes.
bigframes.core.groupby.SeriesGroupBy.skew
skew(*args, **kwargs) -> bigframes.series.SeriesReturn unbiased skew within groups.
bigframes.core.groupby.SeriesGroupBy.std
std(*args, **kwargs) -> bigframes.series.SeriesCompute standard deviation of groups, excluding missing values.
See more: bigframes.core.groupby.SeriesGroupBy.std
bigframes.core.groupby.SeriesGroupBy.sum
sum(*args) -> bigframes.series.SeriesCompute sum of group values.
See more: bigframes.core.groupby.SeriesGroupBy.sum
bigframes.core.groupby.SeriesGroupBy.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True, ) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]Return a Series or DataFrame containing counts of unique rows.
bigframes.core.groupby.SeriesGroupBy.var
var(*args, **kwargs) -> bigframes.series.SeriesCompute variance of groups, excluding missing values.
See more: bigframes.core.groupby.SeriesGroupBy.var
bigframes.core.indexers.ILocDataFrameIndexer.__getitem__
__getitem__( key, ) -> typing.Union[bigframes.dataframe.DataFrame, pandas.core.series.Series]Index dataframe using integer offsets.
See more: bigframes.core.indexers.ILocDataFrameIndexer.getitem
bigframes.core.indexers.IlocSeriesIndexer.__getitem__
__getitem__(key) -> typing.Union[typing.Any, bigframes.series.Series]Index series using integer offsets.
bigframes.core.indexes.base.Index.__setitem__
__setitem__(key, value) -> NoneIndex objects are immutable.
bigframes.core.indexes.base.Index.all
all() -> boolReturn whether all elements are Truthy.
See more: bigframes.core.indexes.base.Index.all
bigframes.core.indexes.base.Index.any
any() -> boolReturn whether any element is Truthy.
See more: bigframes.core.indexes.base.Index.any
bigframes.core.indexes.base.Index.argmax
argmax() -> intReturn int position of the largest value in the Series.
See more: bigframes.core.indexes.base.Index.argmax
bigframes.core.indexes.base.Index.argmin
argmin() -> intReturn int position of the smallest value in the series.
See more: bigframes.core.indexes.base.Index.argmin
bigframes.core.indexes.base.Index.astype
astype( dtype, *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.core.indexes.base.IndexCreate an Index with values cast to dtypes.
See more: bigframes.core.indexes.base.Index.astype
bigframes.core.indexes.base.Index.copy
copy(name: typing.Optional[typing.Hashable] = None)Make a copy of this object.
See more: bigframes.core.indexes.base.Index.copy
bigframes.core.indexes.base.Index.drop
drop(labels: typing.Any) -> bigframes.core.indexes.base.IndexMake new Index with passed list of labels deleted.
See more: bigframes.core.indexes.base.Index.drop
bigframes.core.indexes.base.Index.drop_duplicates
drop_duplicates(*, keep: __builtins__.str = "first") -> IndexReturn Index with duplicate values removed.
bigframes.core.indexes.base.Index.dropna
dropna( how: typing.Literal["all", "any"] = "any", ) -> bigframes.core.indexes.base.IndexReturn Index without NA/NaN values.
See more: bigframes.core.indexes.base.Index.dropna
bigframes.core.indexes.base.Index.fillna
fillna(value=None) -> bigframes.core.indexes.base.IndexFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.core.indexes.base.Index.fillna
bigframes.core.indexes.base.Index.from_frame
from_frame( frame: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> bigframes.core.indexes.base.IndexMake a MultiIndex from a DataFrame.
bigframes.core.indexes.base.Index.get_level_values
get_level_values(level) -> bigframes.core.indexes.base.IndexReturn an Index of values for requested level.
See more: bigframes.core.indexes.base.Index.get_level_values
bigframes.core.indexes.base.Index.get_loc
get_loc(key) -> typing.Union[int, slice, bigframes.series.Series]Get integer location, slice or boolean mask for requested label.
bigframes.core.indexes.base.Index.isin
isin(values) -> bigframes.core.indexes.base.IndexReturn a boolean array where the index values are in values.
See more: bigframes.core.indexes.base.Index.isin
bigframes.core.indexes.base.Index.item
item()Return the first element of the underlying data as a Python scalar.
See more: bigframes.core.indexes.base.Index.item
bigframes.core.indexes.base.Index.max
max() -> typing.AnyReturn the maximum value of the Index.
See more: bigframes.core.indexes.base.Index.max
bigframes.core.indexes.base.Index.min
min() -> typing.AnyReturn the minimum value of the Index.
See more: bigframes.core.indexes.base.Index.min
bigframes.core.indexes.base.Index.nunique
nunique() -> intReturn number of unique elements in the object.
bigframes.core.indexes.base.Index.rename
Alter Index or MultiIndex name.
See more: bigframes.core.indexes.base.Index.rename
bigframes.core.indexes.base.Index.sort_values
sort_values( *, inplace: bool = False, ascending: bool = True, na_position: __builtins__.str = "last" ) -> IndexReturn a sorted copy of the index.
bigframes.core.indexes.base.Index.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listAPI documentation for to_list method.
bigframes.core.indexes.base.Index.to_numpy
to_numpy(dtype=None, *, allow_large_results=None, **kwargs) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
bigframes.core.indexes.base.Index.to_pandas
Gets the Index as a pandas Index.
bigframes.core.indexes.base.Index.to_series
to_series( index: typing.Optional[bigframes.core.indexes.base.Index] = None, name: typing.Optional[typing.Hashable] = None, ) -> bigframes.series.SeriesCreate a Series with both index and values equal to the index keys.
bigframes.core.indexes.base.Index.transpose
transpose() -> bigframes.core.indexes.base.IndexReturn the transpose, which is by definition self.
bigframes.core.indexes.base.Index.unique
unique( level: typing.Optional[typing.Union[typing.Hashable, int]] = None, ) -> bigframes.core.indexes.base.IndexReturns unique values in the index.
See more: bigframes.core.indexes.base.Index.unique
bigframes.core.indexes.base.Index.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
bigframes.core.indexes.datetimes.DatetimeIndex.__setitem__
__setitem__(key, value) -> NoneIndex objects are immutable.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.setitem
bigframes.core.indexes.datetimes.DatetimeIndex.all
all() -> boolReturn whether all elements are Truthy.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.all
bigframes.core.indexes.datetimes.DatetimeIndex.any
any() -> boolReturn whether any element is Truthy.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.any
bigframes.core.indexes.datetimes.DatetimeIndex.argmax
argmax() -> intReturn int position of the largest value in the Series.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.argmax
bigframes.core.indexes.datetimes.DatetimeIndex.argmin
argmin() -> intReturn int position of the smallest value in the series.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.argmin
bigframes.core.indexes.datetimes.DatetimeIndex.astype
astype( dtype, *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.core.indexes.base.IndexCreate an Index with values cast to dtypes.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.astype
bigframes.core.indexes.datetimes.DatetimeIndex.copy
copy(name: typing.Optional[typing.Hashable] = None)Make a copy of this object.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.copy
bigframes.core.indexes.datetimes.DatetimeIndex.drop
drop(labels: typing.Any) -> bigframes.core.indexes.base.IndexMake new Index with passed list of labels deleted.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.drop
bigframes.core.indexes.datetimes.DatetimeIndex.drop_duplicates
drop_duplicates(*, keep: __builtins__.str = "first") -> IndexReturn Index with duplicate values removed.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.drop_duplicates
bigframes.core.indexes.datetimes.DatetimeIndex.dropna
dropna( how: typing.Literal["all", "any"] = "any", ) -> bigframes.core.indexes.base.IndexReturn Index without NA/NaN values.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.dropna
bigframes.core.indexes.datetimes.DatetimeIndex.fillna
fillna(value=None) -> bigframes.core.indexes.base.IndexFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.fillna
bigframes.core.indexes.datetimes.DatetimeIndex.from_frame
from_frame( frame: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> bigframes.core.indexes.base.IndexMake a MultiIndex from a DataFrame.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.from_frame
bigframes.core.indexes.datetimes.DatetimeIndex.get_level_values
get_level_values(level) -> bigframes.core.indexes.base.IndexReturn an Index of values for requested level.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.get_level_values
bigframes.core.indexes.datetimes.DatetimeIndex.get_loc
get_loc(key) -> typing.Union[int, slice, bigframes.series.Series]Get integer location, slice or boolean mask for requested label.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.get_loc
bigframes.core.indexes.datetimes.DatetimeIndex.isin
isin(values) -> bigframes.core.indexes.base.IndexReturn a boolean array where the index values are in values.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.isin
bigframes.core.indexes.datetimes.DatetimeIndex.item
item()Return the first element of the underlying data as a Python scalar.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.item
bigframes.core.indexes.datetimes.DatetimeIndex.max
max() -> typing.AnyReturn the maximum value of the Index.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.max
bigframes.core.indexes.datetimes.DatetimeIndex.min
min() -> typing.AnyReturn the minimum value of the Index.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.min
bigframes.core.indexes.datetimes.DatetimeIndex.nunique
nunique() -> intReturn number of unique elements in the object.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.nunique
bigframes.core.indexes.datetimes.DatetimeIndex.rename
rename( name: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], *, inplace: bool = False ) -> typing.Optional[bigframes.core.indexes.base.Index]Alter Index or MultiIndex name.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.rename
bigframes.core.indexes.datetimes.DatetimeIndex.sort_values
sort_values( *, inplace: bool = False, ascending: bool = True, na_position: __builtins__.str = "last" ) -> IndexReturn a sorted copy of the index.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.sort_values
bigframes.core.indexes.datetimes.DatetimeIndex.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listAPI documentation for to_list method.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.to_list
bigframes.core.indexes.datetimes.DatetimeIndex.to_numpy
to_numpy(dtype=None, *, allow_large_results=None, **kwargs) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.to_numpy
bigframes.core.indexes.datetimes.DatetimeIndex.to_pandas
to_pandas( *, allow_large_results: typing.Optional[bool] = None, dry_run: bool = False ) -> pandas.core.indexes.base.Index | pandas.core.series.SeriesGets the Index as a pandas Index.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.to_pandas
bigframes.core.indexes.datetimes.DatetimeIndex.to_series
to_series( index: typing.Optional[bigframes.core.indexes.base.Index] = None, name: typing.Optional[typing.Hashable] = None, ) -> bigframes.series.SeriesCreate a Series with both index and values equal to the index keys.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.to_series
bigframes.core.indexes.datetimes.DatetimeIndex.transpose
transpose() -> bigframes.core.indexes.base.IndexReturn the transpose, which is by definition self.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.transpose
bigframes.core.indexes.datetimes.DatetimeIndex.unique
unique( level: typing.Optional[typing.Union[typing.Hashable, int]] = None, ) -> bigframes.core.indexes.base.IndexReturns unique values in the index.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.unique
bigframes.core.indexes.datetimes.DatetimeIndex.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
See more: bigframes.core.indexes.datetimes.DatetimeIndex.value_counts
bigframes.core.indexes.multi.MultiIndex.__setitem__
__setitem__(key, value) -> NoneIndex objects are immutable.
bigframes.core.indexes.multi.MultiIndex.all
all() -> boolReturn whether all elements are Truthy.
bigframes.core.indexes.multi.MultiIndex.any
any() -> boolReturn whether any element is Truthy.
bigframes.core.indexes.multi.MultiIndex.argmax
argmax() -> intReturn int position of the largest value in the Series.
bigframes.core.indexes.multi.MultiIndex.argmin
argmin() -> intReturn int position of the smallest value in the series.
bigframes.core.indexes.multi.MultiIndex.astype
astype( dtype, *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.core.indexes.base.IndexCreate an Index with values cast to dtypes.
bigframes.core.indexes.multi.MultiIndex.copy
copy(name: typing.Optional[typing.Hashable] = None)Make a copy of this object.
bigframes.core.indexes.multi.MultiIndex.drop
drop(labels: typing.Any) -> bigframes.core.indexes.base.IndexMake new Index with passed list of labels deleted.
bigframes.core.indexes.multi.MultiIndex.drop_duplicates
drop_duplicates(*, keep: __builtins__.str = "first") -> IndexReturn Index with duplicate values removed.
See more: bigframes.core.indexes.multi.MultiIndex.drop_duplicates
bigframes.core.indexes.multi.MultiIndex.dropna
dropna( how: typing.Literal["all", "any"] = "any", ) -> bigframes.core.indexes.base.IndexReturn Index without NA/NaN values.
bigframes.core.indexes.multi.MultiIndex.fillna
fillna(value=None) -> bigframes.core.indexes.base.IndexFill NA (NULL in BigQuery) values using the specified method.
bigframes.core.indexes.multi.MultiIndex.from_arrays
from_arrays( arrays, sortorder: int | None = None, names=None, *, session: Optional[bigframes.session.Session] = None ) -> MultiIndexConvert arrays to MultiIndex.
See more: bigframes.core.indexes.multi.MultiIndex.from_arrays
bigframes.core.indexes.multi.MultiIndex.from_frame
from_frame( frame: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> bigframes.core.indexes.base.IndexMake a MultiIndex from a DataFrame.
See more: bigframes.core.indexes.multi.MultiIndex.from_frame
bigframes.core.indexes.multi.MultiIndex.from_tuples
from_tuples( tuples: Iterable[tuple[Hashable, ...]], sortorder: int | None = None, names: Sequence[Hashable] | Hashable | None = None, *, session: Optional[bigframes.session.Session] = None ) -> MultiIndexConvert list of tuples to MultiIndex.
See more: bigframes.core.indexes.multi.MultiIndex.from_tuples
bigframes.core.indexes.multi.MultiIndex.get_level_values
get_level_values(level) -> bigframes.core.indexes.base.IndexReturn an Index of values for requested level.
See more: bigframes.core.indexes.multi.MultiIndex.get_level_values
bigframes.core.indexes.multi.MultiIndex.get_loc
get_loc(key) -> typing.Union[int, slice, bigframes.series.Series]Get integer location, slice or boolean mask for requested label.
bigframes.core.indexes.multi.MultiIndex.isin
isin(values) -> bigframes.core.indexes.base.IndexReturn a boolean array where the index values are in values.
bigframes.core.indexes.multi.MultiIndex.item
item()Return the first element of the underlying data as a Python scalar.
bigframes.core.indexes.multi.MultiIndex.max
max() -> typing.AnyReturn the maximum value of the Index.
bigframes.core.indexes.multi.MultiIndex.min
min() -> typing.AnyReturn the minimum value of the Index.
bigframes.core.indexes.multi.MultiIndex.nunique
nunique() -> intReturn number of unique elements in the object.
bigframes.core.indexes.multi.MultiIndex.rename
rename( name: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], *, inplace: bool = False ) -> typing.Optional[bigframes.core.indexes.base.Index]Alter Index or MultiIndex name.
bigframes.core.indexes.multi.MultiIndex.sort_values
sort_values( *, inplace: bool = False, ascending: bool = True, na_position: __builtins__.str = "last" ) -> IndexReturn a sorted copy of the index.
See more: bigframes.core.indexes.multi.MultiIndex.sort_values
bigframes.core.indexes.multi.MultiIndex.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listAPI documentation for to_list method.
bigframes.core.indexes.multi.MultiIndex.to_numpy
to_numpy(dtype=None, *, allow_large_results=None, **kwargs) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
bigframes.core.indexes.multi.MultiIndex.to_pandas
to_pandas( *, allow_large_results: typing.Optional[bool] = None, dry_run: bool = False ) -> pandas.core.indexes.base.Index | pandas.core.series.SeriesGets the Index as a pandas Index.
bigframes.core.indexes.multi.MultiIndex.to_series
to_series( index: typing.Optional[bigframes.core.indexes.base.Index] = None, name: typing.Optional[typing.Hashable] = None, ) -> bigframes.series.SeriesCreate a Series with both index and values equal to the index keys.
bigframes.core.indexes.multi.MultiIndex.transpose
transpose() -> bigframes.core.indexes.base.IndexReturn the transpose, which is by definition self.
bigframes.core.indexes.multi.MultiIndex.unique
unique( level: typing.Optional[typing.Union[typing.Hashable, int]] = None, ) -> bigframes.core.indexes.base.IndexReturns unique values in the index.
bigframes.core.indexes.multi.MultiIndex.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
See more: bigframes.core.indexes.multi.MultiIndex.value_counts
bigframes.core.window.Window.count
count()Calculate the window count of non-NULL observations.
See more: bigframes.core.window.Window.count
bigframes.core.window.Window.max
max()Calculate the weighted window maximum.
See more: bigframes.core.window.Window.max
bigframes.core.window.Window.mean
mean()Calculate the weighted window mean.
See more: bigframes.core.window.Window.mean
bigframes.core.window.Window.min
min()Calculate the weighted window minimum.
See more: bigframes.core.window.Window.min
bigframes.core.window.Window.std
std()Calculate the weighted window standard deviation.
See more: bigframes.core.window.Window.std
bigframes.core.window.Window.sum
sum()Calculate the weighted window sum.
See more: bigframes.core.window.Window.sum
bigframes.core.window.Window.var
var()Calculate the weighted window variance.
See more: bigframes.core.window.Window.var
bigframes.dataframe.DataFrame.__abs__
__abs__() -> bigframes.dataframe.DataFrameReturn the absolute value of the argument.
See more: bigframes.dataframe.DataFrame.abs
bigframes.dataframe.DataFrame.__add__
__add__(other) -> bigframes.dataframe.DataFrameGet addition of DataFrame and other, column-wise, using arithmetic operator +.
See more: bigframes.dataframe.DataFrame.add
bigframes.dataframe.DataFrame.__and__
__and__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise AND of DataFrame and other, element-wise, using operator &.
See more: bigframes.dataframe.DataFrame.and
bigframes.dataframe.DataFrame.__array__
__array__(dtype=None, copy: typing.Optional[bool] = None) -> numpy.ndarrayReturns the rows as NumPy array.
See more: bigframes.dataframe.DataFrame.array
bigframes.dataframe.DataFrame.__array_ufunc__
__array_ufunc__( ufunc: numpy.ufunc, method: str, *inputs, **kwargs ) -> bigframes.dataframe.DataFrameUsed to support numpy ufuncs.
bigframes.dataframe.DataFrame.__bool__
__bool__()Returns the truth value of the object.
See more: bigframes.dataframe.DataFrame.bool
bigframes.dataframe.DataFrame.__eq__
__eq__(other) -> bigframes.dataframe.DataFrameCheck equality of DataFrame and other, element-wise, using logical operator ==.
See more: bigframes.dataframe.DataFrame.eq
bigframes.dataframe.DataFrame.__floordiv__
__floordiv__(other)Get integer division of DataFrame by other, using arithmetic operator //.
See more: bigframes.dataframe.DataFrame.floordiv
bigframes.dataframe.DataFrame.__ge__
__ge__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is greater than or equal to other, element-wise, using logical operator >=.
See more: bigframes.dataframe.DataFrame.ge
bigframes.dataframe.DataFrame.__getitem__
Gets the specified column(s) from the DataFrame.
See more: bigframes.dataframe.DataFrame.getitem
bigframes.dataframe.DataFrame.__gt__
__gt__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is greater than other, element-wise, using logical operator >.
See more: bigframes.dataframe.DataFrame.gt
bigframes.dataframe.DataFrame.__invert__
__invert__() -> bigframes.dataframe.DataFrameReturns the bitwise inversion of the DataFrame, element-wise using operator ````.
See more: bigframes.dataframe.DataFrame.invert
bigframes.dataframe.DataFrame.__le__
__le__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is less than or equal to other, element-wise, using logical operator <=.
See more: bigframes.dataframe.DataFrame.le
bigframes.dataframe.DataFrame.__len__
__len__()Returns number of rows in the DataFrame, serves len operator.
See more: bigframes.dataframe.DataFrame.len
bigframes.dataframe.DataFrame.__lt__
__lt__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is less than other, element-wise, using logical operator <.
See more: bigframes.dataframe.DataFrame.lt
bigframes.dataframe.DataFrame.__matmul__
__matmul__(other) -> bigframes.dataframe.DataFrameCompute the matrix multiplication between the DataFrame and other, using operator @.
See more: bigframes.dataframe.DataFrame.matmul
bigframes.dataframe.DataFrame.__mod__
__mod__(other)Get modulo of DataFrame with other, element-wise, using operator %.
See more: bigframes.dataframe.DataFrame.mod
bigframes.dataframe.DataFrame.__mul__
__mul__(other)Get multiplication of DataFrame with other, element-wise, using operator *.
See more: bigframes.dataframe.DataFrame.mul
bigframes.dataframe.DataFrame.__ne__
__ne__(other) -> bigframes.dataframe.DataFrameCheck inequality of DataFrame and other, element-wise, using logical operator !=.
See more: bigframes.dataframe.DataFrame.ne
bigframes.dataframe.DataFrame.__nonzero__
__nonzero__()Returns the truth value of the object.
See more: bigframes.dataframe.DataFrame.nonzero
bigframes.dataframe.DataFrame.__or__
__or__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise OR of DataFrame and other, element-wise, using operator |.
See more: bigframes.dataframe.DataFrame.or
bigframes.dataframe.DataFrame.__pow__
__pow__(other)Get exponentiation of DataFrame with other, element-wise, using operator **.
See more: bigframes.dataframe.DataFrame.pow
bigframes.dataframe.DataFrame.__radd__
__radd__(other) -> bigframes.dataframe.DataFrameGet addition of other and DataFrame, element-wise (binary operator +).
See more: bigframes.dataframe.DataFrame.radd
bigframes.dataframe.DataFrame.__rand__
__rand__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise AND of DataFrame and other, element-wise, using operator &.
See more: bigframes.dataframe.DataFrame.rand
bigframes.dataframe.DataFrame.__repr__
__repr__() -> strConverts a DataFrame to a string.
See more: bigframes.dataframe.DataFrame.repr
bigframes.dataframe.DataFrame.__rfloordiv__
__rfloordiv__(other)Get integer divison of other by DataFrame.
See more: bigframes.dataframe.DataFrame.rfloordiv
bigframes.dataframe.DataFrame.__rmod__
__rmod__(other)Get integer divison of other by DataFrame.
See more: bigframes.dataframe.DataFrame.rmod
bigframes.dataframe.DataFrame.__rmul__
__rmul__(other)Get multiplication of DataFrame with other, element-wise, using operator *.
See more: bigframes.dataframe.DataFrame.rmul
bigframes.dataframe.DataFrame.__ror__
__ror__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise OR of DataFrame and other, element-wise, using operator |.
See more: bigframes.dataframe.DataFrame.ror
bigframes.dataframe.DataFrame.__rpow__
__rpow__(other)Get exponentiation of other with DataFrame, element-wise, using operator **.
See more: bigframes.dataframe.DataFrame.rpow
bigframes.dataframe.DataFrame.__rsub__
__rsub__(other)Get subtraction of DataFrame from other, element-wise, using operator -.
See more: bigframes.dataframe.DataFrame.rsub
bigframes.dataframe.DataFrame.__rtruediv__
__rtruediv__(other)Get division of other by DataFrame, element-wise, using operator /.
See more: bigframes.dataframe.DataFrame.rtruediv
bigframes.dataframe.DataFrame.__rxor__
__rxor__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise XOR of DataFrame and other, element-wise, using operator ^.
See more: bigframes.dataframe.DataFrame.rxor
bigframes.dataframe.DataFrame.__setitem__
__setitem__( key: str | list[str] | pandas.Index, value: SingleItemValue | MultiItemValue )Modify or insert a column into the DataFrame.
See more: bigframes.dataframe.DataFrame.setitem
bigframes.dataframe.DataFrame.__sub__
__sub__(other)Get subtraction of other from DataFrame, element-wise, using operator -.
See more: bigframes.dataframe.DataFrame.sub
bigframes.dataframe.DataFrame.__truediv__
__truediv__(other)Get division of DataFrame by other, element-wise, using operator /.
See more: bigframes.dataframe.DataFrame.truediv
bigframes.dataframe.DataFrame.__xor__
__xor__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise XOR of DataFrame and other, element-wise, using operator ^.
See more: bigframes.dataframe.DataFrame.xor
bigframes.dataframe.DataFrame.abs
abs() -> bigframes.dataframe.DataFrameReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.dataframe.DataFrame.abs
bigframes.dataframe.DataFrame.add
add( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet addition of DataFrame and other, element-wise (binary operator +).
See more: bigframes.dataframe.DataFrame.add
bigframes.dataframe.DataFrame.add_prefix
add_prefix( prefix: str, axis: int | str | None = None ) -> bigframes.dataframe.DataFramePrefix labels with string prefix.
See more: bigframes.dataframe.DataFrame.add_prefix
bigframes.dataframe.DataFrame.add_suffix
add_suffix( suffix: str, axis: int | str | None = None ) -> bigframes.dataframe.DataFrameSuffix labels with string suffix.
See more: bigframes.dataframe.DataFrame.add_suffix
bigframes.dataframe.DataFrame.agg
agg(func) -> bigframes.dataframe.DataFrame | bigframes.series.SeriesAggregate using one or more operations over columns.
See more: bigframes.dataframe.DataFrame.agg
bigframes.dataframe.DataFrame.aggregate
aggregate(func) -> bigframes.dataframe.DataFrame | bigframes.series.SeriesAggregate using one or more operations over columns.
See more: bigframes.dataframe.DataFrame.aggregate
bigframes.dataframe.DataFrame.align
align( other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], join: str = "outer", axis: typing.Optional[typing.Union[str, int]] = None, ) -> typing.Tuple[ typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ]Align two objects on their axes with the specified join method.
See more: bigframes.dataframe.DataFrame.align
bigframes.dataframe.DataFrame.all
all( axis: typing.Union[str, int] = 0, *, bool_only: bool = False ) -> bigframes.series.SeriesReturn whether all elements are True, potentially over an axis.
See more: bigframes.dataframe.DataFrame.all
bigframes.dataframe.DataFrame.any
any( *, axis: typing.Union[str, int] = 0, bool_only: bool = False ) -> bigframes.series.SeriesReturn whether any element is True, potentially over an axis.
See more: bigframes.dataframe.DataFrame.any
bigframes.dataframe.DataFrame.apply
apply(func, *, axis=0, args: typing.Tuple = (), **kwargs)Apply a function along an axis of the DataFrame.
See more: bigframes.dataframe.DataFrame.apply
bigframes.dataframe.DataFrame.applymap
applymap( func, na_action: typing.Optional[str] = None ) -> bigframes.dataframe.DataFrameApply a function to a Dataframe elementwise.
See more: bigframes.dataframe.DataFrame.applymap
bigframes.dataframe.DataFrame.area
area( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, stacked: bool = True, **kwargs )Draw a stacked area plot.
See more: bigframes.dataframe.DataFrame.area
bigframes.dataframe.DataFrame.assign
assign(**kwargs) -> bigframes.dataframe.DataFrameAssign new columns to a DataFrame.
See more: bigframes.dataframe.DataFrame.assign
bigframes.dataframe.DataFrame.astype
astype( dtype: typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, type, dict[ str, typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, ], ], ], *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.dataframe.DataFrameCast a pandas object to a specified dtype dtype.
See more: bigframes.dataframe.DataFrame.astype
bigframes.dataframe.DataFrame.bar
bar( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a vertical bar plot.
See more: bigframes.dataframe.DataFrame.bar
bigframes.dataframe.DataFrame.bfill
bfill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrameFill NA/NaN values by using the next valid observation to fill the gap.
See more: bigframes.dataframe.DataFrame.bfill
bigframes.dataframe.DataFrame.cache
cache()Materializes the DataFrame to a temporary table.
See more: bigframes.dataframe.DataFrame.cache
bigframes.dataframe.DataFrame.combine
combine( other: bigframes.dataframe.DataFrame, func: typing.Callable[ [bigframes.series.Series, bigframes.series.Series], bigframes.series.Series ], fill_value=None, overwrite: bool = True, *, how: str = "outer" ) -> bigframes.dataframe.DataFramePerform column-wise combine with another DataFrame.
See more: bigframes.dataframe.DataFrame.combine
bigframes.dataframe.DataFrame.combine_first
combine_first(other: bigframes.dataframe.DataFrame)Update null elements with value in the same location in other.
bigframes.dataframe.DataFrame.copy
copy() -> bigframes.dataframe.DataFrameMake a copy of this object's indices and data.
See more: bigframes.dataframe.DataFrame.copy
bigframes.dataframe.DataFrame.corr
corr( method="pearson", min_periods=None, numeric_only=False ) -> bigframes.dataframe.DataFrameCompute pairwise correlation of columns, excluding NA/null values.
See more: bigframes.dataframe.DataFrame.corr
bigframes.dataframe.DataFrame.corrwith
corrwith( other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, numeric_only: bool = False )Compute pairwise correlation.
See more: bigframes.dataframe.DataFrame.corrwith
bigframes.dataframe.DataFrame.count
count(*, numeric_only: bool = False) -> bigframes.series.SeriesCount non-NA cells for each column.
See more: bigframes.dataframe.DataFrame.count
bigframes.dataframe.DataFrame.cov
cov(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameCompute pairwise covariance of columns, excluding NA/null values.
See more: bigframes.dataframe.DataFrame.cov
bigframes.dataframe.DataFrame.cummax
cummax() -> bigframes.dataframe.DataFrameReturn cumulative maximum over columns.
See more: bigframes.dataframe.DataFrame.cummax
bigframes.dataframe.DataFrame.cummin
cummin() -> bigframes.dataframe.DataFrameReturn cumulative minimum over columns.
See more: bigframes.dataframe.DataFrame.cummin
bigframes.dataframe.DataFrame.cumprod
cumprod() -> bigframes.dataframe.DataFrameReturn cumulative product over columns.
See more: bigframes.dataframe.DataFrame.cumprod
bigframes.dataframe.DataFrame.cumsum
cumsum()Return cumulative sum over columns.
See more: bigframes.dataframe.DataFrame.cumsum
bigframes.dataframe.DataFrame.describe
describe( include: typing.Union[None, typing.Literal["all"]] = None, ) -> bigframes.dataframe.DataFrameGenerate descriptive statistics.
See more: bigframes.dataframe.DataFrame.describe
bigframes.dataframe.DataFrame.diff
diff(periods: int = 1) -> bigframes.dataframe.DataFrameFirst discrete difference of element.
See more: bigframes.dataframe.DataFrame.diff
bigframes.dataframe.DataFrame.div
div( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.dataframe.DataFrame.div
bigframes.dataframe.DataFrame.divide
divide( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.dataframe.DataFrame.divide
bigframes.dataframe.DataFrame.dot
dot(other: _DataFrameOrSeries) -> _DataFrameOrSeriesCompute the matrix multiplication between the DataFrame and other.
See more: bigframes.dataframe.DataFrame.dot
bigframes.dataframe.DataFrame.drop
Drop specified labels from columns.
See more: bigframes.dataframe.DataFrame.drop
bigframes.dataframe.DataFrame.drop_duplicates
drop_duplicates( subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, *, keep: str = "first" ) -> bigframes.dataframe.DataFrameReturn DataFrame with duplicate rows removed.
bigframes.dataframe.DataFrame.droplevel
droplevel( level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], axis: int | str = 0, )Return DataFrame with requested index / column level(s) removed.
See more: bigframes.dataframe.DataFrame.droplevel
bigframes.dataframe.DataFrame.dropna
dropna( *, axis: int | str = 0, how: str = "any", thresh: typing.Optional[int] = None, subset: typing.Union[ None, typing.Hashable, typing.Sequence[typing.Hashable] ] = None, inplace: bool = False, ignore_index=False ) -> bigframes.dataframe.DataFrameRemove missing values.
See more: bigframes.dataframe.DataFrame.dropna
bigframes.dataframe.DataFrame.duplicated
duplicated(subset=None, keep: str = "first") -> bigframes.series.SeriesReturn boolean Series denoting duplicate rows.
See more: bigframes.dataframe.DataFrame.duplicated
bigframes.dataframe.DataFrame.eq
eq(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet equal to of DataFrame and other, element-wise (binary operator eq).
See more: bigframes.dataframe.DataFrame.eq
bigframes.dataframe.DataFrame.equals
equals( other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> boolTest whether two objects contain the same elements.
See more: bigframes.dataframe.DataFrame.equals
bigframes.dataframe.DataFrame.eval
eval(expr: str) -> bigframes.dataframe.DataFrameEvaluate a string describing operations on DataFrame columns.
See more: bigframes.dataframe.DataFrame.eval
bigframes.dataframe.DataFrame.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvide expanding window calculations.
See more: bigframes.dataframe.DataFrame.expanding
bigframes.dataframe.DataFrame.explode
explode( column: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], *, ignore_index: typing.Optional[bool] = False ) -> bigframes.dataframe.DataFrameTransform each element of an array to a row, replicating index values.
See more: bigframes.dataframe.DataFrame.explode
bigframes.dataframe.DataFrame.ffill
ffill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrameFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.dataframe.DataFrame.ffill
bigframes.dataframe.DataFrame.fillna
fillna(value=None) -> bigframes.dataframe.DataFrameFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.dataframe.DataFrame.fillna
bigframes.dataframe.DataFrame.filter
filter( items: typing.Optional[typing.Iterable] = None, like: typing.Optional[str] = None, regex: typing.Optional[str] = None, axis: int | str | None = None, ) -> bigframes.dataframe.DataFrameSubset the dataframe rows or columns according to the specified index labels.
See more: bigframes.dataframe.DataFrame.filter
bigframes.dataframe.DataFrame.first_valid_index
first_valid_index()API documentation for first_valid_index method.
bigframes.dataframe.DataFrame.floordiv
floordiv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet integer division of DataFrame and other, element-wise (binary operator //).
See more: bigframes.dataframe.DataFrame.floordiv
bigframes.dataframe.DataFrame.from_dict
from_dict( data: dict, orient: str = "columns", dtype=None, columns=None ) -> bigframes.dataframe.DataFrameConstruct DataFrame from dict of array-like or dicts.
See more: bigframes.dataframe.DataFrame.from_dict
bigframes.dataframe.DataFrame.from_records
from_records( data, index=None, exclude=None, columns=None, coerce_float: bool = False, nrows: typing.Optional[int] = None, ) -> bigframes.dataframe.DataFrameConvert structured or record ndarray to DataFrame.
bigframes.dataframe.DataFrame.ge
ge(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'greater than or equal to' of DataFrame and other, element-wise (binary operator >=).
See more: bigframes.dataframe.DataFrame.ge
bigframes.dataframe.DataFrame.get
get(key, default=None)Get item from object for given key (ex: DataFrame column).
See more: bigframes.dataframe.DataFrame.get
bigframes.dataframe.DataFrame.groupby
groupby( by: typing.Optional[ typing.Union[ typing.Hashable, bigframes.series.Series, typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]], ] ] = None, *, level: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, as_index: bool = True, dropna: bool = True ) -> bigframes.core.groupby.dataframe_group_by.DataFrameGroupByGroup DataFrame by columns.
See more: bigframes.dataframe.DataFrame.groupby
bigframes.dataframe.DataFrame.gt
gt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'greater than' of DataFrame and other, element-wise (binary operator >).
See more: bigframes.dataframe.DataFrame.gt
bigframes.dataframe.DataFrame.head
head(n: int = 5) -> bigframes.dataframe.DataFrameReturn the first n rows.
See more: bigframes.dataframe.DataFrame.head
bigframes.dataframe.DataFrame.hist
hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)Draw one histogram of the DataFrame’s columns.
See more: bigframes.dataframe.DataFrame.hist
bigframes.dataframe.DataFrame.idxmax
idxmax() -> bigframes.series.SeriesReturn index of first occurrence of maximum over columns.
See more: bigframes.dataframe.DataFrame.idxmax
bigframes.dataframe.DataFrame.idxmin
idxmin() -> bigframes.series.SeriesReturn index of first occurrence of minimum over columns.
See more: bigframes.dataframe.DataFrame.idxmin
bigframes.dataframe.DataFrame.info
info( verbose: typing.Optional[bool] = None, buf=None, max_cols: typing.Optional[int] = None, memory_usage: typing.Optional[bool] = None, show_counts: typing.Optional[bool] = None, )Print a concise summary of a DataFrame.
See more: bigframes.dataframe.DataFrame.info
bigframes.dataframe.DataFrame.insert
insert( loc: int, column: blocks.Label, value: SingleItemValue, allow_duplicates: bool = False, )Insert column into DataFrame at specified location.
See more: bigframes.dataframe.DataFrame.insert
bigframes.dataframe.DataFrame.interpolate
interpolate(method: str = "linear") -> bigframes.dataframe.DataFrameFill NA (NULL in BigQuery) values using an interpolation method.
bigframes.dataframe.DataFrame.isin
isin(values) -> bigframes.dataframe.DataFrameWhether each element in the DataFrame is contained in values.
See more: bigframes.dataframe.DataFrame.isin
bigframes.dataframe.DataFrame.isna
isna() -> bigframes.dataframe.DataFrameDetect missing (NULL) values.
See more: bigframes.dataframe.DataFrame.isna
bigframes.dataframe.DataFrame.isnull
isnull() -> bigframes.dataframe.DataFrameDetect missing (NULL) values.
See more: bigframes.dataframe.DataFrame.isnull
bigframes.dataframe.DataFrame.items
items()Iterate over (column name, Series) pairs.
See more: bigframes.dataframe.DataFrame.items
bigframes.dataframe.DataFrame.iterrows
iterrows() -> typing.Iterable[tuple[typing.Any, pandas.core.series.Series]]Iterate over DataFrame rows as (index, Series) pairs.
See more: bigframes.dataframe.DataFrame.iterrows
bigframes.dataframe.DataFrame.itertuples
itertuples( index: bool = True, name: typing.Optional[str] = "Pandas" ) -> typing.Iterable[tuple[typing.Any, ...]]Iterate over DataFrame rows as namedtuples.
See more: bigframes.dataframe.DataFrame.itertuples
bigframes.dataframe.DataFrame.join
join( other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], on: typing.Optional[str] = None, how: str = "left", lsuffix: str = "", rsuffix: str = "", ) -> bigframes.dataframe.DataFrameJoin columns of another DataFrame.
See more: bigframes.dataframe.DataFrame.join
bigframes.dataframe.DataFrame.keys
keys() -> pandas.core.indexes.base.IndexGet the 'info axis'.
See more: bigframes.dataframe.DataFrame.keys
bigframes.dataframe.DataFrame.kurt
kurt(*, numeric_only: bool = False)Return unbiased kurtosis over columns.
See more: bigframes.dataframe.DataFrame.kurt
bigframes.dataframe.DataFrame.kurtosis
kurtosis(*, numeric_only: bool = False)Return unbiased kurtosis over columns.
See more: bigframes.dataframe.DataFrame.kurtosis
bigframes.dataframe.DataFrame.le
le(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'less than or equal to' of dataframe and other, element-wise (binary operator <=).
See more: bigframes.dataframe.DataFrame.le
bigframes.dataframe.DataFrame.line
line( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Plot Series or DataFrame as lines.
See more: bigframes.dataframe.DataFrame.line
bigframes.dataframe.DataFrame.lt
lt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'less than' of DataFrame and other, element-wise (binary operator <).
See more: bigframes.dataframe.DataFrame.lt
bigframes.dataframe.DataFrame.map
map(func, na_action: typing.Optional[str] = None) -> bigframes.dataframe.DataFrameApply a function to a Dataframe elementwise.
See more: bigframes.dataframe.DataFrame.map
bigframes.dataframe.DataFrame.mask
mask(cond, other=None)Replace values where the condition is False.
See more: bigframes.dataframe.DataFrame.mask
bigframes.dataframe.DataFrame.max
max( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the maximum of the values over the requested axis.
See more: bigframes.dataframe.DataFrame.max
bigframes.dataframe.DataFrame.mean
mean( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the mean of the values over the requested axis.
See more: bigframes.dataframe.DataFrame.mean
bigframes.dataframe.DataFrame.median
median( *, numeric_only: bool = False, exact: bool = True ) -> bigframes.series.SeriesReturn the median of the values over colunms.
See more: bigframes.dataframe.DataFrame.median
bigframes.dataframe.DataFrame.melt
melt( id_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None, value_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None, var_name: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, value_name: typing.Hashable = "value", )Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
See more: bigframes.dataframe.DataFrame.melt
bigframes.dataframe.DataFrame.memory_usage
memory_usage(index: bool = True)Return the memory usage of each column in bytes.
bigframes.dataframe.DataFrame.merge
merge( right: bigframes.dataframe.DataFrame, how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner", on: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, *, left_on: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, right_on: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, left_index: bool = False, right_index: bool = False, sort: bool = False, suffixes: tuple[str, str] = ("_x", "_y") ) -> bigframes.dataframe.DataFrameMerge DataFrame objects with a database-style join.
See more: bigframes.dataframe.DataFrame.merge
bigframes.dataframe.DataFrame.min
min( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the minimum of the values over the requested axis.
See more: bigframes.dataframe.DataFrame.min
bigframes.dataframe.DataFrame.mod
mod( other: int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet modulo of DataFrame and other, element-wise (binary operator %).
See more: bigframes.dataframe.DataFrame.mod
bigframes.dataframe.DataFrame.mul
mul( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet multiplication of DataFrame and other, element-wise (binary operator *).
See more: bigframes.dataframe.DataFrame.mul
bigframes.dataframe.DataFrame.multiply
multiply( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet multiplication of DataFrame and other, element-wise (binary operator *).
See more: bigframes.dataframe.DataFrame.multiply
bigframes.dataframe.DataFrame.ne
ne(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet not equal to of DataFrame and other, element-wise (binary operator ne).
See more: bigframes.dataframe.DataFrame.ne
bigframes.dataframe.DataFrame.nlargest
nlargest( n: int, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], keep: str = "first", ) -> bigframes.dataframe.DataFrameReturn the first n rows ordered by columns in descending order.
See more: bigframes.dataframe.DataFrame.nlargest
bigframes.dataframe.DataFrame.notna
notna() -> bigframes.dataframe.DataFrameDetect existing (non-missing) values.
See more: bigframes.dataframe.DataFrame.notna
bigframes.dataframe.DataFrame.notnull
notnull() -> bigframes.dataframe.DataFrameDetect existing (non-missing) values.
See more: bigframes.dataframe.DataFrame.notnull
bigframes.dataframe.DataFrame.nsmallest
nsmallest( n: int, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], keep: str = "first", ) -> bigframes.dataframe.DataFrameReturn the first n rows ordered by columns in ascending order.
See more: bigframes.dataframe.DataFrame.nsmallest
bigframes.dataframe.DataFrame.nunique
nunique() -> bigframes.series.SeriesCount number of distinct elements in each column.
See more: bigframes.dataframe.DataFrame.nunique
bigframes.dataframe.DataFrame.pct_change
pct_change(periods: int = 1) -> bigframes.dataframe.DataFrameFractional change between the current and a prior element.
See more: bigframes.dataframe.DataFrame.pct_change
bigframes.dataframe.DataFrame.peek
peek( n: int = 5, *, force: bool = True, allow_large_results=None ) -> pandas.core.frame.DataFramePreview n arbitrary rows from the dataframe.
See more: bigframes.dataframe.DataFrame.peek
bigframes.dataframe.DataFrame.pipe
pipe(func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs) -> TApply chainable functions that expect Series or DataFrames.
See more: bigframes.dataframe.DataFrame.pipe
bigframes.dataframe.DataFrame.pivot
pivot( *, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], index: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, values: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None ) -> bigframes.dataframe.DataFrameReturn reshaped DataFrame organized by given index / column values.
See more: bigframes.dataframe.DataFrame.pivot
bigframes.dataframe.DataFrame.pivot_table
pivot_table( values: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, index: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, aggfunc: str = "mean", ) -> bigframes.dataframe.DataFrameCreate a spreadsheet-style pivot table as a DataFrame.
bigframes.dataframe.DataFrame.pow
pow( other: int | bigframes.series.Series, axis: str | int = "columns" ) -> bigframes.dataframe.DataFrameGet Exponential power of dataframe and other, element-wise (binary operator **).
See more: bigframes.dataframe.DataFrame.pow
bigframes.dataframe.DataFrame.prod
prod( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the product of the values over the requested axis.
See more: bigframes.dataframe.DataFrame.prod
bigframes.dataframe.DataFrame.product
product( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the product of the values over the requested axis.
See more: bigframes.dataframe.DataFrame.product
bigframes.dataframe.DataFrame.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False )Return values at the given quantile over requested axis.
See more: bigframes.dataframe.DataFrame.quantile
bigframes.dataframe.DataFrame.query
query(expr: str) -> bigframes.dataframe.DataFrameQuery the columns of a DataFrame with a boolean expression.
See more: bigframes.dataframe.DataFrame.query
bigframes.dataframe.DataFrame.radd
radd( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet addition of DataFrame and other, element-wise (binary operator +).
See more: bigframes.dataframe.DataFrame.radd
bigframes.dataframe.DataFrame.rank
rank( axis=0, method: str = "average", numeric_only=False, na_option: str = "keep", ascending=True, pct: bool = False, ) -> bigframes.dataframe.DataFrameCompute numerical data ranks (1 through n) along axis.
See more: bigframes.dataframe.DataFrame.rank
bigframes.dataframe.DataFrame.rdiv
rdiv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.dataframe.DataFrame.rdiv
bigframes.dataframe.DataFrame.reindex
reindex( labels=None, *, index=None, columns=None, axis: typing.Optional[typing.Union[str, int]] = None, validate: typing.Optional[bool] = None )Conform DataFrame to new index with optional filling logic.
See more: bigframes.dataframe.DataFrame.reindex
bigframes.dataframe.DataFrame.reindex_like
reindex_like( other: bigframes.dataframe.DataFrame, *, validate: typing.Optional[bool] = None )Return an object with matching indices as other object.
bigframes.dataframe.DataFrame.rename
Rename columns.
See more: bigframes.dataframe.DataFrame.rename
bigframes.dataframe.DataFrame.rename_axis
Set the name of the axis for the index.
bigframes.dataframe.DataFrame.reorder_levels
reorder_levels( order: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], axis: int | str = 0, )Rearrange index levels using input order.
bigframes.dataframe.DataFrame.replace
replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)Replace values given in to_replace with value.
See more: bigframes.dataframe.DataFrame.replace
bigframes.dataframe.DataFrame.resample
resample( rule: str, *, closed: typing.Optional[typing.Literal["right", "left"]] = None, label: typing.Optional[typing.Literal["right", "left"]] = None, on: typing.Hashable = None, level: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, origin: typing.Union[ pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, numpy.datetime64, int, float, str, typing.Literal["epoch", "start", "start_day", "end", "end_day"], ] = "start_day" ) -> bigframes.core.groupby.dataframe_group_by.DataFrameGroupByResample time-series data.
See more: bigframes.dataframe.DataFrame.resample
bigframes.dataframe.DataFrame.reset_index
Reset the index.
bigframes.dataframe.DataFrame.rfloordiv
rfloordiv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet integer division of DataFrame and other, element-wise (binary operator //).
See more: bigframes.dataframe.DataFrame.rfloordiv
bigframes.dataframe.DataFrame.rmod
rmod( other: int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet modulo of DataFrame and other, element-wise (binary operator %).
See more: bigframes.dataframe.DataFrame.rmod
bigframes.dataframe.DataFrame.rmul
rmul( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet multiplication of DataFrame and other, element-wise (binary operator *).
See more: bigframes.dataframe.DataFrame.rmul
bigframes.dataframe.DataFrame.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods=None, on: str | None = None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowProvide rolling window calculations.
See more: bigframes.dataframe.DataFrame.rolling
bigframes.dataframe.DataFrame.round
round( decimals: typing.Union[int, dict[typing.Hashable, int]] = 0, ) -> bigframes.dataframe.DataFrameRound a DataFrame to a variable number of decimal places.
See more: bigframes.dataframe.DataFrame.round
bigframes.dataframe.DataFrame.rpow
rpow( other: int | bigframes.series.Series, axis: str | int = "columns" ) -> bigframes.dataframe.DataFrameGet Exponential power of dataframe and other, element-wise (binary operator rpow).
See more: bigframes.dataframe.DataFrame.rpow
bigframes.dataframe.DataFrame.rsub
rsub( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet subtraction of DataFrame and other, element-wise (binary operator -).
See more: bigframes.dataframe.DataFrame.rsub
bigframes.dataframe.DataFrame.rtruediv
rtruediv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.dataframe.DataFrame.rtruediv
bigframes.dataframe.DataFrame.sample
sample( n: typing.Optional[int] = None, frac: typing.Optional[float] = None, *, random_state: typing.Optional[int] = None, sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random" ) -> bigframes.dataframe.DataFrameReturn a random sample of items from an axis of object.
See more: bigframes.dataframe.DataFrame.sample
bigframes.dataframe.DataFrame.scatter
scatter( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, **kwargs )Create a scatter plot with varying marker point size and color.
See more: bigframes.dataframe.DataFrame.scatter
bigframes.dataframe.DataFrame.select_dtypes
select_dtypes(include=None, exclude=None) -> bigframes.dataframe.DataFrameReturn a subset of the DataFrame's columns based on the column dtypes.
bigframes.dataframe.DataFrame.set_index
set_index( keys: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], append: bool = False, drop: bool = True, ) -> bigframes.dataframe.DataFrameSet the DataFrame index using existing columns.
See more: bigframes.dataframe.DataFrame.set_index
bigframes.dataframe.DataFrame.shift
shift(periods: int = 1) -> bigframes.dataframe.DataFrameShift index by desired number of periods.
See more: bigframes.dataframe.DataFrame.shift
bigframes.dataframe.DataFrame.skew
skew(*, numeric_only: bool = False)Return unbiased skew over columns.
See more: bigframes.dataframe.DataFrame.skew
bigframes.dataframe.DataFrame.sort_index
Sort object by labels (along an axis).
See more: bigframes.dataframe.DataFrame.sort_index
bigframes.dataframe.DataFrame.sort_values
Sort by the values along row axis.
bigframes.dataframe.DataFrame.stack
stack(level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1)Stack the prescribed level(s) from columns to index.
See more: bigframes.dataframe.DataFrame.stack
bigframes.dataframe.DataFrame.std
std( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn sample standard deviation over columns.
See more: bigframes.dataframe.DataFrame.std
bigframes.dataframe.DataFrame.sub
sub( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet subtraction of DataFrame and other, element-wise (binary operator -).
See more: bigframes.dataframe.DataFrame.sub
bigframes.dataframe.DataFrame.subtract
subtract( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet subtraction of DataFrame and other, element-wise (binary operator -).
See more: bigframes.dataframe.DataFrame.subtract
bigframes.dataframe.DataFrame.sum
sum( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the sum of the values over the requested axis.
See more: bigframes.dataframe.DataFrame.sum
bigframes.dataframe.DataFrame.swaplevel
swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)Swap levels i and j in a MultiIndex.
See more: bigframes.dataframe.DataFrame.swaplevel
bigframes.dataframe.DataFrame.tail
tail(n: int = 5) -> bigframes.dataframe.DataFrameReturn the last n rows.
See more: bigframes.dataframe.DataFrame.tail
bigframes.dataframe.DataFrame.take
take( indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs ) -> bigframes.dataframe.DataFrameReturn the elements in the given positional indices along an axis.
See more: bigframes.dataframe.DataFrame.take
bigframes.dataframe.DataFrame.to_arrow
to_arrow( *, ordered: bool = True, allow_large_results: typing.Optional[bool] = None ) -> pyarrow.lib.TableWrite DataFrame to an Arrow table / record batch.
See more: bigframes.dataframe.DataFrame.to_arrow
bigframes.dataframe.DataFrame.to_csv
to_csv( path_or_buf=None, sep=",", *, header: bool = True, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Write object to a comma-separated values (csv) file on Cloud Storage.
See more: bigframes.dataframe.DataFrame.to_csv
bigframes.dataframe.DataFrame.to_dict
to_dict( orient: typing.Literal[ "dict", "list", "series", "split", "tight", "records", "index" ] = "dict", into: type[dict] = dict, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> dict | list[dict]Convert the DataFrame to a dictionary.
See more: bigframes.dataframe.DataFrame.to_dict
bigframes.dataframe.DataFrame.to_excel
to_excel( excel_writer, sheet_name: str = "Sheet1", *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> NoneWrite DataFrame to an Excel sheet.
See more: bigframes.dataframe.DataFrame.to_excel
bigframes.dataframe.DataFrame.to_gbq
to_gbq( destination_table: typing.Optional[str] = None, *, if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = None, index: bool = True, ordering_id: typing.Optional[str] = None, clustering_columns: typing.Union[ pandas.core.indexes.base.Index, typing.Iterable[typing.Hashable] ] = (), labels: dict[str, str] = {} ) -> strWrite a DataFrame to a BigQuery table.
See more: bigframes.dataframe.DataFrame.to_gbq
bigframes.dataframe.DataFrame.to_html
to_html( buf=None, columns: typing.Optional[typing.Sequence[str]] = None, col_space=None, header: bool = True, index: bool = True, na_rep: str = "NaN", formatters=None, float_format=None, sparsify: bool | None = None, index_names: bool = True, justify: str | None = None, max_rows: int | None = None, max_cols: int | None = None, show_dimensions: bool = False, decimal: str = ".", bold_rows: bool = True, classes: str | list | tuple | None = None, escape: bool = True, notebook: bool = False, border: int | None = None, table_id: str | None = None, render_links: bool = False, encoding: str | None = None, *, allow_large_results: bool | None = None ) -> strRender a DataFrame as an HTML table.
See more: bigframes.dataframe.DataFrame.to_html
bigframes.dataframe.DataFrame.to_json
to_json( path_or_buf=None, orient: typing.Optional[ typing.Literal["split", "records", "index", "columns", "values", "table"] ] = None, *, lines: bool = False, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Convert the object to a JSON string, written to Cloud Storage.
See more: bigframes.dataframe.DataFrame.to_json
bigframes.dataframe.DataFrame.to_latex
to_latex( buf=None, columns: typing.Optional[typing.Sequence] = None, header: typing.Union[bool, typing.Sequence[str]] = True, index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> str | NoneRender object to a LaTeX tabular, longtable, or nested table.
See more: bigframes.dataframe.DataFrame.to_latex
bigframes.dataframe.DataFrame.to_markdown
to_markdown( buf=None, mode: str = "wt", index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> str | NonePrint DataFrame in Markdown-friendly format.
bigframes.dataframe.DataFrame.to_numpy
to_numpy( dtype=None, copy=False, na_value=_NoDefault.no_default, *, allow_large_results=None, **kwargs ) -> numpy.ndarrayConvert the DataFrame to a NumPy array.
See more: bigframes.dataframe.DataFrame.to_numpy
bigframes.dataframe.DataFrame.to_orc
to_orc(path=None, *, allow_large_results=None, **kwargs) -> bytes | NoneWrite a DataFrame to the ORC format.
See more: bigframes.dataframe.DataFrame.to_orc
bigframes.dataframe.DataFrame.to_pandas
Write DataFrame to pandas DataFrame.
See more: bigframes.dataframe.DataFrame.to_pandas
bigframes.dataframe.DataFrame.to_pandas_batches
to_pandas_batches( page_size: typing.Optional[int] = None, max_results: typing.Optional[int] = None, *, allow_large_results: typing.Optional[bool] = None ) -> typing.Iterable[pandas.core.frame.DataFrame]Stream DataFrame results to an iterable of pandas DataFrame.
bigframes.dataframe.DataFrame.to_parquet
to_parquet( path=None, *, compression: typing.Optional[typing.Literal["snappy", "gzip"]] = "snappy", index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[bytes]Write a DataFrame to the binary Parquet format.
See more: bigframes.dataframe.DataFrame.to_parquet
bigframes.dataframe.DataFrame.to_pickle
to_pickle(path, *, allow_large_results=None, **kwargs) -> NonePickle (serialize) object to file.
See more: bigframes.dataframe.DataFrame.to_pickle
bigframes.dataframe.DataFrame.to_records
to_records( index: bool = True, column_dtypes=None, index_dtypes=None, *, allow_large_results=None ) -> numpy.rec.recarrayConvert DataFrame to a NumPy record array.
See more: bigframes.dataframe.DataFrame.to_records
bigframes.dataframe.DataFrame.to_string
to_string( buf=None, columns: typing.Optional[typing.Sequence[str]] = None, col_space=None, header: typing.Union[bool, typing.Sequence[str]] = True, index: bool = True, na_rep: str = "NaN", formatters=None, float_format=None, sparsify: bool | None = None, index_names: bool = True, justify: str | None = None, max_rows: int | None = None, max_cols: int | None = None, show_dimensions: bool = False, decimal: str = ".", line_width: int | None = None, min_rows: int | None = None, max_colwidth: int | None = None, encoding: str | None = None, *, allow_large_results: typing.Optional[bool] = None ) -> str | NoneRender a DataFrame to a console-friendly tabular output.
See more: bigframes.dataframe.DataFrame.to_string
bigframes.dataframe.DataFrame.transpose
transpose() -> bigframes.dataframe.DataFrameTranspose index and columns.
See more: bigframes.dataframe.DataFrame.transpose
bigframes.dataframe.DataFrame.truediv
truediv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.dataframe.DataFrame.truediv
bigframes.dataframe.DataFrame.unstack
unstack( level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1, )Pivot a level of the (necessarily hierarchical) index labels.
See more: bigframes.dataframe.DataFrame.unstack
bigframes.dataframe.DataFrame.update
update(other, join: str = "left", overwrite=True, filter_func=None)Modify in place using non-NA values from another DataFrame.
See more: bigframes.dataframe.DataFrame.update
bigframes.dataframe.DataFrame.value_counts
value_counts( subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True, )Return a Series containing counts of unique rows in the DataFrame.
bigframes.dataframe.DataFrame.var
var( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn unbiased variance over requested axis.
See more: bigframes.dataframe.DataFrame.var
bigframes.dataframe.DataFrame.where
where(cond, other=None)Replace values where the condition is False.
See more: bigframes.dataframe.DataFrame.where
bigframes.geopandas.GeoSeries.__abs__
__abs__() -> bigframes.series.SeriesReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.geopandas.GeoSeries.abs
bigframes.geopandas.GeoSeries.__add__
__add__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet addition of Series and other, element-wise, using operator +.
See more: bigframes.geopandas.GeoSeries.add
bigframes.geopandas.GeoSeries.__and__
__and__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise AND of Series and other, element-wise, using operator &.
See more: bigframes.geopandas.GeoSeries.and
bigframes.geopandas.GeoSeries.__array__
__array__(dtype=None, copy: typing.Optional[bool] = None) -> numpy.ndarrayReturns the values as NumPy array.
See more: bigframes.geopandas.GeoSeries.array
bigframes.geopandas.GeoSeries.__array_ufunc__
__array_ufunc__( ufunc: numpy.ufunc, method: str, *inputs, **kwargs ) -> bigframes.series.SeriesUsed to support numpy ufuncs.
bigframes.geopandas.GeoSeries.__bool__
__bool__()Returns the truth value of the object.
See more: bigframes.geopandas.GeoSeries.bool
bigframes.geopandas.GeoSeries.__floordiv__
__floordiv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet integer division of Series by other, using arithmetic operator //.
See more: bigframes.geopandas.GeoSeries.floordiv
bigframes.geopandas.GeoSeries.__getitem__
__getitem__(indexer)Gets the specified index from the Series.
See more: bigframes.geopandas.GeoSeries.getitem
bigframes.geopandas.GeoSeries.__invert__
__invert__() -> bigframes.series.SeriesReturns the logical inversion (binary NOT) of the Series, element-wise using operator ````.
See more: bigframes.geopandas.GeoSeries.invert
bigframes.geopandas.GeoSeries.__len__
__len__()Returns number of values in the Series, serves len operator.
See more: bigframes.geopandas.GeoSeries.len
bigframes.geopandas.GeoSeries.__matmul__
__matmul__(other)Matrix multiplication using binary @ operator.
See more: bigframes.geopandas.GeoSeries.matmul
bigframes.geopandas.GeoSeries.__mod__
__mod__(other) -> bigframes.series.SeriesGet modulo of Series with other, element-wise, using operator %.
See more: bigframes.geopandas.GeoSeries.mod
bigframes.geopandas.GeoSeries.__mul__
__mul__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet multiplication of Series with other, element-wise, using operator *.
See more: bigframes.geopandas.GeoSeries.mul
bigframes.geopandas.GeoSeries.__nonzero__
__nonzero__()Returns the truth value of the object.
See more: bigframes.geopandas.GeoSeries.nonzero
bigframes.geopandas.GeoSeries.__or__
__or__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise XOR of Series and other, element-wise, using operator ^.
See more: bigframes.geopandas.GeoSeries.or
bigframes.geopandas.GeoSeries.__pow__
__pow__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet exponentiation of Series with other, element-wise, using operator **.
See more: bigframes.geopandas.GeoSeries.pow
bigframes.geopandas.GeoSeries.__radd__
__radd__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet addition of Series and other, element-wise, using operator +.
See more: bigframes.geopandas.GeoSeries.radd
bigframes.geopandas.GeoSeries.__rand__
__rand__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise AND of Series and other, element-wise, using operator &.
See more: bigframes.geopandas.GeoSeries.rand
bigframes.geopandas.GeoSeries.__rfloordiv__
__rfloordiv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet integer division of other by Series, using arithmetic operator //.
See more: bigframes.geopandas.GeoSeries.rfloordiv
bigframes.geopandas.GeoSeries.__rmatmul__
__rmatmul__(other)Matrix multiplication using binary @ operator.
See more: bigframes.geopandas.GeoSeries.rmatmul
bigframes.geopandas.GeoSeries.__rmod__
__rmod__(other) -> bigframes.series.SeriesGet modulo of other with Series, element-wise, using operator %.
See more: bigframes.geopandas.GeoSeries.rmod
bigframes.geopandas.GeoSeries.__rmul__
__rmul__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet multiplication of other with Series, element-wise, using operator *.
See more: bigframes.geopandas.GeoSeries.rmul
bigframes.geopandas.GeoSeries.__ror__
__ror__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise XOR of Series and other, element-wise, using operator ^.
See more: bigframes.geopandas.GeoSeries.ror
bigframes.geopandas.GeoSeries.__rpow__
__rpow__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet exponentiation of other with Series, element-wise, using operator **.
See more: bigframes.geopandas.GeoSeries.rpow
bigframes.geopandas.GeoSeries.__rsub__
__rsub__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet subtraction of Series from other, element-wise, using operator -.
See more: bigframes.geopandas.GeoSeries.rsub
bigframes.geopandas.GeoSeries.__rtruediv__
__rtruediv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet division of other by Series, element-wise, using operator /.
See more: bigframes.geopandas.GeoSeries.rtruediv
bigframes.geopandas.GeoSeries.__setitem__
__setitem__(key, value) -> NoneSet item using direct assignment, delegating to .loc indexer.
See more: bigframes.geopandas.GeoSeries.setitem
bigframes.geopandas.GeoSeries.__sub__
__sub__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet subtraction of other from Series, element-wise, using operator -.
See more: bigframes.geopandas.GeoSeries.sub
bigframes.geopandas.GeoSeries.__truediv__
__truediv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet division of Series by other, element-wise, using operator /.
See more: bigframes.geopandas.GeoSeries.truediv
bigframes.geopandas.GeoSeries.abs
abs() -> bigframes.series.SeriesReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.geopandas.GeoSeries.abs
bigframes.geopandas.GeoSeries.add
add( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn addition of Series and other, element-wise (binary operator add).
See more: bigframes.geopandas.GeoSeries.add
bigframes.geopandas.GeoSeries.add_prefix
add_prefix(prefix: str, axis: int | str | None = None) -> bigframes.series.SeriesPrefix labels with string prefix.
See more: bigframes.geopandas.GeoSeries.add_prefix
bigframes.geopandas.GeoSeries.add_suffix
add_suffix(suffix: str, axis: int | str | None = None) -> bigframes.series.SeriesSuffix labels with string suffix.
See more: bigframes.geopandas.GeoSeries.add_suffix
bigframes.geopandas.GeoSeries.agg
agg( func: typing.Union[str, typing.Sequence[str]], ) -> typing.Union[typing.Any, bigframes.series.Series]Aggregate using one or more operations over the specified axis.
See more: bigframes.geopandas.GeoSeries.agg
bigframes.geopandas.GeoSeries.aggregate
aggregate( func: typing.Union[str, typing.Sequence[str]], ) -> typing.Union[typing.Any, bigframes.series.Series]Aggregate using one or more operations over the specified axis.
See more: bigframes.geopandas.GeoSeries.aggregate
bigframes.geopandas.GeoSeries.all
all() -> boolReturn whether all elements are True, potentially over an axis.
See more: bigframes.geopandas.GeoSeries.all
bigframes.geopandas.GeoSeries.any
any() -> boolReturn whether any element is True, potentially over an axis.
See more: bigframes.geopandas.GeoSeries.any
bigframes.geopandas.GeoSeries.apply
apply( func, by_row: typing.Union[typing.Literal["compat"], bool] = "compat", *, args: typing.Tuple = () ) -> bigframes.series.SeriesInvoke function on values of a Series.
See more: bigframes.geopandas.GeoSeries.apply
bigframes.geopandas.GeoSeries.argmax
argmax() -> intReturn int position of the largest value in the series.
See more: bigframes.geopandas.GeoSeries.argmax
bigframes.geopandas.GeoSeries.argmin
argmin() -> intReturn int position of the smallest value in the Series.
See more: bigframes.geopandas.GeoSeries.argmin
bigframes.geopandas.GeoSeries.astype
astype( dtype: typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, ], *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.series.SeriesCast a pandas object to a specified dtype dtype.
See more: bigframes.geopandas.GeoSeries.astype
bigframes.geopandas.GeoSeries.autocorr
autocorr(lag: int = 1) -> floatCompute the lag-N autocorrelation.
See more: bigframes.geopandas.GeoSeries.autocorr
bigframes.geopandas.GeoSeries.bar
bar( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a vertical bar plot.
See more: bigframes.geopandas.GeoSeries.bar
bigframes.geopandas.GeoSeries.between
between(left, right, inclusive="both")Return boolean Series equivalent to left <= series <= right.
See more: bigframes.geopandas.GeoSeries.between
bigframes.geopandas.GeoSeries.bfill
bfill(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by using the next valid observation to fill the gap.
See more: bigframes.geopandas.GeoSeries.bfill
bigframes.geopandas.GeoSeries.buffer
buffer(distance: float) -> bigframes.series.SeriesAPI documentation for buffer method.
See more: bigframes.geopandas.GeoSeries.buffer
bigframes.geopandas.GeoSeries.cache
cache()Materializes the Series to a temporary table.
See more: bigframes.geopandas.GeoSeries.cache
bigframes.geopandas.GeoSeries.case_when
case_when(caselist) -> bigframes.series.SeriesReplace values where the conditions are True.
See more: bigframes.geopandas.GeoSeries.case_when
bigframes.geopandas.GeoSeries.clip
clip(lower=None, upper=None)Trim values at input threshold(s).
See more: bigframes.geopandas.GeoSeries.clip
bigframes.geopandas.GeoSeries.combine
combine(other, func) -> bigframes.series.SeriesCombine the Series with a Series or scalar according to func.
See more: bigframes.geopandas.GeoSeries.combine
bigframes.geopandas.GeoSeries.combine_first
combine_first(other: bigframes.series.Series) -> bigframes.series.SeriesUpdate null elements with value in the same location in 'other'.
bigframes.geopandas.GeoSeries.copy
copy() -> bigframes.series.SeriesMake a copy of this object's indices and data.
See more: bigframes.geopandas.GeoSeries.copy
bigframes.geopandas.GeoSeries.corr
corr(other: bigframes.series.Series, method="pearson", min_periods=None) -> floatCompute the correlation with the other Series.
See more: bigframes.geopandas.GeoSeries.corr
bigframes.geopandas.GeoSeries.count
count() -> intReturn number of non-NA/null observations in the Series.
See more: bigframes.geopandas.GeoSeries.count
bigframes.geopandas.GeoSeries.cov
cov(other: bigframes.series.Series) -> floatCompute covariance with Series, excluding missing values.
See more: bigframes.geopandas.GeoSeries.cov
bigframes.geopandas.GeoSeries.cummax
cummax() -> bigframes.series.SeriesReturn cumulative maximum over a DataFrame or Series axis.
See more: bigframes.geopandas.GeoSeries.cummax
bigframes.geopandas.GeoSeries.cummin
cummin() -> bigframes.series.SeriesReturn cumulative minimum over a DataFrame or Series axis.
See more: bigframes.geopandas.GeoSeries.cummin
bigframes.geopandas.GeoSeries.cumprod
cumprod() -> bigframes.series.SeriesReturn cumulative product over a DataFrame or Series axis.
See more: bigframes.geopandas.GeoSeries.cumprod
bigframes.geopandas.GeoSeries.cumsum
cumsum() -> bigframes.series.SeriesReturn cumulative sum over a DataFrame or Series axis.
See more: bigframes.geopandas.GeoSeries.cumsum
bigframes.geopandas.GeoSeries.describe
describe() -> bigframes.series.SeriesGenerate descriptive statistics.
See more: bigframes.geopandas.GeoSeries.describe
bigframes.geopandas.GeoSeries.diff
diff(periods: int = 1) -> bigframes.series.SeriesFirst discrete difference of element.
See more: bigframes.geopandas.GeoSeries.diff
bigframes.geopandas.GeoSeries.difference
difference( other: bigframes.geopandas.geoseries.GeoSeries, ) -> bigframes.series.SeriesReturns a GeoSeries of the points in each aligned geometry that are not in other.
See more: bigframes.geopandas.GeoSeries.difference
bigframes.geopandas.GeoSeries.distance
distance(other: bigframes.geopandas.geoseries.GeoSeries) -> bigframes.series.Series[Not Implemented] Use <xref uid="bigframes.bigquery.st_distance">bigframes.bigquery.st_distance</xref>(series, other) instead to return the shorted distance between two GEOGRAPHY objects in meters.
See more: bigframes.geopandas.GeoSeries.distance
bigframes.geopandas.GeoSeries.div
div( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.geopandas.GeoSeries.div
bigframes.geopandas.GeoSeries.divide
divide( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.geopandas.GeoSeries.divide
bigframes.geopandas.GeoSeries.divmod
divmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]Return integer division and modulo of Series and other, element-wise (binary operator divmod).
See more: bigframes.geopandas.GeoSeries.divmod
bigframes.geopandas.GeoSeries.dot
dot(other)Compute the dot product between the Series and the columns of other.
See more: bigframes.geopandas.GeoSeries.dot
bigframes.geopandas.GeoSeries.drop
drop( labels: typing.Any = None, *, axis: typing.Union[int, str] = 0, index: typing.Any = None, columns: typing.Union[typing.Hashable, typing.Iterable[typing.Hashable]] = None, level: typing.Optional[typing.Union[str, int]] = None ) -> bigframes.series.SeriesReturn Series with specified index labels removed.
See more: bigframes.geopandas.GeoSeries.drop
bigframes.geopandas.GeoSeries.drop_duplicates
drop_duplicates(*, keep: str = "first") -> bigframes.series.SeriesReturn Series with duplicate values removed.
bigframes.geopandas.GeoSeries.droplevel
droplevel( level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]], axis: int | str = 0, )Return Series with requested index / column level(s) removed.
See more: bigframes.geopandas.GeoSeries.droplevel
bigframes.geopandas.GeoSeries.dropna
dropna( *, axis: int = 0, inplace: bool = False, how: typing.Optional[str] = None, ignore_index: bool = False ) -> bigframes.series.SeriesReturn a new Series with missing values removed.
See more: bigframes.geopandas.GeoSeries.dropna
bigframes.geopandas.GeoSeries.duplicated
duplicated(keep: str = "first") -> bigframes.series.SeriesIndicate duplicate Series values.
See more: bigframes.geopandas.GeoSeries.duplicated
bigframes.geopandas.GeoSeries.eq
eq(other: object) -> bigframes.series.SeriesReturn equal of Series and other, element-wise (binary operator eq).
See more: bigframes.geopandas.GeoSeries.eq
bigframes.geopandas.GeoSeries.equals
equals( other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> boolTest whether two objects contain the same elements.
See more: bigframes.geopandas.GeoSeries.equals
bigframes.geopandas.GeoSeries.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvide expanding window calculations.
See more: bigframes.geopandas.GeoSeries.expanding
bigframes.geopandas.GeoSeries.explode
explode(*, ignore_index: typing.Optional[bool] = False) -> bigframes.series.SeriesTransform each element of a list-like to a row.
See more: bigframes.geopandas.GeoSeries.explode
bigframes.geopandas.GeoSeries.ffill
ffill(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.geopandas.GeoSeries.ffill
bigframes.geopandas.GeoSeries.fillna
fillna(value=None) -> bigframes.series.SeriesFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.geopandas.GeoSeries.fillna
bigframes.geopandas.GeoSeries.filter
filter( items: typing.Optional[typing.Iterable] = None, like: typing.Optional[str] = None, regex: typing.Optional[str] = None, axis: typing.Optional[typing.Union[str, int]] = None, ) -> bigframes.series.SeriesSubset the dataframe rows or columns according to the specified index labels.
See more: bigframes.geopandas.GeoSeries.filter
bigframes.geopandas.GeoSeries.floordiv
floordiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn integer division of Series and other, element-wise (binary operator floordiv).
See more: bigframes.geopandas.GeoSeries.floordiv
bigframes.geopandas.GeoSeries.from_wkt
from_wkt( data, index=None, *, session: typing.Optional[bigframes.session.Session] = None ) -> bigframes.geopandas.geoseries.GeoSeriesAlternate constructor to create a GeoSeries from a list or array of WKT objects.
See more: bigframes.geopandas.GeoSeries.from_wkt
bigframes.geopandas.GeoSeries.from_xy
from_xy( x, y, index=None, session=None, **kwargs ) -> bigframes.geopandas.geoseries.GeoSeriesAlternate constructor to create a GeoSeries of Point geometries from lists or arrays of x, y coordinates.
See more: bigframes.geopandas.GeoSeries.from_xy
bigframes.geopandas.GeoSeries.ge
ge(other) -> bigframes.series.SeriesGet 'greater than or equal to' of Series and other, element-wise (binary operator ge).
See more: bigframes.geopandas.GeoSeries.ge
bigframes.geopandas.GeoSeries.get
get(key, default=None)Get item from object for given key (ex: DataFrame column).
See more: bigframes.geopandas.GeoSeries.get
bigframes.geopandas.GeoSeries.groupby
groupby( by: typing.Union[ typing.Hashable, bigframes.series.Series, typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]], ] = None, axis=0, level: typing.Optional[ typing.Union[int, str, typing.Sequence[int], typing.Sequence[str]] ] = None, as_index: bool = True, *, dropna: bool = True ) -> bigframes.core.groupby.series_group_by.SeriesGroupByGroup Series using a mapper or by a Series of columns.
See more: bigframes.geopandas.GeoSeries.groupby
bigframes.geopandas.GeoSeries.gt
gt(other) -> bigframes.series.SeriesReturn Greater than of series and other, element-wise (binary operator gt).
See more: bigframes.geopandas.GeoSeries.gt
bigframes.geopandas.GeoSeries.head
head(n: int = 5) -> bigframes.series.SeriesReturn the first n rows.
See more: bigframes.geopandas.GeoSeries.head
bigframes.geopandas.GeoSeries.hist
hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)Draw one histogram of the DataFrame’s columns.
See more: bigframes.geopandas.GeoSeries.hist
bigframes.geopandas.GeoSeries.idxmax
idxmax() -> typing.HashableReturn the row label of the maximum value.
See more: bigframes.geopandas.GeoSeries.idxmax
bigframes.geopandas.GeoSeries.idxmin
idxmin() -> typing.HashableReturn the row label of the minimum value.
See more: bigframes.geopandas.GeoSeries.idxmin
bigframes.geopandas.GeoSeries.interpolate
interpolate(method: str = "linear") -> bigframes.series.SeriesFill NaN values using an interpolation method.
bigframes.geopandas.GeoSeries.intersection
intersection( other: bigframes.geopandas.geoseries.GeoSeries, ) -> bigframes.series.SeriesReturns a GeoSeries of the intersection of points in each aligned geometry with other.
bigframes.geopandas.GeoSeries.isin
isin(values) -> bigframes.series.SeriesWhether elements in Series are contained in values.
See more: bigframes.geopandas.GeoSeries.isin
bigframes.geopandas.GeoSeries.isna
isna() -> bigframes.series.SeriesDetect missing (NULL) values.
See more: bigframes.geopandas.GeoSeries.isna
bigframes.geopandas.GeoSeries.isnull
isnull() -> bigframes.series.SeriesDetect missing (NULL) values.
See more: bigframes.geopandas.GeoSeries.isnull
bigframes.geopandas.GeoSeries.item
item()Return the first element of the underlying data as a Python scalar.
See more: bigframes.geopandas.GeoSeries.item
bigframes.geopandas.GeoSeries.items
items()Lazily iterate over (index, value) tuples.
See more: bigframes.geopandas.GeoSeries.items
bigframes.geopandas.GeoSeries.keys
keys() -> bigframes.core.indexes.base.IndexReturn alias for index.
See more: bigframes.geopandas.GeoSeries.keys
bigframes.geopandas.GeoSeries.kurt
kurt()Return unbiased kurtosis over requested axis.
See more: bigframes.geopandas.GeoSeries.kurt
bigframes.geopandas.GeoSeries.kurtosis
kurtosis()Return unbiased kurtosis over requested axis.
See more: bigframes.geopandas.GeoSeries.kurtosis
bigframes.geopandas.GeoSeries.le
le(other) -> bigframes.series.SeriesGet 'less than or equal to' of Series and other, element-wise (binary operator le).
See more: bigframes.geopandas.GeoSeries.le
bigframes.geopandas.GeoSeries.line
line( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Plot Series or DataFrame as lines.
See more: bigframes.geopandas.GeoSeries.line
bigframes.geopandas.GeoSeries.lt
lt(other) -> bigframes.series.SeriesGet 'less than' of Series and other, element-wise (binary operator lt).
See more: bigframes.geopandas.GeoSeries.lt
bigframes.geopandas.GeoSeries.map
map( arg: typing.Union[typing.Mapping, bigframes.series.Series, typing.Callable], na_action: typing.Optional[str] = None, *, verify_integrity: bool = False ) -> bigframes.series.SeriesMap values of Series according to an input mapping or function.
See more: bigframes.geopandas.GeoSeries.map
bigframes.geopandas.GeoSeries.mask
mask(cond, other=None) -> bigframes.series.SeriesReplace values where the condition is True.
See more: bigframes.geopandas.GeoSeries.mask
bigframes.geopandas.GeoSeries.max
max() -> typing.AnyReturn the maximum of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.max
bigframes.geopandas.GeoSeries.mean
mean() -> floatReturn the mean of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.mean
bigframes.geopandas.GeoSeries.median
median(*, exact: bool = True) -> floatReturn the median of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.median
bigframes.geopandas.GeoSeries.min
min() -> typing.AnyReturn the maximum of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.min
bigframes.geopandas.GeoSeries.mod
mod(other) -> bigframes.series.SeriesReturn modulo of Series and other, element-wise (binary operator mod).
See more: bigframes.geopandas.GeoSeries.mod
bigframes.geopandas.GeoSeries.mode
mode() -> bigframes.series.SeriesReturn the mode(s) of the Series.
See more: bigframes.geopandas.GeoSeries.mode
bigframes.geopandas.GeoSeries.mul
mul(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.geopandas.GeoSeries.mul
bigframes.geopandas.GeoSeries.multiply
multiply(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.geopandas.GeoSeries.multiply
bigframes.geopandas.GeoSeries.ne
ne(other: object) -> bigframes.series.SeriesReturn not equal of Series and other, element-wise (binary operator ne).
See more: bigframes.geopandas.GeoSeries.ne
bigframes.geopandas.GeoSeries.nlargest
nlargest(n: int = 5, keep: str = "first") -> bigframes.series.SeriesReturn the largest n elements.
See more: bigframes.geopandas.GeoSeries.nlargest
bigframes.geopandas.GeoSeries.notna
notna() -> bigframes.series.SeriesDetect existing (non-missing) values.
See more: bigframes.geopandas.GeoSeries.notna
bigframes.geopandas.GeoSeries.notnull
notnull() -> bigframes.series.SeriesDetect existing (non-missing) values.
See more: bigframes.geopandas.GeoSeries.notnull
bigframes.geopandas.GeoSeries.nsmallest
nsmallest(n: int = 5, keep: str = "first") -> bigframes.series.SeriesReturn the smallest n elements.
See more: bigframes.geopandas.GeoSeries.nsmallest
bigframes.geopandas.GeoSeries.nunique
nunique() -> intReturn number of unique elements in the object.
See more: bigframes.geopandas.GeoSeries.nunique
bigframes.geopandas.GeoSeries.pad
pad(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.geopandas.GeoSeries.pad
bigframes.geopandas.GeoSeries.pct_change
pct_change(periods: int = 1) -> bigframes.series.SeriesFractional change between the current and a prior element.
See more: bigframes.geopandas.GeoSeries.pct_change
bigframes.geopandas.GeoSeries.peek
peek( n: int = 5, *, force: bool = True, allow_large_results=None ) -> pandas.core.series.SeriesPreview n arbitrary elements from the series without guarantees about row selection or ordering.
See more: bigframes.geopandas.GeoSeries.peek
bigframes.geopandas.GeoSeries.pipe
pipe(func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs) -> TApply chainable functions that expect Series or DataFrames.
See more: bigframes.geopandas.GeoSeries.pipe
bigframes.geopandas.GeoSeries.pow
pow(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn Exponential power of series and other, element-wise (binary operator pow).
See more: bigframes.geopandas.GeoSeries.pow
bigframes.geopandas.GeoSeries.prod
prod() -> floatReturn the product of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.prod
bigframes.geopandas.GeoSeries.product
product() -> floatReturn the product of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.product
bigframes.geopandas.GeoSeries.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, ) -> typing.Union[bigframes.series.Series, float]Return value at the given quantile.
See more: bigframes.geopandas.GeoSeries.quantile
bigframes.geopandas.GeoSeries.radd
radd( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn addition of Series and other, element-wise (binary operator radd).
See more: bigframes.geopandas.GeoSeries.radd
bigframes.geopandas.GeoSeries.rank
rank( axis=0, method: str = "average", numeric_only=False, na_option: str = "keep", ascending: bool = True, pct: bool = False, ) -> bigframes.series.SeriesCompute numerical data ranks (1 through n) along axis.
See more: bigframes.geopandas.GeoSeries.rank
bigframes.geopandas.GeoSeries.rdiv
rdiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator rtruediv).
See more: bigframes.geopandas.GeoSeries.rdiv
bigframes.geopandas.GeoSeries.rdivmod
rdivmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]Return integer division and modulo of Series and other, element-wise (binary operator rdivmod).
See more: bigframes.geopandas.GeoSeries.rdivmod
bigframes.geopandas.GeoSeries.reindex
reindex(index=None, *, validate: typing.Optional[bool] = None)Conform Series to new index with optional filling logic.
See more: bigframes.geopandas.GeoSeries.reindex
bigframes.geopandas.GeoSeries.reindex_like
reindex_like( other: bigframes.series.Series, *, validate: typing.Optional[bool] = None )Return an object with matching indices as other object.
bigframes.geopandas.GeoSeries.rename
rename( index: typing.Union[typing.Hashable, typing.Mapping[typing.Any, typing.Any]] = None, *, inplace: bool = False, **kwargs ) -> typing.Optional[bigframes.series.Series]Alter Series index labels or name.
See more: bigframes.geopandas.GeoSeries.rename
bigframes.geopandas.GeoSeries.rename_axis
rename_axis( mapper: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], *, inplace: bool = False, **kwargs ) -> typing.Optional[bigframes.series.Series]Set the name of the axis for the index or columns.
bigframes.geopandas.GeoSeries.reorder_levels
reorder_levels( order: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]], axis: int | str = 0, )Rearrange index levels using input order.
bigframes.geopandas.GeoSeries.replace
replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)Replace values given in to_replace with value.
See more: bigframes.geopandas.GeoSeries.replace
bigframes.geopandas.GeoSeries.resample
resample( rule: str, *, closed: typing.Optional[typing.Literal["right", "left"]] = None, label: typing.Optional[typing.Literal["right", "left"]] = None, level: typing.Optional[ typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] ] = None, origin: typing.Union[ pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, numpy.datetime64, int, float, str, typing.Literal["epoch", "start", "start_day", "end", "end_day"], ] = "start_day" ) -> bigframes.core.groupby.series_group_by.SeriesGroupByResample time-series data.
See more: bigframes.geopandas.GeoSeries.resample
bigframes.geopandas.GeoSeries.reset_index
reset_index( level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, *, name: typing.Optional[str] = None, drop: bool = False, inplace: bool = False, allow_duplicates: typing.Optional[bool] = None ) -> bigframes.dataframe.DataFrame | bigframes.series.Series | NoneGenerate a new DataFrame or Series with the index reset.
bigframes.geopandas.GeoSeries.rfloordiv
rfloordiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn integer division of Series and other, element-wise (binary operator rfloordiv).
See more: bigframes.geopandas.GeoSeries.rfloordiv
bigframes.geopandas.GeoSeries.rmod
rmod(other) -> bigframes.series.SeriesReturn modulo of Series and other, element-wise (binary operator mod).
See more: bigframes.geopandas.GeoSeries.rmod
bigframes.geopandas.GeoSeries.rmul
rmul(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.geopandas.GeoSeries.rmul
bigframes.geopandas.GeoSeries.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods: int | None = None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowProvide rolling window calculations.
See more: bigframes.geopandas.GeoSeries.rolling
bigframes.geopandas.GeoSeries.round
round(decimals=0) -> bigframes.series.SeriesRound each value in a Series to the given number of decimals.
See more: bigframes.geopandas.GeoSeries.round
bigframes.geopandas.GeoSeries.rpow
rpow(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn Exponential power of series and other, element-wise (binary operator rpow).
See more: bigframes.geopandas.GeoSeries.rpow
bigframes.geopandas.GeoSeries.rsub
rsub(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator rsub).
See more: bigframes.geopandas.GeoSeries.rsub
bigframes.geopandas.GeoSeries.rtruediv
rtruediv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator rtruediv).
See more: bigframes.geopandas.GeoSeries.rtruediv
bigframes.geopandas.GeoSeries.sample
sample( n: typing.Optional[int] = None, frac: typing.Optional[float] = None, *, random_state: typing.Optional[int] = None, sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random" ) -> bigframes.series.SeriesReturn a random sample of items from an axis of object.
See more: bigframes.geopandas.GeoSeries.sample
bigframes.geopandas.GeoSeries.shift
shift(periods: int = 1) -> bigframes.series.SeriesShift index by desired number of periods.
See more: bigframes.geopandas.GeoSeries.shift
bigframes.geopandas.GeoSeries.simplify
simplify(tolerance, preserve_topology=True)[Not Implemented] Use <xref uid="bigframes.bigquery.st_simplify">bigframes.bigquery.st_simplify</xref>(series, tolerance_meters), instead to set the tolerance in meters.
See more: bigframes.geopandas.GeoSeries.simplify
bigframes.geopandas.GeoSeries.skew
skew()Return unbiased skew over requested axis.
See more: bigframes.geopandas.GeoSeries.skew
bigframes.geopandas.GeoSeries.sort_index
sort_index( *, axis=0, inplace: bool = False, ascending=True, na_position="last" ) -> typing.Optional[bigframes.series.Series]Sort Series by index labels.
See more: bigframes.geopandas.GeoSeries.sort_index
bigframes.geopandas.GeoSeries.sort_values
sort_values( *, axis=0, inplace: bool = False, ascending=True, kind: str = "quicksort", na_position: typing.Literal["first", "last"] = "last" ) -> typing.Optional[bigframes.series.Series]Sort by the values.
bigframes.geopandas.GeoSeries.std
std() -> floatReturn sample standard deviation over requested axis.
See more: bigframes.geopandas.GeoSeries.std
bigframes.geopandas.GeoSeries.sub
sub(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator sub).
See more: bigframes.geopandas.GeoSeries.sub
bigframes.geopandas.GeoSeries.subtract
subtract(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator sub).
See more: bigframes.geopandas.GeoSeries.subtract
bigframes.geopandas.GeoSeries.sum
sum() -> floatReturn the sum of the values over the requested axis.
See more: bigframes.geopandas.GeoSeries.sum
bigframes.geopandas.GeoSeries.swaplevel
swaplevel(i: int = -2, j: int = -1)Swap levels i and j in a MultiIndex.
See more: bigframes.geopandas.GeoSeries.swaplevel
bigframes.geopandas.GeoSeries.tail
tail(n: int = 5) -> bigframes.series.SeriesReturn the last n rows.
See more: bigframes.geopandas.GeoSeries.tail
bigframes.geopandas.GeoSeries.take
take( indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs ) -> bigframes.series.SeriesReturn the elements in the given positional indices along an axis.
See more: bigframes.geopandas.GeoSeries.take
bigframes.geopandas.GeoSeries.to_csv
to_csv( path_or_buf=None, sep=",", *, header: bool = True, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Write object to a comma-separated values (csv) file on Cloud Storage.
See more: bigframes.geopandas.GeoSeries.to_csv
bigframes.geopandas.GeoSeries.to_dict
to_dict( into: type[dict] = dict, *, allow_large_results: typing.Optional[bool] = None ) -> typing.MappingConvert Series to {label -> value} dict or dict-like object.
See more: bigframes.geopandas.GeoSeries.to_dict
bigframes.geopandas.GeoSeries.to_excel
to_excel( excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs ) -> NoneWrite Series to an Excel sheet.
See more: bigframes.geopandas.GeoSeries.to_excel
bigframes.geopandas.GeoSeries.to_frame
to_frame(name: typing.Hashable = None) -> bigframes.dataframe.DataFrameConvert Series to DataFrame.
See more: bigframes.geopandas.GeoSeries.to_frame
bigframes.geopandas.GeoSeries.to_json
to_json( path_or_buf=None, orient: typing.Optional[ typing.Literal["split", "records", "index", "columns", "values", "table"] ] = None, *, lines: bool = False, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Convert the object to a JSON string, written to Cloud Storage.
See more: bigframes.geopandas.GeoSeries.to_json
bigframes.geopandas.GeoSeries.to_latex
to_latex( buf=None, columns=None, header=True, index=True, *, allow_large_results=None, **kwargs ) -> typing.Optional[str]Render object to a LaTeX tabular, longtable, or nested table.
See more: bigframes.geopandas.GeoSeries.to_latex
bigframes.geopandas.GeoSeries.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listReturn a list of the values.
See more: bigframes.geopandas.GeoSeries.to_list
bigframes.geopandas.GeoSeries.to_markdown
to_markdown( buf: typing.Optional[typing.IO[str]] = None, mode: str = "wt", index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> typing.Optional[str]Print Series in Markdown-friendly format.
bigframes.geopandas.GeoSeries.to_numpy
to_numpy( dtype=None, copy=False, na_value=_NoDefault.no_default, *, allow_large_results=None, **kwargs ) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
See more: bigframes.geopandas.GeoSeries.to_numpy
bigframes.geopandas.GeoSeries.to_pandas
to_pandas( max_download_size: typing.Optional[int] = None, sampling_method: typing.Optional[str] = None, random_state: typing.Optional[int] = None, *, ordered: bool = True, dry_run: bool = False, allow_large_results: typing.Optional[bool] = None ) -> pandas.core.series.SeriesWrites Series to pandas Series.
See more: bigframes.geopandas.GeoSeries.to_pandas
bigframes.geopandas.GeoSeries.to_pandas_batches
to_pandas_batches( page_size: typing.Optional[int] = None, max_results: typing.Optional[int] = None, *, allow_large_results: typing.Optional[bool] = None ) -> typing.Iterable[pandas.core.series.Series]Stream Series results to an iterable of pandas Series.
bigframes.geopandas.GeoSeries.to_pickle
to_pickle(path, *, allow_large_results=None, **kwargs) -> NonePickle (serialize) object to file.
See more: bigframes.geopandas.GeoSeries.to_pickle
bigframes.geopandas.GeoSeries.to_string
to_string( buf=None, na_rep="NaN", float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None, min_rows=None, *, allow_large_results=None ) -> typing.Optional[str]Render a string representation of the Series.
See more: bigframes.geopandas.GeoSeries.to_string
bigframes.geopandas.GeoSeries.to_wkt
to_wkt() -> bigframes.series.SeriesConvert GeoSeries geometries to WKT.
See more: bigframes.geopandas.GeoSeries.to_wkt
bigframes.geopandas.GeoSeries.to_xarray
to_xarray(*, allow_large_results: typing.Optional[bool] = None)Return an xarray object from the pandas object.
See more: bigframes.geopandas.GeoSeries.to_xarray
bigframes.geopandas.GeoSeries.tolist
tolist(*, allow_large_results: typing.Optional[bool] = None) -> listReturn a list of the values.
See more: bigframes.geopandas.GeoSeries.tolist
bigframes.geopandas.GeoSeries.transpose
transpose() -> bigframes.series.SeriesReturn the transpose, which is by definition self.
See more: bigframes.geopandas.GeoSeries.transpose
bigframes.geopandas.GeoSeries.truediv
truediv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.geopandas.GeoSeries.truediv
bigframes.geopandas.GeoSeries.unique
unique(keep_order=True) -> bigframes.series.SeriesReturn unique values of Series object.
See more: bigframes.geopandas.GeoSeries.unique
bigframes.geopandas.GeoSeries.unstack
unstack( level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1, )Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
See more: bigframes.geopandas.GeoSeries.unstack
bigframes.geopandas.GeoSeries.update
update( other: typing.Union[bigframes.series.Series, typing.Sequence, typing.Mapping], ) -> NoneModify Series in place using values from passed Series.
See more: bigframes.geopandas.GeoSeries.update
bigframes.geopandas.GeoSeries.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
bigframes.geopandas.GeoSeries.var
var() -> floatReturn unbiased variance over requested axis.
See more: bigframes.geopandas.GeoSeries.var
bigframes.geopandas.GeoSeries.where
where(cond, other=None)Replace values where the condition is False.
See more: bigframes.geopandas.GeoSeries.where
bigframes.ml.cluster.KMeans.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.cluster.KMeans.repr
bigframes.ml.cluster.KMeans.detect_anomalies
detect_anomalies( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, contamination: float = 0.1 ) -> bigframes.dataframe.DataFrameDetect the anomaly data points of the input.
bigframes.ml.cluster.KMeans.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TCompute k-means clustering.
See more: bigframes.ml.cluster.KMeans.fit
bigframes.ml.cluster.KMeans.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.cluster.KMeans.get_params
bigframes.ml.cluster.KMeans.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict the closest cluster each sample in X belongs to.
See more: bigframes.ml.cluster.KMeans.predict
bigframes.ml.cluster.KMeans.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.cluster.KMeans.register
bigframes.ml.cluster.KMeans.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.dataframe.DataFrameCalculate evaluation metrics of the model.
See more: bigframes.ml.cluster.KMeans.score
bigframes.ml.cluster.KMeans.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.cluster.KMeansSave the model to BigQuery.
See more: bigframes.ml.cluster.KMeans.to_gbq
bigframes.ml.compose.ColumnTransformer.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.compose.ColumnTransformer.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.compose.ColumnTransformerFit all transformers using X.
bigframes.ml.compose.ColumnTransformer.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameAPI documentation for fit_transform method.
See more: bigframes.ml.compose.ColumnTransformer.fit_transform
bigframes.ml.compose.ColumnTransformer.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.compose.ColumnTransformer.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
bigframes.ml.compose.ColumnTransformer.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameTransform X separately by each transformer, concatenate results.
bigframes.ml.decomposition.MatrixFactorization.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.decomposition.MatrixFactorization.repr
bigframes.ml.decomposition.MatrixFactorization.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TFit the model according to the given training data.
See more: bigframes.ml.decomposition.MatrixFactorization.fit
bigframes.ml.decomposition.MatrixFactorization.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.decomposition.MatrixFactorization.get_params
bigframes.ml.decomposition.MatrixFactorization.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameGenerate a predicted rating for every user-item row combination for a matrix factorization model.
See more: bigframes.ml.decomposition.MatrixFactorization.predict
bigframes.ml.decomposition.MatrixFactorization.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.decomposition.MatrixFactorization.register
bigframes.ml.decomposition.MatrixFactorization.score
score(X=None, y=None) -> bigframes.dataframe.DataFrameCalculate evaluation metrics of the model.
See more: bigframes.ml.decomposition.MatrixFactorization.score
bigframes.ml.decomposition.MatrixFactorization.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.decomposition.MatrixFactorizationSave the model to BigQuery.
See more: bigframes.ml.decomposition.MatrixFactorization.to_gbq
bigframes.ml.decomposition.PCA.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.decomposition.PCA.repr
bigframes.ml.decomposition.PCA.detect_anomalies
detect_anomalies( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, contamination: float = 0.1 ) -> bigframes.dataframe.DataFrameDetect the anomaly data points of the input.
bigframes.ml.decomposition.PCA.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TFit the model according to the given training data.
See more: bigframes.ml.decomposition.PCA.fit
bigframes.ml.decomposition.PCA.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.decomposition.PCA.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict the closest cluster for each sample in X.
See more: bigframes.ml.decomposition.PCA.predict
bigframes.ml.decomposition.PCA.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.decomposition.PCA.register
bigframes.ml.decomposition.PCA.score
score(X=None, y=None) -> bigframes.dataframe.DataFrameCalculate evaluation metrics of the model.
See more: bigframes.ml.decomposition.PCA.score
bigframes.ml.decomposition.PCA.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.decomposition.PCASave the model to BigQuery.
See more: bigframes.ml.decomposition.PCA.to_gbq
bigframes.ml.ensemble.RandomForestClassifier.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.ensemble.RandomForestClassifier.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], X_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, y_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TBuild a forest of trees from the training set (X, y).
bigframes.ml.ensemble.RandomForestClassifier.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.ensemble.RandomForestClassifier.get_params
bigframes.ml.ensemble.RandomForestClassifier.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict regression target for X.
See more: bigframes.ml.ensemble.RandomForestClassifier.predict
bigframes.ml.ensemble.RandomForestClassifier.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.ensemble.RandomForestClassifier.register
bigframes.ml.ensemble.RandomForestClassifier.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], )Calculate evaluation metrics of the model.
See more: bigframes.ml.ensemble.RandomForestClassifier.score
bigframes.ml.ensemble.RandomForestClassifier.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.ensemble.RandomForestClassifierSave the model to BigQuery.
See more: bigframes.ml.ensemble.RandomForestClassifier.to_gbq
bigframes.ml.ensemble.RandomForestRegressor.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.ensemble.RandomForestRegressor.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], X_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, y_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TBuild a forest of trees from the training set (X, y).
bigframes.ml.ensemble.RandomForestRegressor.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.ensemble.RandomForestRegressor.get_params
bigframes.ml.ensemble.RandomForestRegressor.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict regression target for X.
See more: bigframes.ml.ensemble.RandomForestRegressor.predict
bigframes.ml.ensemble.RandomForestRegressor.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.ensemble.RandomForestRegressor.register
bigframes.ml.ensemble.RandomForestRegressor.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], )Calculate evaluation metrics of the model.
bigframes.ml.ensemble.RandomForestRegressor.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.ensemble.RandomForestRegressorSave the model to BigQuery.
See more: bigframes.ml.ensemble.RandomForestRegressor.to_gbq
bigframes.ml.ensemble.XGBClassifier.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.ensemble.XGBClassifier.repr
bigframes.ml.ensemble.XGBClassifier.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], X_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, y_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TFit gradient boosting model.
See more: bigframes.ml.ensemble.XGBClassifier.fit
bigframes.ml.ensemble.XGBClassifier.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.ensemble.XGBClassifier.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict using the XGB model.
bigframes.ml.ensemble.XGBClassifier.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
bigframes.ml.ensemble.XGBClassifier.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], )Return the mean accuracy on the given test data and labels.
bigframes.ml.ensemble.XGBClassifier.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.ensemble.XGBClassifierSave the model to BigQuery.
bigframes.ml.ensemble.XGBRegressor.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.ensemble.XGBRegressor.repr
bigframes.ml.ensemble.XGBRegressor.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], X_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, y_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TFit gradient boosting model.
See more: bigframes.ml.ensemble.XGBRegressor.fit
bigframes.ml.ensemble.XGBRegressor.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.ensemble.XGBRegressor.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict using the XGB model.
bigframes.ml.ensemble.XGBRegressor.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
bigframes.ml.ensemble.XGBRegressor.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], )Calculate evaluation metrics of the model.
See more: bigframes.ml.ensemble.XGBRegressor.score
bigframes.ml.ensemble.XGBRegressor.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.ensemble.XGBRegressorSave the model to BigQuery.
bigframes.ml.forecasting.ARIMAPlus.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.forecasting.ARIMAPlus.repr
bigframes.ml.forecasting.ARIMAPlus.detect_anomalies
detect_anomalies( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, anomaly_prob_threshold: float = 0.95 ) -> bigframes.dataframe.DataFrameDetect the anomaly data points of the input.
See more: bigframes.ml.forecasting.ARIMAPlus.detect_anomalies
bigframes.ml.forecasting.ARIMAPlus.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], transforms=None, id_col: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, )API documentation for fit method.
See more: bigframes.ml.forecasting.ARIMAPlus.fit
bigframes.ml.forecasting.ARIMAPlus.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.forecasting.ARIMAPlus.predict
predict( X=None, *, horizon: int = 3, confidence_level: float = 0.95 ) -> bigframes.dataframe.DataFrameForecast time series at future horizon.
bigframes.ml.forecasting.ARIMAPlus.predict_explain
predict_explain( X=None, *, horizon: int = 3, confidence_level: float = 0.95 ) -> bigframes.dataframe.DataFrameExplain Forecast time series at future horizon.
See more: bigframes.ml.forecasting.ARIMAPlus.predict_explain
bigframes.ml.forecasting.ARIMAPlus.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
bigframes.ml.forecasting.ARIMAPlus.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], id_col: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameCalculate evaluation metrics of the model.
See more: bigframes.ml.forecasting.ARIMAPlus.score
bigframes.ml.forecasting.ARIMAPlus.summary
summary(show_all_candidate_models: bool = False) -> bigframes.dataframe.DataFrameSummary of the evaluation metrics of the time series model.
bigframes.ml.forecasting.ARIMAPlus.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.forecasting.ARIMAPlusSave the model to BigQuery.
bigframes.ml.imported.ONNXModel.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.imported.ONNXModel.repr
bigframes.ml.imported.ONNXModel.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.imported.ONNXModel.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
See more: bigframes.ml.imported.ONNXModel.predict
bigframes.ml.imported.ONNXModel.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.imported.ONNXModel.register
bigframes.ml.imported.ONNXModel.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.imported.ONNXModelSave the model to BigQuery.
See more: bigframes.ml.imported.ONNXModel.to_gbq
bigframes.ml.imported.TensorFlowModel.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.imported.TensorFlowModel.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.imported.TensorFlowModel.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
bigframes.ml.imported.TensorFlowModel.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
bigframes.ml.imported.TensorFlowModel.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.imported.TensorFlowModelSave the model to BigQuery.
bigframes.ml.imported.XGBoostModel.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.imported.XGBoostModel.repr
bigframes.ml.imported.XGBoostModel.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.imported.XGBoostModel.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
bigframes.ml.imported.XGBoostModel.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
bigframes.ml.imported.XGBoostModel.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.imported.XGBoostModelSave the model to BigQuery.
bigframes.ml.impute.SimpleImputer.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.impute.SimpleImputer.repr
bigframes.ml.impute.SimpleImputer.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.impute.SimpleImputerFit the imputer on X.
See more: bigframes.ml.impute.SimpleImputer.fit
bigframes.ml.impute.SimpleImputer.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameFit to data, then transform it.
bigframes.ml.impute.SimpleImputer.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.impute.SimpleImputer.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
See more: bigframes.ml.impute.SimpleImputer.to_gbq
bigframes.ml.impute.SimpleImputer.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameImpute all missing values in X.
bigframes.ml.linear_model.LinearRegression.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.linear_model.LinearRegression.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], X_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, y_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TFit linear model.
bigframes.ml.linear_model.LinearRegression.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.linear_model.LinearRegression.get_params
bigframes.ml.linear_model.LinearRegression.global_explain
global_explain() -> bigframes.dataframe.DataFrameProvide explanations for an entire linear regression model.
See more: bigframes.ml.linear_model.LinearRegression.global_explain
bigframes.ml.linear_model.LinearRegression.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict using the linear model.
See more: bigframes.ml.linear_model.LinearRegression.predict
bigframes.ml.linear_model.LinearRegression.predict_explain
predict_explain( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, top_k_features: int = 5 ) -> bigframes.dataframe.DataFrameExplain predictions for a linear regression model.
See more: bigframes.ml.linear_model.LinearRegression.predict_explain
bigframes.ml.linear_model.LinearRegression.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.linear_model.LinearRegression.register
bigframes.ml.linear_model.LinearRegression.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameCalculate evaluation metrics of the model.
bigframes.ml.linear_model.LinearRegression.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.linear_model.LinearRegressionSave the model to BigQuery.
bigframes.ml.linear_model.LogisticRegression.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.linear_model.LogisticRegression.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], X_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, y_eval: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.ml.base._TFit the model according to the given training data.
bigframes.ml.linear_model.LogisticRegression.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.linear_model.LogisticRegression.get_params
bigframes.ml.linear_model.LogisticRegression.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict class labels for samples in X.
See more: bigframes.ml.linear_model.LogisticRegression.predict
bigframes.ml.linear_model.LogisticRegression.predict_explain
predict_explain( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, top_k_features: int = 5 ) -> bigframes.dataframe.DataFrameExplain predictions for a logistic regression model.
See more: bigframes.ml.linear_model.LogisticRegression.predict_explain
bigframes.ml.linear_model.LogisticRegression.register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._TRegister the model to Vertex AI.
See more: bigframes.ml.linear_model.LogisticRegression.register
bigframes.ml.linear_model.LogisticRegression.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameReturn the mean accuracy on the given test data and labels.
See more: bigframes.ml.linear_model.LogisticRegression.score
bigframes.ml.linear_model.LogisticRegression.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.linear_model.LogisticRegressionSave the model to BigQuery.
See more: bigframes.ml.linear_model.LogisticRegression.to_gbq
bigframes.ml.llm.Claude3TextGenerator.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.llm.Claude3TextGenerator.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.llm.Claude3TextGenerator.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, max_output_tokens: int = 128, top_k: int = 40, top_p: float = 0.95, max_retries: int = 0 ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
bigframes.ml.llm.Claude3TextGenerator.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.llm.Claude3TextGeneratorSave the model to BigQuery.
bigframes.ml.llm.GeminiTextGenerator.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.llm.GeminiTextGenerator.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.ml.llm.GeminiTextGeneratorFine tune GeminiTextGenerator model.
See more: bigframes.ml.llm.GeminiTextGenerator.fit
bigframes.ml.llm.GeminiTextGenerator.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.llm.GeminiTextGenerator.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, temperature: float = 0.9, max_output_tokens: int = 8192, top_k: int = 40, top_p: float = 1.0, ground_with_google_search: bool = False, max_retries: int = 0, prompt: typing.Optional[ typing.Iterable[typing.Union[str, bigframes.series.Series]] ] = None, output_schema: typing.Optional[typing.Mapping[str, str]] = None ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
bigframes.ml.llm.GeminiTextGenerator.score
score( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], task_type: typing.Literal[ "text_generation", "classification", "summarization", "question_answering" ] = "text_generation", ) -> bigframes.dataframe.DataFrameCalculate evaluation metrics of the model.
bigframes.ml.llm.GeminiTextGenerator.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.llm.GeminiTextGeneratorSave the model to BigQuery.
bigframes.ml.llm.MultimodalEmbeddingGenerator.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.llm.MultimodalEmbeddingGenerator.repr
bigframes.ml.llm.MultimodalEmbeddingGenerator.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.llm.MultimodalEmbeddingGenerator.get_params
bigframes.ml.llm.MultimodalEmbeddingGenerator.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, max_retries: int = 0 ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
See more: bigframes.ml.llm.MultimodalEmbeddingGenerator.predict
bigframes.ml.llm.MultimodalEmbeddingGenerator.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.llm.MultimodalEmbeddingGeneratorSave the model to BigQuery.
See more: bigframes.ml.llm.MultimodalEmbeddingGenerator.to_gbq
bigframes.ml.llm.TextEmbeddingGenerator.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.llm.TextEmbeddingGenerator.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.llm.TextEmbeddingGenerator.get_params
bigframes.ml.llm.TextEmbeddingGenerator.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], *, max_retries: int = 0 ) -> bigframes.dataframe.DataFramePredict the result from input DataFrame.
bigframes.ml.llm.TextEmbeddingGenerator.to_gbq
to_gbq( model_name: str, replace: bool = False ) -> bigframes.ml.llm.TextEmbeddingGeneratorSave the model to BigQuery.
bigframes.ml.model_selection.KFold.get_n_splits
get_n_splits() -> intReturns the number of splitting iterations in the cross-validator.
bigframes.ml.model_selection.KFold.split
split( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> typing.Generator[ tuple[ typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series, NoneType], ..., ], None, None, ]Generate indices to split data into training and test set.
See more: bigframes.ml.model_selection.KFold.split
bigframes.ml.pipeline.Pipeline.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.pipeline.Pipeline.repr
bigframes.ml.pipeline.Pipeline.fit
fit( X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y: typing.Optional[ typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series] ] = None, ) -> bigframes.ml.pipeline.PipelineFit the model.
See more: bigframes.ml.pipeline.Pipeline.fit
bigframes.ml.pipeline.Pipeline.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.pipeline.Pipeline.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameAPI documentation for predict method.
See more: bigframes.ml.pipeline.Pipeline.predict
bigframes.ml.pipeline.Pipeline.score
score( X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y: typing.Optional[ typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series] ] = None, ) -> bigframes.dataframe.DataFrameAPI documentation for score method.
See more: bigframes.ml.pipeline.Pipeline.score
bigframes.ml.pipeline.Pipeline.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.pipeline.PipelineSave the pipeline to BigQuery.
See more: bigframes.ml.pipeline.Pipeline.to_gbq
bigframes.ml.preprocessing.KBinsDiscretizer.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.preprocessing.KBinsDiscretizer.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.preprocessing.KBinsDiscretizerFit the estimator.
bigframes.ml.preprocessing.KBinsDiscretizer.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameFit to data, then transform it.
See more: bigframes.ml.preprocessing.KBinsDiscretizer.fit_transform
bigframes.ml.preprocessing.KBinsDiscretizer.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.KBinsDiscretizer.get_params
bigframes.ml.preprocessing.KBinsDiscretizer.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
See more: bigframes.ml.preprocessing.KBinsDiscretizer.to_gbq
bigframes.ml.preprocessing.KBinsDiscretizer.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameDiscretize the data.
See more: bigframes.ml.preprocessing.KBinsDiscretizer.transform
bigframes.ml.preprocessing.LabelEncoder.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.preprocessing.LabelEncoder.fit
fit( y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.ml.preprocessing.LabelEncoderFit label encoder.
bigframes.ml.preprocessing.LabelEncoder.fit_transform
fit_transform( y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameAPI documentation for fit_transform method.
See more: bigframes.ml.preprocessing.LabelEncoder.fit_transform
bigframes.ml.preprocessing.LabelEncoder.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.LabelEncoder.get_params
bigframes.ml.preprocessing.LabelEncoder.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
bigframes.ml.preprocessing.LabelEncoder.transform
transform( y: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameTransform y using label encoding.
bigframes.ml.preprocessing.MaxAbsScaler.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.preprocessing.MaxAbsScaler.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.preprocessing.MaxAbsScalerCompute the maximum absolute value to be used for later scaling.
bigframes.ml.preprocessing.MaxAbsScaler.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameFit to data, then transform it.
See more: bigframes.ml.preprocessing.MaxAbsScaler.fit_transform
bigframes.ml.preprocessing.MaxAbsScaler.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.MaxAbsScaler.get_params
bigframes.ml.preprocessing.MaxAbsScaler.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
bigframes.ml.preprocessing.MaxAbsScaler.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameScale the data.
bigframes.ml.preprocessing.MinMaxScaler.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.preprocessing.MinMaxScaler.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.preprocessing.MinMaxScalerCompute the minimum and maximum to be used for later scaling.
bigframes.ml.preprocessing.MinMaxScaler.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameFit to data, then transform it.
See more: bigframes.ml.preprocessing.MinMaxScaler.fit_transform
bigframes.ml.preprocessing.MinMaxScaler.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.MinMaxScaler.get_params
bigframes.ml.preprocessing.MinMaxScaler.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
bigframes.ml.preprocessing.MinMaxScaler.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameScale the data.
bigframes.ml.preprocessing.OneHotEncoder.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.preprocessing.OneHotEncoder.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.preprocessing.OneHotEncoderFit OneHotEncoder to X.
bigframes.ml.preprocessing.OneHotEncoder.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameAPI documentation for fit_transform method.
See more: bigframes.ml.preprocessing.OneHotEncoder.fit_transform
bigframes.ml.preprocessing.OneHotEncoder.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.OneHotEncoder.get_params
bigframes.ml.preprocessing.OneHotEncoder.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
bigframes.ml.preprocessing.OneHotEncoder.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameTransform X using one-hot encoding.
See more: bigframes.ml.preprocessing.OneHotEncoder.transform
bigframes.ml.preprocessing.PolynomialFeatures.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.preprocessing.PolynomialFeatures.repr
bigframes.ml.preprocessing.PolynomialFeatures.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.preprocessing.PolynomialFeaturesCompute number of output features.
bigframes.ml.preprocessing.PolynomialFeatures.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameFit to data, then transform it.
See more: bigframes.ml.preprocessing.PolynomialFeatures.fit_transform
bigframes.ml.preprocessing.PolynomialFeatures.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.PolynomialFeatures.get_params
bigframes.ml.preprocessing.PolynomialFeatures.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
See more: bigframes.ml.preprocessing.PolynomialFeatures.to_gbq
bigframes.ml.preprocessing.PolynomialFeatures.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFrameTransform data to polynomial features.
See more: bigframes.ml.preprocessing.PolynomialFeatures.transform
bigframes.ml.preprocessing.StandardScaler.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
bigframes.ml.preprocessing.StandardScaler.fit
fit( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y=None, ) -> bigframes.ml.preprocessing.StandardScalerCompute the mean and std to be used for later scaling.
bigframes.ml.preprocessing.StandardScaler.fit_transform
fit_transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], y: typing.Optional[ typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ] ] = None, ) -> bigframes.dataframe.DataFrameFit to data, then transform it.
See more: bigframes.ml.preprocessing.StandardScaler.fit_transform
bigframes.ml.preprocessing.StandardScaler.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
See more: bigframes.ml.preprocessing.StandardScaler.get_params
bigframes.ml.preprocessing.StandardScaler.to_gbq
to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._TSave the transformer as a BigQuery model.
bigframes.ml.preprocessing.StandardScaler.transform
transform( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePerform standardization by centering and scaling.
See more: bigframes.ml.preprocessing.StandardScaler.transform
bigframes.ml.remote.VertexAIModel.__repr__
__repr__()Print the estimator's constructor with all non-default parameter values.
See more: bigframes.ml.remote.VertexAIModel.repr
bigframes.ml.remote.VertexAIModel.get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]Get parameters for this estimator.
bigframes.ml.remote.VertexAIModel.predict
predict( X: typing.Union[ bigframes.dataframe.DataFrame, bigframes.series.Series, pandas.core.frame.DataFrame, pandas.core.series.Series, ], ) -> bigframes.dataframe.DataFramePredict the result from the input DataFrame.
bigframes.operations.ai.AIAccessor.classify
classify( instruction: str, model, labels: typing.Sequence[str], output_column: str = "result", ground_with_google_search: bool = False, )Classifies the rows of dataframes based on user instruction into the provided labels.
bigframes.operations.ai.AIAccessor.filter
filter(instruction: str, model, ground_with_google_search: bool = False)Filters the DataFrame with the semantics of the user instruction.
bigframes.operations.ai.AIAccessor.forecast
forecast( timestamp_column: str, data_column: str, *, model: str = "TimesFM 2.0", id_columns: typing.Optional[typing.Iterable[str]] = None, horizon: int = 10, confidence_level: float = 0.95 )Forecast time series at future horizon.
bigframes.operations.ai.AIAccessor.join
join(other, instruction: str, model, ground_with_google_search: bool = False)Joines two dataframes by applying the instruction over each pair of rows from the left and right table.
See more: bigframes.operations.ai.AIAccessor.join
bigframes.operations.ai.AIAccessor.map
map( instruction: str, model, output_schema: typing.Optional[typing.Dict[str, str]] = None, ground_with_google_search: bool = False, )Maps the DataFrame with the semantics of the user instruction.
See more: bigframes.operations.ai.AIAccessor.map
bigframes.operations.ai.AIAccessor.search
search( search_column: str, query: str, top_k: int, model, score_column: typing.Optional[str] = None, )Performs AI semantic search on the DataFrame.
bigframes.operations.ai.AIAccessor.sim_join
sim_join( other, left_on: str, right_on: str, model, top_k: int = 3, score_column: typing.Optional[str] = None, max_rows: int = 1000, )Joins two dataframes based on the similarity of the specified columns.
bigframes.operations.blob.BlobAccessor.audio_transcribe
audio_transcribe( *, engine: typing.Literal["bigquery"] = "bigquery", connection: typing.Optional[str] = None, model_name: typing.Optional[ typing.Literal["gemini-2.0-flash-001", "gemini-2.0-flash-lite-001"] ] = None, verbose: bool = False ) -> bigframes.series.SeriesTranscribe audio content using a Gemini multimodal model.
See more: bigframes.operations.blob.BlobAccessor.audio_transcribe
bigframes.operations.blob.BlobAccessor.authorizer
authorizer() -> bigframes.series.SeriesAuthorizers of the Blob.
bigframes.operations.blob.BlobAccessor.content_type
content_type() -> bigframes.series.SeriesRetrieve the content type of the Blob.
See more: bigframes.operations.blob.BlobAccessor.content_type
bigframes.operations.blob.BlobAccessor.display
display( n: int = 3, *, content_type: str = "", width: typing.Optional[int] = None, height: typing.Optional[int] = None )Display the blob content in the IPython Notebook environment.
bigframes.operations.blob.BlobAccessor.exif
exif( *, engine: typing.Literal[None, "pillow"] = None, connection: typing.Optional[str] = None, max_batching_rows: int = 8192, container_cpu: typing.Union[float, int] = 0.33, container_memory: str = "512Mi", verbose: bool = False ) -> bigframes.series.SeriesExtract EXIF data.
bigframes.operations.blob.BlobAccessor.get_runtime_json_str
get_runtime_json_str( mode: str = "R", *, with_metadata: bool = False ) -> bigframes.series.SeriesGet the runtime (contains signed URL to access gcs data) and apply the ToJSONSTring transformation.
See more: bigframes.operations.blob.BlobAccessor.get_runtime_json_str
bigframes.operations.blob.BlobAccessor.image_blur
image_blur( ksize: tuple[int, int], *, engine: typing.Literal[None, "opencv"] = None, dst: typing.Optional[typing.Union[str, bigframes.series.Series]] = None, connection: typing.Optional[str] = None, max_batching_rows: int = 8192, container_cpu: typing.Union[float, int] = 0.33, container_memory: str = "512Mi", verbose: bool = False ) -> bigframes.series.SeriesBlurs images.
bigframes.operations.blob.BlobAccessor.image_normalize
image_normalize( *, engine: typing.Literal[None, "opencv"] = None, alpha: float = 1.0, beta: float = 0.0, norm_type: str = "l2", dst: typing.Optional[typing.Union[str, bigframes.series.Series]] = None, connection: typing.Optional[str] = None, max_batching_rows: int = 8192, container_cpu: typing.Union[float, int] = 0.33, container_memory: str = "512Mi", verbose: bool = False ) -> bigframes.series.SeriesNormalize images.
See more: bigframes.operations.blob.BlobAccessor.image_normalize
bigframes.operations.blob.BlobAccessor.image_resize
image_resize( dsize: tuple[int, int] = (0, 0), *, engine: typing.Literal[None, "opencv"] = None, fx: float = 0.0, fy: float = 0.0, dst: typing.Optional[typing.Union[str, bigframes.series.Series]] = None, connection: typing.Optional[str] = None, max_batching_rows: int = 8192, container_cpu: typing.Union[float, int] = 0.33, container_memory: str = "512Mi", verbose: bool = False )Resize images.
See more: bigframes.operations.blob.BlobAccessor.image_resize
bigframes.operations.blob.BlobAccessor.md5_hash
md5_hash() -> bigframes.series.SeriesRetrieve the md5 hash of the Blob.
bigframes.operations.blob.BlobAccessor.metadata
metadata() -> bigframes.series.SeriesRetrieve the metadata of the Blob.
bigframes.operations.blob.BlobAccessor.pdf_chunk
pdf_chunk( *, engine: typing.Literal[None, "pypdf"] = None, connection: typing.Optional[str] = None, chunk_size: int = 2000, overlap_size: int = 200, max_batching_rows: int = 1, container_cpu: typing.Union[float, int] = 2, container_memory: str = "1Gi", verbose: bool = False ) -> bigframes.series.SeriesExtracts and chunks text from PDF URLs and saves the text as arrays of strings.
bigframes.operations.blob.BlobAccessor.pdf_extract
pdf_extract( *, engine: typing.Literal[None, "pypdf"] = None, connection: typing.Optional[str] = None, max_batching_rows: int = 1, container_cpu: typing.Union[float, int] = 2, container_memory: str = "1Gi", verbose: bool = False ) -> bigframes.series.SeriesExtracts text from PDF URLs and saves the text as string.
See more: bigframes.operations.blob.BlobAccessor.pdf_extract
bigframes.operations.blob.BlobAccessor.read_url
read_url() -> bigframes.series.SeriesRetrieve the read URL of the Blob.
bigframes.operations.blob.BlobAccessor.size
size() -> bigframes.series.SeriesRetrieve the file size of the Blob.
bigframes.operations.blob.BlobAccessor.updated
updated() -> bigframes.series.SeriesRetrieve the updated time of the Blob.
bigframes.operations.blob.BlobAccessor.uri
uri() -> bigframes.series.SeriesURIs of the Blob.
bigframes.operations.blob.BlobAccessor.version
version() -> bigframes.series.SeriesVersions of the Blob.
bigframes.operations.blob.BlobAccessor.write_url
write_url() -> bigframes.series.SeriesRetrieve the write URL of the Blob.
bigframes.operations.datetimes.DatetimeMethods.day_name
day_name() -> bigframes.series.SeriesReturn the day names in english.
See more: bigframes.operations.datetimes.DatetimeMethods.day_name
bigframes.operations.datetimes.DatetimeMethods.floor
floor(freq: str) -> bigframes.series.SeriesPerform floor operation on the data to the specified freq.
See more: bigframes.operations.datetimes.DatetimeMethods.floor
bigframes.operations.datetimes.DatetimeMethods.isocalendar
isocalendar() -> bigframes.dataframe.DataFrameCalculate year, week, and day according to the ISO 8601 standard.
See more: bigframes.operations.datetimes.DatetimeMethods.isocalendar
bigframes.operations.datetimes.DatetimeMethods.normalize
normalize() -> bigframes.series.SeriesConvert times to midnight.
See more: bigframes.operations.datetimes.DatetimeMethods.normalize
bigframes.operations.datetimes.DatetimeMethods.strftime
strftime(date_format: str) -> bigframes.series.SeriesConvert to string Series using specified date_format.
See more: bigframes.operations.datetimes.DatetimeMethods.strftime
bigframes.operations.datetimes.DatetimeMethods.total_seconds
total_seconds() -> bigframes.series.SeriesReturn total duration of each element expressed in seconds.
See more: bigframes.operations.datetimes.DatetimeMethods.total_seconds
bigframes.operations.lists.ListAccessor.__getitem__
__getitem__(key: typing.Union[int, slice]) -> bigframes.series.SeriesIndex or slice lists in the Series.
bigframes.operations.lists.ListAccessor.len
len()Compute the length of each list in the Series.
bigframes.operations.plotting.PlotAccessor.area
area( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, stacked: bool = True, **kwargs )Draw a stacked area plot.
bigframes.operations.plotting.PlotAccessor.bar
bar( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a vertical bar plot.
bigframes.operations.plotting.PlotAccessor.barh
barh( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a horizontal bar plot.
bigframes.operations.plotting.PlotAccessor.hist
hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)Draw one histogram of the DataFrame’s columns.
bigframes.operations.plotting.PlotAccessor.line
line( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Plot Series or DataFrame as lines.
bigframes.operations.plotting.PlotAccessor.pie
pie(y: typing.Optional[typing.Hashable] = None, **kwargs)Generate a pie plot.
bigframes.operations.plotting.PlotAccessor.scatter
scatter( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, **kwargs )Create a scatter plot with varying marker point size and color.
See more: bigframes.operations.plotting.PlotAccessor.scatter
bigframes.operations.plotting.PlotAccessor
PlotAccessor(data)Make plots of Series or DataFrame with the matplotlib backend.
bigframes.operations.strings.StringMethods.capitalize
capitalize() -> bigframes.operations.strings.TConvert strings in the Series/Index to be capitalized.
See more: bigframes.operations.strings.StringMethods.capitalize
bigframes.operations.strings.StringMethods.cat
cat( others: typing.Union[ str, bigframes.core.indexes.base.Index, bigframes.series.Series ], *, join: typing.Literal["outer", "left"] = "left" ) -> bigframes.operations.strings.TConcatenate strings in the Series/Index with given separator.
bigframes.operations.strings.StringMethods.center
center(width: int, fillchar: str = " ") -> bigframes.operations.strings.TPad left and right side of strings in the Series/Index.
bigframes.operations.strings.StringMethods.contains
contains( pat, case: bool = True, flags: int = 0, *, regex: bool = True ) -> bigframes.operations.strings.TTest if pattern or regex is contained within a string of a Series or Index.
See more: bigframes.operations.strings.StringMethods.contains
bigframes.operations.strings.StringMethods.endswith
endswith(pat: typing.Union[str, tuple[str, ...]]) -> bigframes.operations.strings.TTest if the end of each string element matches a pattern.
See more: bigframes.operations.strings.StringMethods.endswith
bigframes.operations.strings.StringMethods.extract
extract(pat: str, flags: int = 0) -> bigframes.dataframe.DataFrameExtract capture groups in the regex pat as columns in a DataFrame.
See more: bigframes.operations.strings.StringMethods.extract
bigframes.operations.strings.StringMethods.find
find( sub: str, start: typing.Optional[int] = None, end: typing.Optional[int] = None ) -> bigframes.operations.strings.TReturn lowest indexes in each strings in the Series/Index.
bigframes.operations.strings.StringMethods.fullmatch
fullmatch(pat, case=True, flags=0) -> bigframes.operations.strings.TDetermine if each string entirely matches a regular expression.
See more: bigframes.operations.strings.StringMethods.fullmatch
bigframes.operations.strings.StringMethods.get
get(i: int) -> bigframes.operations.strings.TExtract element from each component at specified position or with specified key.
bigframes.operations.strings.StringMethods.isalnum
isalnum() -> bigframes.operations.strings.TCheck whether all characters in each string are alphanumeric.
See more: bigframes.operations.strings.StringMethods.isalnum
bigframes.operations.strings.StringMethods.isalpha
isalpha() -> bigframes.operations.strings.TCheck whether all characters in each string are alphabetic.
See more: bigframes.operations.strings.StringMethods.isalpha
bigframes.operations.strings.StringMethods.isdecimal
isdecimal() -> bigframes.operations.strings.TCheck whether all characters in each string are decimal.
See more: bigframes.operations.strings.StringMethods.isdecimal
bigframes.operations.strings.StringMethods.isdigit
isdigit() -> bigframes.operations.strings.TCheck whether all characters in each string are digits.
See more: bigframes.operations.strings.StringMethods.isdigit
bigframes.operations.strings.StringMethods.islower
islower() -> bigframes.operations.strings.TCheck whether all characters in each string are lowercase.
See more: bigframes.operations.strings.StringMethods.islower
bigframes.operations.strings.StringMethods.isnumeric
isnumeric() -> bigframes.operations.strings.TCheck whether all characters in each string are numeric.
See more: bigframes.operations.strings.StringMethods.isnumeric
bigframes.operations.strings.StringMethods.isspace
isspace() -> bigframes.operations.strings.TCheck whether all characters in each string are whitespace.
See more: bigframes.operations.strings.StringMethods.isspace
bigframes.operations.strings.StringMethods.isupper
isupper() -> bigframes.operations.strings.TCheck whether all characters in each string are uppercase.
See more: bigframes.operations.strings.StringMethods.isupper
bigframes.operations.strings.StringMethods.join
join(sep: str) -> bigframes.operations.strings.TJoin lists contained as elements in the Series/Index with passed delimiter.
bigframes.operations.strings.StringMethods.len
len() -> bigframes.operations.strings.TCompute the length of each element in the Series/Index.
bigframes.operations.strings.StringMethods.ljust
ljust(width, fillchar=" ") -> bigframes.operations.strings.TPad right side of strings in the Series/Index up to width.
bigframes.operations.strings.StringMethods.lower
lower() -> bigframes.operations.strings.TConvert strings in the Series/Index to lowercase.
bigframes.operations.strings.StringMethods.lstrip
lstrip(to_strip: typing.Optional[str] = None) -> bigframes.operations.strings.TRemove leading characters.
bigframes.operations.strings.StringMethods.match
match(pat, case=True, flags=0) -> bigframes.operations.strings.TDetermine if each string starts with a match of a regular expression.
bigframes.operations.strings.StringMethods.pad
pad(width, side="left", fillchar=" ") -> bigframes.operations.strings.TPad strings in the Series/Index up to width.
bigframes.operations.strings.StringMethods.repeat
repeat(repeats: int) -> bigframes.operations.strings.TDuplicate each string in the Series or Index.
bigframes.operations.strings.StringMethods.replace
replace( pat: typing.Union[str, re.Pattern], repl: str, *, case: typing.Optional[bool] = None, flags: int = 0, regex: bool = False ) -> bigframes.operations.strings.TReplace each occurrence of pattern/regex in the Series/Index.
See more: bigframes.operations.strings.StringMethods.replace
bigframes.operations.strings.StringMethods.reverse
reverse() -> bigframes.operations.strings.TReverse strings in the Series.
See more: bigframes.operations.strings.StringMethods.reverse
bigframes.operations.strings.StringMethods.rjust
rjust(width, fillchar=" ") -> bigframes.operations.strings.TPad left side of strings in the Series/Index up to width.
bigframes.operations.strings.StringMethods.rstrip
rstrip(to_strip: typing.Optional[str] = None) -> bigframes.operations.strings.TRemove trailing characters.
bigframes.operations.strings.StringMethods.slice
slice( start: typing.Optional[int] = None, stop: typing.Optional[int] = None ) -> bigframes.operations.strings.TSlice substrings from each element in the Series or Index.
bigframes.operations.strings.StringMethods.split
split( pat: str = " ", regex: typing.Optional[bool] = None ) -> bigframes.operations.strings.TSplit strings around given separator/delimiter.
bigframes.operations.strings.StringMethods.startswith
startswith( pat: typing.Union[str, tuple[str, ...]], ) -> bigframes.operations.strings.TTest if the start of each string element matches a pattern.
See more: bigframes.operations.strings.StringMethods.startswith
bigframes.operations.strings.StringMethods.strip
strip(to_strip: typing.Optional[str] = None) -> bigframes.operations.strings.TRemove leading and trailing characters.
bigframes.operations.strings.StringMethods.to_blob
to_blob(connection: typing.Optional[str] = None) -> bigframes.operations.strings.TCreate a BigFrames Blob series from a series of URIs.
See more: bigframes.operations.strings.StringMethods.to_blob
bigframes.operations.strings.StringMethods.upper
upper() -> bigframes.operations.strings.TConvert strings in the Series/Index to uppercase.
bigframes.operations.strings.StringMethods.zfill
zfill(width: int) -> bigframes.operations.strings.TPad strings in the Series/Index by prepending '0' characters.
bigframes.operations.structs.StructAccessor.explode
explode() -> bigframes.dataframe.DataFrameExtract all child fields of a struct as a DataFrame.
See more: bigframes.operations.structs.StructAccessor.explode
bigframes.operations.structs.StructAccessor.field
field(name_or_index: str | int) -> bigframes.series.SeriesExtract a child field of a struct as a Series.
bigframes.operations.structs.StructFrameAccessor.explode
explode(column, *, separator: str = ".") -> bigframes.dataframe.DataFrameExtract all child fields of struct column(s) and add to the DataFrame.
See more: bigframes.operations.structs.StructFrameAccessor.explode
bigframes.pandas.ArrowDtype.__from_arrow__
__from_arrow__(array: pyarrow.lib.Array | pyarrow.lib.ChunkedArray)Construct IntegerArray/FloatingArray from pyarrow Array/ChunkedArray.
See more: bigframes.pandas.ArrowDtype.from_arrow
bigframes.pandas.ArrowDtype.construct_array_type
construct_array_type() -> type_t[ArrowExtensionArray]Return the array type associated with this dtype.
bigframes.pandas.ArrowDtype.construct_from_string
construct_from_string(string: str) -> pandas.core.dtypes.dtypes.ArrowDtypeConstruct this type from a string.
bigframes.pandas.BooleanDtype.__from_arrow__
__from_arrow__(array: pyarrow.Array | pyarrow.ChunkedArray) -> BooleanArrayConstruct BooleanArray from pyarrow Array/ChunkedArray.
See more: bigframes.pandas.BooleanDtype.from_arrow
bigframes.pandas.BooleanDtype.construct_array_type
construct_array_type() -> type_t[BooleanArray]Return the array type associated with this dtype.
See more: bigframes.pandas.BooleanDtype.construct_array_type
bigframes.pandas.DataFrame.__abs__
__abs__() -> bigframes.dataframe.DataFrameReturn the absolute value of the argument.
See more: bigframes.pandas.DataFrame.abs
bigframes.pandas.DataFrame.__add__
__add__(other) -> bigframes.dataframe.DataFrameGet addition of DataFrame and other, column-wise, using arithmetic operator +.
See more: bigframes.pandas.DataFrame.add
bigframes.pandas.DataFrame.__and__
__and__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise AND of DataFrame and other, element-wise, using operator &.
See more: bigframes.pandas.DataFrame.and
bigframes.pandas.DataFrame.__array__
__array__(dtype=None, copy: typing.Optional[bool] = None) -> numpy.ndarrayReturns the rows as NumPy array.
See more: bigframes.pandas.DataFrame.array
bigframes.pandas.DataFrame.__array_ufunc__
__array_ufunc__( ufunc: numpy.ufunc, method: str, *inputs, **kwargs ) -> bigframes.dataframe.DataFrameUsed to support numpy ufuncs.
See more: bigframes.pandas.DataFrame.array_ufunc
bigframes.pandas.DataFrame.__eq__
__eq__(other) -> bigframes.dataframe.DataFrameCheck equality of DataFrame and other, element-wise, using logical operator ==.
See more: bigframes.pandas.DataFrame.eq
bigframes.pandas.DataFrame.__floordiv__
__floordiv__(other)Get integer division of DataFrame by other, using arithmetic operator //.
See more: bigframes.pandas.DataFrame.floordiv
bigframes.pandas.DataFrame.__ge__
__ge__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is greater than or equal to other, element-wise, using logical operator >=.
See more: bigframes.pandas.DataFrame.ge
bigframes.pandas.DataFrame.__getitem__
Gets the specified column(s) from the DataFrame.
See more: bigframes.pandas.DataFrame.getitem
bigframes.pandas.DataFrame.__gt__
__gt__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is greater than other, element-wise, using logical operator >.
See more: bigframes.pandas.DataFrame.gt
bigframes.pandas.DataFrame.__invert__
__invert__() -> bigframes.dataframe.DataFrameReturns the bitwise inversion of the DataFrame, element-wise using operator ````.
See more: bigframes.pandas.DataFrame.invert
bigframes.pandas.DataFrame.__le__
__le__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is less than or equal to other, element-wise, using logical operator <=.
See more: bigframes.pandas.DataFrame.le
bigframes.pandas.DataFrame.__len__
__len__()Returns number of rows in the DataFrame, serves len operator.
See more: bigframes.pandas.DataFrame.len
bigframes.pandas.DataFrame.__lt__
__lt__(other) -> bigframes.dataframe.DataFrameCheck whether DataFrame is less than other, element-wise, using logical operator <.
See more: bigframes.pandas.DataFrame.lt
bigframes.pandas.DataFrame.__matmul__
__matmul__(other) -> bigframes.dataframe.DataFrameCompute the matrix multiplication between the DataFrame and other, using operator @.
See more: bigframes.pandas.DataFrame.matmul
bigframes.pandas.DataFrame.__mod__
__mod__(other)Get modulo of DataFrame with other, element-wise, using operator %.
See more: bigframes.pandas.DataFrame.mod
bigframes.pandas.DataFrame.__mul__
__mul__(other)Get multiplication of DataFrame with other, element-wise, using operator *.
See more: bigframes.pandas.DataFrame.mul
bigframes.pandas.DataFrame.__ne__
__ne__(other) -> bigframes.dataframe.DataFrameCheck inequality of DataFrame and other, element-wise, using logical operator !=.
See more: bigframes.pandas.DataFrame.ne
bigframes.pandas.DataFrame.__or__
__or__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise OR of DataFrame and other, element-wise, using operator |.
See more: bigframes.pandas.DataFrame.or
bigframes.pandas.DataFrame.__pow__
__pow__(other)Get exponentiation of DataFrame with other, element-wise, using operator **.
See more: bigframes.pandas.DataFrame.pow
bigframes.pandas.DataFrame.__radd__
__radd__(other) -> bigframes.dataframe.DataFrameGet addition of other and DataFrame, element-wise (binary operator +).
See more: bigframes.pandas.DataFrame.radd
bigframes.pandas.DataFrame.__rand__
__rand__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise AND of DataFrame and other, element-wise, using operator &.
See more: bigframes.pandas.DataFrame.rand
bigframes.pandas.DataFrame.__repr__
__repr__() -> strConverts a DataFrame to a string.
See more: bigframes.pandas.DataFrame.repr
bigframes.pandas.DataFrame.__rfloordiv__
__rfloordiv__(other)Get integer divison of other by DataFrame.
See more: bigframes.pandas.DataFrame.rfloordiv
bigframes.pandas.DataFrame.__rmod__
__rmod__(other)Get integer divison of other by DataFrame.
See more: bigframes.pandas.DataFrame.rmod
bigframes.pandas.DataFrame.__rmul__
__rmul__(other)Get multiplication of DataFrame with other, element-wise, using operator *.
See more: bigframes.pandas.DataFrame.rmul
bigframes.pandas.DataFrame.__ror__
__ror__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise OR of DataFrame and other, element-wise, using operator |.
See more: bigframes.pandas.DataFrame.ror
bigframes.pandas.DataFrame.__rpow__
__rpow__(other)Get exponentiation of other with DataFrame, element-wise, using operator **.
See more: bigframes.pandas.DataFrame.rpow
bigframes.pandas.DataFrame.__rsub__
__rsub__(other)Get subtraction of DataFrame from other, element-wise, using operator -.
See more: bigframes.pandas.DataFrame.rsub
bigframes.pandas.DataFrame.__rtruediv__
__rtruediv__(other)Get division of other by DataFrame, element-wise, using operator /.
See more: bigframes.pandas.DataFrame.rtruediv
bigframes.pandas.DataFrame.__rxor__
__rxor__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise XOR of DataFrame and other, element-wise, using operator ^.
See more: bigframes.pandas.DataFrame.rxor
bigframes.pandas.DataFrame.__setitem__
__setitem__( key: str | list[str] | pandas.Index, value: SingleItemValue | MultiItemValue )Modify or insert a column into the DataFrame.
See more: bigframes.pandas.DataFrame.setitem
bigframes.pandas.DataFrame.__sub__
__sub__(other)Get subtraction of other from DataFrame, element-wise, using operator -.
See more: bigframes.pandas.DataFrame.sub
bigframes.pandas.DataFrame.__truediv__
__truediv__(other)Get division of DataFrame by other, element-wise, using operator /.
See more: bigframes.pandas.DataFrame.truediv
bigframes.pandas.DataFrame.__xor__
__xor__( other: bool | int | bigframes.series.Series, ) -> bigframes.dataframe.DataFrameGet bitwise XOR of DataFrame and other, element-wise, using operator ^.
See more: bigframes.pandas.DataFrame.xor
bigframes.pandas.DataFrame.abs
abs() -> bigframes.dataframe.DataFrameReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.pandas.DataFrame.abs
bigframes.pandas.DataFrame.add
add( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet addition of DataFrame and other, element-wise (binary operator +).
See more: bigframes.pandas.DataFrame.add
bigframes.pandas.DataFrame.add_prefix
add_prefix( prefix: str, axis: int | str | None = None ) -> bigframes.dataframe.DataFramePrefix labels with string prefix.
See more: bigframes.pandas.DataFrame.add_prefix
bigframes.pandas.DataFrame.add_suffix
add_suffix( suffix: str, axis: int | str | None = None ) -> bigframes.dataframe.DataFrameSuffix labels with string suffix.
See more: bigframes.pandas.DataFrame.add_suffix
bigframes.pandas.DataFrame.agg
agg(func) -> bigframes.dataframe.DataFrame | bigframes.series.SeriesAggregate using one or more operations over columns.
See more: bigframes.pandas.DataFrame.agg
bigframes.pandas.DataFrame.aggregate
aggregate(func) -> bigframes.dataframe.DataFrame | bigframes.series.SeriesAggregate using one or more operations over columns.
See more: bigframes.pandas.DataFrame.aggregate
bigframes.pandas.DataFrame.align
align( other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], join: str = "outer", axis: typing.Optional[typing.Union[str, int]] = None, ) -> typing.Tuple[ typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], ]Align two objects on their axes with the specified join method.
See more: bigframes.pandas.DataFrame.align
bigframes.pandas.DataFrame.all
all( axis: typing.Union[str, int] = 0, *, bool_only: bool = False ) -> bigframes.series.SeriesReturn whether all elements are True, potentially over an axis.
See more: bigframes.pandas.DataFrame.all
bigframes.pandas.DataFrame.any
any( *, axis: typing.Union[str, int] = 0, bool_only: bool = False ) -> bigframes.series.SeriesReturn whether any element is True, potentially over an axis.
See more: bigframes.pandas.DataFrame.any
bigframes.pandas.DataFrame.apply
apply(func, *, axis=0, args: typing.Tuple = (), **kwargs)Apply a function along an axis of the DataFrame.
See more: bigframes.pandas.DataFrame.apply
bigframes.pandas.DataFrame.applymap
applymap( func, na_action: typing.Optional[str] = None ) -> bigframes.dataframe.DataFrameApply a function to a Dataframe elementwise.
See more: bigframes.pandas.DataFrame.applymap
bigframes.pandas.DataFrame.area
area( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, stacked: bool = True, **kwargs )Draw a stacked area plot.
See more: bigframes.pandas.DataFrame.area
bigframes.pandas.DataFrame.assign
assign(**kwargs) -> bigframes.dataframe.DataFrameAssign new columns to a DataFrame.
See more: bigframes.pandas.DataFrame.assign
bigframes.pandas.DataFrame.astype
astype( dtype: typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, type, dict[ str, typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, ], ], ], *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.dataframe.DataFrameCast a pandas object to a specified dtype dtype.
See more: bigframes.pandas.DataFrame.astype
bigframes.pandas.DataFrame.bar
bar( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a vertical bar plot.
See more: bigframes.pandas.DataFrame.bar
bigframes.pandas.DataFrame.bfill
bfill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrameFill NA/NaN values by using the next valid observation to fill the gap.
See more: bigframes.pandas.DataFrame.bfill
bigframes.pandas.DataFrame.cache
cache()Materializes the DataFrame to a temporary table.
See more: bigframes.pandas.DataFrame.cache
bigframes.pandas.DataFrame.combine
combine( other: bigframes.dataframe.DataFrame, func: typing.Callable[ [bigframes.series.Series, bigframes.series.Series], bigframes.series.Series ], fill_value=None, overwrite: bool = True, *, how: str = "outer" ) -> bigframes.dataframe.DataFramePerform column-wise combine with another DataFrame.
See more: bigframes.pandas.DataFrame.combine
bigframes.pandas.DataFrame.combine_first
combine_first(other: bigframes.dataframe.DataFrame)Update null elements with value in the same location in other.
See more: bigframes.pandas.DataFrame.combine_first
bigframes.pandas.DataFrame.copy
copy() -> bigframes.dataframe.DataFrameMake a copy of this object's indices and data.
See more: bigframes.pandas.DataFrame.copy
bigframes.pandas.DataFrame.corr
corr( method="pearson", min_periods=None, numeric_only=False ) -> bigframes.dataframe.DataFrameCompute pairwise correlation of columns, excluding NA/null values.
See more: bigframes.pandas.DataFrame.corr
bigframes.pandas.DataFrame.corrwith
corrwith( other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], *, numeric_only: bool = False )Compute pairwise correlation.
See more: bigframes.pandas.DataFrame.corrwith
bigframes.pandas.DataFrame.count
count(*, numeric_only: bool = False) -> bigframes.series.SeriesCount non-NA cells for each column.
See more: bigframes.pandas.DataFrame.count
bigframes.pandas.DataFrame.cov
cov(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrameCompute pairwise covariance of columns, excluding NA/null values.
See more: bigframes.pandas.DataFrame.cov
bigframes.pandas.DataFrame.cummax
cummax() -> bigframes.dataframe.DataFrameReturn cumulative maximum over columns.
See more: bigframes.pandas.DataFrame.cummax
bigframes.pandas.DataFrame.cummin
cummin() -> bigframes.dataframe.DataFrameReturn cumulative minimum over columns.
See more: bigframes.pandas.DataFrame.cummin
bigframes.pandas.DataFrame.cumprod
cumprod() -> bigframes.dataframe.DataFrameReturn cumulative product over columns.
See more: bigframes.pandas.DataFrame.cumprod
bigframes.pandas.DataFrame.cumsum
cumsum()Return cumulative sum over columns.
See more: bigframes.pandas.DataFrame.cumsum
bigframes.pandas.DataFrame.describe
describe( include: typing.Union[None, typing.Literal["all"]] = None, ) -> bigframes.dataframe.DataFrameGenerate descriptive statistics.
See more: bigframes.pandas.DataFrame.describe
bigframes.pandas.DataFrame.diff
diff(periods: int = 1) -> bigframes.dataframe.DataFrameFirst discrete difference of element.
See more: bigframes.pandas.DataFrame.diff
bigframes.pandas.DataFrame.div
div( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.pandas.DataFrame.div
bigframes.pandas.DataFrame.divide
divide( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.pandas.DataFrame.divide
bigframes.pandas.DataFrame.dot
dot(other: _DataFrameOrSeries) -> _DataFrameOrSeriesCompute the matrix multiplication between the DataFrame and other.
See more: bigframes.pandas.DataFrame.dot
bigframes.pandas.DataFrame.drop
Drop specified labels from columns.
See more: bigframes.pandas.DataFrame.drop
bigframes.pandas.DataFrame.drop_duplicates
drop_duplicates( subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, *, keep: str = "first" ) -> bigframes.dataframe.DataFrameReturn DataFrame with duplicate rows removed.
bigframes.pandas.DataFrame.droplevel
droplevel( level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], axis: int | str = 0, )Return DataFrame with requested index / column level(s) removed.
See more: bigframes.pandas.DataFrame.droplevel
bigframes.pandas.DataFrame.dropna
dropna( *, axis: int | str = 0, how: str = "any", thresh: typing.Optional[int] = None, subset: typing.Union[ None, typing.Hashable, typing.Sequence[typing.Hashable] ] = None, inplace: bool = False, ignore_index=False ) -> bigframes.dataframe.DataFrameRemove missing values.
See more: bigframes.pandas.DataFrame.dropna
bigframes.pandas.DataFrame.duplicated
duplicated(subset=None, keep: str = "first") -> bigframes.series.SeriesReturn boolean Series denoting duplicate rows.
See more: bigframes.pandas.DataFrame.duplicated
bigframes.pandas.DataFrame.eq
eq(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet equal to of DataFrame and other, element-wise (binary operator eq).
See more: bigframes.pandas.DataFrame.eq
bigframes.pandas.DataFrame.equals
equals( other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> boolTest whether two objects contain the same elements.
See more: bigframes.pandas.DataFrame.equals
bigframes.pandas.DataFrame.eval
eval(expr: str) -> bigframes.dataframe.DataFrameEvaluate a string describing operations on DataFrame columns.
See more: bigframes.pandas.DataFrame.eval
bigframes.pandas.DataFrame.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvide expanding window calculations.
See more: bigframes.pandas.DataFrame.expanding
bigframes.pandas.DataFrame.explode
explode( column: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], *, ignore_index: typing.Optional[bool] = False ) -> bigframes.dataframe.DataFrameTransform each element of an array to a row, replicating index values.
See more: bigframes.pandas.DataFrame.explode
bigframes.pandas.DataFrame.ffill
ffill(*, limit: typing.Optional[int] = None) -> bigframes.dataframe.DataFrameFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.pandas.DataFrame.ffill
bigframes.pandas.DataFrame.fillna
fillna(value=None) -> bigframes.dataframe.DataFrameFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.pandas.DataFrame.fillna
bigframes.pandas.DataFrame.filter
filter( items: typing.Optional[typing.Iterable] = None, like: typing.Optional[str] = None, regex: typing.Optional[str] = None, axis: int | str | None = None, ) -> bigframes.dataframe.DataFrameSubset the dataframe rows or columns according to the specified index labels.
See more: bigframes.pandas.DataFrame.filter
bigframes.pandas.DataFrame.first_valid_index
first_valid_index()API documentation for first_valid_index method.
bigframes.pandas.DataFrame.floordiv
floordiv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet integer division of DataFrame and other, element-wise (binary operator //).
See more: bigframes.pandas.DataFrame.floordiv
bigframes.pandas.DataFrame.from_dict
from_dict( data: dict, orient: str = "columns", dtype=None, columns=None ) -> bigframes.dataframe.DataFrameConstruct DataFrame from dict of array-like or dicts.
See more: bigframes.pandas.DataFrame.from_dict
bigframes.pandas.DataFrame.from_records
from_records( data, index=None, exclude=None, columns=None, coerce_float: bool = False, nrows: typing.Optional[int] = None, ) -> bigframes.dataframe.DataFrameConvert structured or record ndarray to DataFrame.
See more: bigframes.pandas.DataFrame.from_records
bigframes.pandas.DataFrame.ge
ge(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'greater than or equal to' of DataFrame and other, element-wise (binary operator >=).
See more: bigframes.pandas.DataFrame.ge
bigframes.pandas.DataFrame.groupby
groupby( by: typing.Optional[ typing.Union[ typing.Hashable, bigframes.series.Series, typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]], ] ] = None, *, level: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, as_index: bool = True, dropna: bool = True ) -> bigframes.core.groupby.dataframe_group_by.DataFrameGroupByGroup DataFrame by columns.
See more: bigframes.pandas.DataFrame.groupby
bigframes.pandas.DataFrame.gt
gt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'greater than' of DataFrame and other, element-wise (binary operator >).
See more: bigframes.pandas.DataFrame.gt
bigframes.pandas.DataFrame.head
head(n: int = 5) -> bigframes.dataframe.DataFrameReturn the first n rows.
See more: bigframes.pandas.DataFrame.head
bigframes.pandas.DataFrame.hist
hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)Draw one histogram of the DataFrame’s columns.
See more: bigframes.pandas.DataFrame.hist
bigframes.pandas.DataFrame.idxmax
idxmax() -> bigframes.series.SeriesReturn index of first occurrence of maximum over columns.
See more: bigframes.pandas.DataFrame.idxmax
bigframes.pandas.DataFrame.idxmin
idxmin() -> bigframes.series.SeriesReturn index of first occurrence of minimum over columns.
See more: bigframes.pandas.DataFrame.idxmin
bigframes.pandas.DataFrame.info
info( verbose: typing.Optional[bool] = None, buf=None, max_cols: typing.Optional[int] = None, memory_usage: typing.Optional[bool] = None, show_counts: typing.Optional[bool] = None, )Print a concise summary of a DataFrame.
See more: bigframes.pandas.DataFrame.info
bigframes.pandas.DataFrame.insert
insert( loc: int, column: blocks.Label, value: SingleItemValue, allow_duplicates: bool = False, )Insert column into DataFrame at specified location.
See more: bigframes.pandas.DataFrame.insert
bigframes.pandas.DataFrame.interpolate
interpolate(method: str = "linear") -> bigframes.dataframe.DataFrameFill NA (NULL in BigQuery) values using an interpolation method.
See more: bigframes.pandas.DataFrame.interpolate
bigframes.pandas.DataFrame.isin
isin(values) -> bigframes.dataframe.DataFrameWhether each element in the DataFrame is contained in values.
See more: bigframes.pandas.DataFrame.isin
bigframes.pandas.DataFrame.isna
isna() -> bigframes.dataframe.DataFrameDetect missing (NULL) values.
See more: bigframes.pandas.DataFrame.isna
bigframes.pandas.DataFrame.isnull
isnull() -> bigframes.dataframe.DataFrameDetect missing (NULL) values.
See more: bigframes.pandas.DataFrame.isnull
bigframes.pandas.DataFrame.items
items()Iterate over (column name, Series) pairs.
See more: bigframes.pandas.DataFrame.items
bigframes.pandas.DataFrame.iterrows
iterrows() -> typing.Iterable[tuple[typing.Any, pandas.core.series.Series]]Iterate over DataFrame rows as (index, Series) pairs.
See more: bigframes.pandas.DataFrame.iterrows
bigframes.pandas.DataFrame.itertuples
itertuples( index: bool = True, name: typing.Optional[str] = "Pandas" ) -> typing.Iterable[tuple[typing.Any, ...]]Iterate over DataFrame rows as namedtuples.
See more: bigframes.pandas.DataFrame.itertuples
bigframes.pandas.DataFrame.join
join( other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], on: typing.Optional[str] = None, how: str = "left", lsuffix: str = "", rsuffix: str = "", ) -> bigframes.dataframe.DataFrameJoin columns of another DataFrame.
See more: bigframes.pandas.DataFrame.join
bigframes.pandas.DataFrame.keys
keys() -> pandas.core.indexes.base.IndexGet the 'info axis'.
See more: bigframes.pandas.DataFrame.keys
bigframes.pandas.DataFrame.kurt
kurt(*, numeric_only: bool = False)Return unbiased kurtosis over columns.
See more: bigframes.pandas.DataFrame.kurt
bigframes.pandas.DataFrame.kurtosis
kurtosis(*, numeric_only: bool = False)Return unbiased kurtosis over columns.
See more: bigframes.pandas.DataFrame.kurtosis
bigframes.pandas.DataFrame.le
le(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'less than or equal to' of dataframe and other, element-wise (binary operator <=).
See more: bigframes.pandas.DataFrame.le
bigframes.pandas.DataFrame.line
line( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Plot Series or DataFrame as lines.
See more: bigframes.pandas.DataFrame.line
bigframes.pandas.DataFrame.lt
lt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet 'less than' of DataFrame and other, element-wise (binary operator <).
See more: bigframes.pandas.DataFrame.lt
bigframes.pandas.DataFrame.map
map(func, na_action: typing.Optional[str] = None) -> bigframes.dataframe.DataFrameApply a function to a Dataframe elementwise.
See more: bigframes.pandas.DataFrame.map
bigframes.pandas.DataFrame.mask
mask(cond, other=None)Replace values where the condition is False.
See more: bigframes.pandas.DataFrame.mask
bigframes.pandas.DataFrame.max
max( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the maximum of the values over the requested axis.
See more: bigframes.pandas.DataFrame.max
bigframes.pandas.DataFrame.mean
mean( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the mean of the values over the requested axis.
See more: bigframes.pandas.DataFrame.mean
bigframes.pandas.DataFrame.median
median( *, numeric_only: bool = False, exact: bool = True ) -> bigframes.series.SeriesReturn the median of the values over colunms.
See more: bigframes.pandas.DataFrame.median
bigframes.pandas.DataFrame.melt
melt( id_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None, value_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None, var_name: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, value_name: typing.Hashable = "value", )Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
See more: bigframes.pandas.DataFrame.melt
bigframes.pandas.DataFrame.memory_usage
memory_usage(index: bool = True)Return the memory usage of each column in bytes.
See more: bigframes.pandas.DataFrame.memory_usage
bigframes.pandas.DataFrame.merge
merge( right: bigframes.dataframe.DataFrame, how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner", on: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, *, left_on: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, right_on: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, left_index: bool = False, right_index: bool = False, sort: bool = False, suffixes: tuple[str, str] = ("_x", "_y") ) -> bigframes.dataframe.DataFrameMerge DataFrame objects with a database-style join.
See more: bigframes.pandas.DataFrame.merge
bigframes.pandas.DataFrame.min
min( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the minimum of the values over the requested axis.
See more: bigframes.pandas.DataFrame.min
bigframes.pandas.DataFrame.mod
mod( other: int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet modulo of DataFrame and other, element-wise (binary operator %).
See more: bigframes.pandas.DataFrame.mod
bigframes.pandas.DataFrame.mul
mul( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet multiplication of DataFrame and other, element-wise (binary operator *).
See more: bigframes.pandas.DataFrame.mul
bigframes.pandas.DataFrame.multiply
multiply( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet multiplication of DataFrame and other, element-wise (binary operator *).
See more: bigframes.pandas.DataFrame.multiply
bigframes.pandas.DataFrame.ne
ne(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrameGet not equal to of DataFrame and other, element-wise (binary operator ne).
See more: bigframes.pandas.DataFrame.ne
bigframes.pandas.DataFrame.nlargest
nlargest( n: int, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], keep: str = "first", ) -> bigframes.dataframe.DataFrameReturn the first n rows ordered by columns in descending order.
See more: bigframes.pandas.DataFrame.nlargest
bigframes.pandas.DataFrame.notna
notna() -> bigframes.dataframe.DataFrameDetect existing (non-missing) values.
See more: bigframes.pandas.DataFrame.notna
bigframes.pandas.DataFrame.notnull
notnull() -> bigframes.dataframe.DataFrameDetect existing (non-missing) values.
See more: bigframes.pandas.DataFrame.notnull
bigframes.pandas.DataFrame.nsmallest
nsmallest( n: int, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], keep: str = "first", ) -> bigframes.dataframe.DataFrameReturn the first n rows ordered by columns in ascending order.
See more: bigframes.pandas.DataFrame.nsmallest
bigframes.pandas.DataFrame.nunique
nunique() -> bigframes.series.SeriesCount number of distinct elements in each column.
See more: bigframes.pandas.DataFrame.nunique
bigframes.pandas.DataFrame.pct_change
pct_change(periods: int = 1) -> bigframes.dataframe.DataFrameFractional change between the current and a prior element.
See more: bigframes.pandas.DataFrame.pct_change
bigframes.pandas.DataFrame.peek
peek( n: int = 5, *, force: bool = True, allow_large_results=None ) -> pandas.core.frame.DataFramePreview n arbitrary rows from the dataframe.
See more: bigframes.pandas.DataFrame.peek
bigframes.pandas.DataFrame.pivot
pivot( *, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], index: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, values: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None ) -> bigframes.dataframe.DataFrameReturn reshaped DataFrame organized by given index / column values.
See more: bigframes.pandas.DataFrame.pivot
bigframes.pandas.DataFrame.pivot_table
pivot_table( values: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, index: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, aggfunc: str = "mean", ) -> bigframes.dataframe.DataFrameCreate a spreadsheet-style pivot table as a DataFrame.
See more: bigframes.pandas.DataFrame.pivot_table
bigframes.pandas.DataFrame.pow
pow( other: int | bigframes.series.Series, axis: str | int = "columns" ) -> bigframes.dataframe.DataFrameGet Exponential power of dataframe and other, element-wise (binary operator **).
See more: bigframes.pandas.DataFrame.pow
bigframes.pandas.DataFrame.prod
prod( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the product of the values over the requested axis.
See more: bigframes.pandas.DataFrame.prod
bigframes.pandas.DataFrame.product
product( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the product of the values over the requested axis.
See more: bigframes.pandas.DataFrame.product
bigframes.pandas.DataFrame.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False )Return values at the given quantile over requested axis.
See more: bigframes.pandas.DataFrame.quantile
bigframes.pandas.DataFrame.query
query(expr: str) -> bigframes.dataframe.DataFrameQuery the columns of a DataFrame with a boolean expression.
See more: bigframes.pandas.DataFrame.query
bigframes.pandas.DataFrame.radd
radd( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet addition of DataFrame and other, element-wise (binary operator +).
See more: bigframes.pandas.DataFrame.radd
bigframes.pandas.DataFrame.rank
rank( axis=0, method: str = "average", numeric_only=False, na_option: str = "keep", ascending=True, pct: bool = False, ) -> bigframes.dataframe.DataFrameCompute numerical data ranks (1 through n) along axis.
See more: bigframes.pandas.DataFrame.rank
bigframes.pandas.DataFrame.rdiv
rdiv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.pandas.DataFrame.rdiv
bigframes.pandas.DataFrame.reindex
reindex( labels=None, *, index=None, columns=None, axis: typing.Optional[typing.Union[str, int]] = None, validate: typing.Optional[bool] = None )Conform DataFrame to new index with optional filling logic.
See more: bigframes.pandas.DataFrame.reindex
bigframes.pandas.DataFrame.reindex_like
reindex_like( other: bigframes.dataframe.DataFrame, *, validate: typing.Optional[bool] = None )Return an object with matching indices as other object.
See more: bigframes.pandas.DataFrame.reindex_like
bigframes.pandas.DataFrame.rename
Rename columns.
See more: bigframes.pandas.DataFrame.rename
bigframes.pandas.DataFrame.rename_axis
Set the name of the axis for the index.
See more: bigframes.pandas.DataFrame.rename_axis
bigframes.pandas.DataFrame.reorder_levels
reorder_levels( order: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], axis: int | str = 0, )Rearrange index levels using input order.
bigframes.pandas.DataFrame.replace
replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)Replace values given in to_replace with value.
See more: bigframes.pandas.DataFrame.replace
bigframes.pandas.DataFrame.resample
resample( rule: str, *, closed: typing.Optional[typing.Literal["right", "left"]] = None, label: typing.Optional[typing.Literal["right", "left"]] = None, on: typing.Hashable = None, level: typing.Optional[ typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] ] = None, origin: typing.Union[ pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, numpy.datetime64, int, float, str, typing.Literal["epoch", "start", "start_day", "end", "end_day"], ] = "start_day" ) -> bigframes.core.groupby.dataframe_group_by.DataFrameGroupByResample time-series data.
See more: bigframes.pandas.DataFrame.resample
bigframes.pandas.DataFrame.reset_index
Reset the index.
See more: bigframes.pandas.DataFrame.reset_index
bigframes.pandas.DataFrame.rfloordiv
rfloordiv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet integer division of DataFrame and other, element-wise (binary operator //).
See more: bigframes.pandas.DataFrame.rfloordiv
bigframes.pandas.DataFrame.rmod
rmod( other: int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet modulo of DataFrame and other, element-wise (binary operator %).
See more: bigframes.pandas.DataFrame.rmod
bigframes.pandas.DataFrame.rmul
rmul( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet multiplication of DataFrame and other, element-wise (binary operator *).
See more: bigframes.pandas.DataFrame.rmul
bigframes.pandas.DataFrame.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods=None, on: str | None = None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowProvide rolling window calculations.
See more: bigframes.pandas.DataFrame.rolling
bigframes.pandas.DataFrame.round
round( decimals: typing.Union[int, dict[typing.Hashable, int]] = 0, ) -> bigframes.dataframe.DataFrameRound a DataFrame to a variable number of decimal places.
See more: bigframes.pandas.DataFrame.round
bigframes.pandas.DataFrame.rpow
rpow( other: int | bigframes.series.Series, axis: str | int = "columns" ) -> bigframes.dataframe.DataFrameGet Exponential power of dataframe and other, element-wise (binary operator rpow).
See more: bigframes.pandas.DataFrame.rpow
bigframes.pandas.DataFrame.rsub
rsub( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet subtraction of DataFrame and other, element-wise (binary operator -).
See more: bigframes.pandas.DataFrame.rsub
bigframes.pandas.DataFrame.rtruediv
rtruediv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.pandas.DataFrame.rtruediv
bigframes.pandas.DataFrame.sample
sample( n: typing.Optional[int] = None, frac: typing.Optional[float] = None, *, random_state: typing.Optional[int] = None, sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random" ) -> bigframes.dataframe.DataFrameReturn a random sample of items from an axis of object.
See more: bigframes.pandas.DataFrame.sample
bigframes.pandas.DataFrame.scatter
scatter( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, **kwargs )Create a scatter plot with varying marker point size and color.
See more: bigframes.pandas.DataFrame.scatter
bigframes.pandas.DataFrame.select_dtypes
select_dtypes(include=None, exclude=None) -> bigframes.dataframe.DataFrameReturn a subset of the DataFrame's columns based on the column dtypes.
See more: bigframes.pandas.DataFrame.select_dtypes
bigframes.pandas.DataFrame.set_index
set_index( keys: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], append: bool = False, drop: bool = True, ) -> bigframes.dataframe.DataFrameSet the DataFrame index using existing columns.
See more: bigframes.pandas.DataFrame.set_index
bigframes.pandas.DataFrame.shift
shift(periods: int = 1) -> bigframes.dataframe.DataFrameShift index by desired number of periods.
See more: bigframes.pandas.DataFrame.shift
bigframes.pandas.DataFrame.skew
skew(*, numeric_only: bool = False)Return unbiased skew over columns.
See more: bigframes.pandas.DataFrame.skew
bigframes.pandas.DataFrame.sort_index
Sort object by labels (along an axis).
See more: bigframes.pandas.DataFrame.sort_index
bigframes.pandas.DataFrame.sort_values
Sort by the values along row axis.
See more: bigframes.pandas.DataFrame.sort_values
bigframes.pandas.DataFrame.stack
stack(level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1)Stack the prescribed level(s) from columns to index.
See more: bigframes.pandas.DataFrame.stack
bigframes.pandas.DataFrame.std
std( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn sample standard deviation over columns.
See more: bigframes.pandas.DataFrame.std
bigframes.pandas.DataFrame.sub
sub( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet subtraction of DataFrame and other, element-wise (binary operator -).
See more: bigframes.pandas.DataFrame.sub
bigframes.pandas.DataFrame.subtract
subtract( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet subtraction of DataFrame and other, element-wise (binary operator -).
See more: bigframes.pandas.DataFrame.subtract
bigframes.pandas.DataFrame.sum
sum( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn the sum of the values over the requested axis.
See more: bigframes.pandas.DataFrame.sum
bigframes.pandas.DataFrame.swaplevel
swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)Swap levels i and j in a MultiIndex.
See more: bigframes.pandas.DataFrame.swaplevel
bigframes.pandas.DataFrame.tail
tail(n: int = 5) -> bigframes.dataframe.DataFrameReturn the last n rows.
See more: bigframes.pandas.DataFrame.tail
bigframes.pandas.DataFrame.take
take( indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs ) -> bigframes.dataframe.DataFrameReturn the elements in the given positional indices along an axis.
See more: bigframes.pandas.DataFrame.take
bigframes.pandas.DataFrame.to_arrow
to_arrow( *, ordered: bool = True, allow_large_results: typing.Optional[bool] = None ) -> pyarrow.lib.TableWrite DataFrame to an Arrow table / record batch.
See more: bigframes.pandas.DataFrame.to_arrow
bigframes.pandas.DataFrame.to_csv
to_csv( path_or_buf=None, sep=",", *, header: bool = True, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Write object to a comma-separated values (csv) file on Cloud Storage.
See more: bigframes.pandas.DataFrame.to_csv
bigframes.pandas.DataFrame.to_dict
to_dict( orient: typing.Literal[ "dict", "list", "series", "split", "tight", "records", "index" ] = "dict", into: type[dict] = dict, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> dict | list[dict]Convert the DataFrame to a dictionary.
See more: bigframes.pandas.DataFrame.to_dict
bigframes.pandas.DataFrame.to_excel
to_excel( excel_writer, sheet_name: str = "Sheet1", *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> NoneWrite DataFrame to an Excel sheet.
See more: bigframes.pandas.DataFrame.to_excel
bigframes.pandas.DataFrame.to_gbq
to_gbq( destination_table: typing.Optional[str] = None, *, if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = None, index: bool = True, ordering_id: typing.Optional[str] = None, clustering_columns: typing.Union[ pandas.core.indexes.base.Index, typing.Iterable[typing.Hashable] ] = (), labels: dict[str, str] = {} ) -> strWrite a DataFrame to a BigQuery table.
See more: bigframes.pandas.DataFrame.to_gbq
bigframes.pandas.DataFrame.to_html
to_html( buf=None, columns: typing.Optional[typing.Sequence[str]] = None, col_space=None, header: bool = True, index: bool = True, na_rep: str = "NaN", formatters=None, float_format=None, sparsify: bool | None = None, index_names: bool = True, justify: str | None = None, max_rows: int | None = None, max_cols: int | None = None, show_dimensions: bool = False, decimal: str = ".", bold_rows: bool = True, classes: str | list | tuple | None = None, escape: bool = True, notebook: bool = False, border: int | None = None, table_id: str | None = None, render_links: bool = False, encoding: str | None = None, *, allow_large_results: bool | None = None ) -> strRender a DataFrame as an HTML table.
See more: bigframes.pandas.DataFrame.to_html
bigframes.pandas.DataFrame.to_json
to_json( path_or_buf=None, orient: typing.Optional[ typing.Literal["split", "records", "index", "columns", "values", "table"] ] = None, *, lines: bool = False, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Convert the object to a JSON string, written to Cloud Storage.
See more: bigframes.pandas.DataFrame.to_json
bigframes.pandas.DataFrame.to_latex
to_latex( buf=None, columns: typing.Optional[typing.Sequence] = None, header: typing.Union[bool, typing.Sequence[str]] = True, index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> str | NoneRender object to a LaTeX tabular, longtable, or nested table.
See more: bigframes.pandas.DataFrame.to_latex
bigframes.pandas.DataFrame.to_markdown
to_markdown( buf=None, mode: str = "wt", index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> str | NonePrint DataFrame in Markdown-friendly format.
See more: bigframes.pandas.DataFrame.to_markdown
bigframes.pandas.DataFrame.to_numpy
to_numpy( dtype=None, copy=False, na_value=_NoDefault.no_default, *, allow_large_results=None, **kwargs ) -> numpy.ndarrayConvert the DataFrame to a NumPy array.
See more: bigframes.pandas.DataFrame.to_numpy
bigframes.pandas.DataFrame.to_orc
to_orc(path=None, *, allow_large_results=None, **kwargs) -> bytes | NoneWrite a DataFrame to the ORC format.
See more: bigframes.pandas.DataFrame.to_orc
bigframes.pandas.DataFrame.to_pandas
Write DataFrame to pandas DataFrame.
See more: bigframes.pandas.DataFrame.to_pandas
bigframes.pandas.DataFrame.to_pandas_batches
to_pandas_batches( page_size: typing.Optional[int] = None, max_results: typing.Optional[int] = None, *, allow_large_results: typing.Optional[bool] = None ) -> typing.Iterable[pandas.core.frame.DataFrame]Stream DataFrame results to an iterable of pandas DataFrame.
bigframes.pandas.DataFrame.to_parquet
to_parquet( path=None, *, compression: typing.Optional[typing.Literal["snappy", "gzip"]] = "snappy", index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[bytes]Write a DataFrame to the binary Parquet format.
See more: bigframes.pandas.DataFrame.to_parquet
bigframes.pandas.DataFrame.to_pickle
to_pickle(path, *, allow_large_results=None, **kwargs) -> NonePickle (serialize) object to file.
See more: bigframes.pandas.DataFrame.to_pickle
bigframes.pandas.DataFrame.to_records
to_records( index: bool = True, column_dtypes=None, index_dtypes=None, *, allow_large_results=None ) -> numpy.rec.recarrayConvert DataFrame to a NumPy record array.
See more: bigframes.pandas.DataFrame.to_records
bigframes.pandas.DataFrame.to_string
to_string( buf=None, columns: typing.Optional[typing.Sequence[str]] = None, col_space=None, header: typing.Union[bool, typing.Sequence[str]] = True, index: bool = True, na_rep: str = "NaN", formatters=None, float_format=None, sparsify: bool | None = None, index_names: bool = True, justify: str | None = None, max_rows: int | None = None, max_cols: int | None = None, show_dimensions: bool = False, decimal: str = ".", line_width: int | None = None, min_rows: int | None = None, max_colwidth: int | None = None, encoding: str | None = None, *, allow_large_results: typing.Optional[bool] = None ) -> str | NoneRender a DataFrame to a console-friendly tabular output.
See more: bigframes.pandas.DataFrame.to_string
bigframes.pandas.DataFrame.transpose
transpose() -> bigframes.dataframe.DataFrameTranspose index and columns.
See more: bigframes.pandas.DataFrame.transpose
bigframes.pandas.DataFrame.truediv
truediv( other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame, axis: str | int = "columns", ) -> bigframes.dataframe.DataFrameGet floating division of DataFrame and other, element-wise (binary operator /).
See more: bigframes.pandas.DataFrame.truediv
bigframes.pandas.DataFrame.unstack
unstack( level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1, )Pivot a level of the (necessarily hierarchical) index labels.
See more: bigframes.pandas.DataFrame.unstack
bigframes.pandas.DataFrame.update
update(other, join: str = "left", overwrite=True, filter_func=None)Modify in place using non-NA values from another DataFrame.
See more: bigframes.pandas.DataFrame.update
bigframes.pandas.DataFrame.value_counts
value_counts( subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True, )Return a Series containing counts of unique rows in the DataFrame.
See more: bigframes.pandas.DataFrame.value_counts
bigframes.pandas.DataFrame.var
var( axis: typing.Union[str, int] = 0, *, numeric_only: bool = False ) -> bigframes.series.SeriesReturn unbiased variance over requested axis.
See more: bigframes.pandas.DataFrame.var
bigframes.pandas.DataFrame.where
where(cond, other=None)Replace values where the condition is False.
See more: bigframes.pandas.DataFrame.where
bigframes.pandas.Index.__setitem__
__setitem__(key, value) -> NoneIndex objects are immutable.
See more: bigframes.pandas.Index.setitem
bigframes.pandas.Index.all
all() -> boolReturn whether all elements are Truthy.
See more: bigframes.pandas.Index.all
bigframes.pandas.Index.any
any() -> boolReturn whether any element is Truthy.
See more: bigframes.pandas.Index.any
bigframes.pandas.Index.argmax
argmax() -> intReturn int position of the largest value in the Series.
See more: bigframes.pandas.Index.argmax
bigframes.pandas.Index.argmin
argmin() -> intReturn int position of the smallest value in the series.
See more: bigframes.pandas.Index.argmin
bigframes.pandas.Index.astype
astype( dtype, *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.core.indexes.base.IndexCreate an Index with values cast to dtypes.
See more: bigframes.pandas.Index.astype
bigframes.pandas.Index.copy
copy(name: typing.Optional[typing.Hashable] = None)Make a copy of this object.
See more: bigframes.pandas.Index.copy
bigframes.pandas.Index.drop
drop(labels: typing.Any) -> bigframes.core.indexes.base.IndexMake new Index with passed list of labels deleted.
See more: bigframes.pandas.Index.drop
bigframes.pandas.Index.drop_duplicates
drop_duplicates(*, keep: __builtins__.str = "first") -> IndexReturn Index with duplicate values removed.
See more: bigframes.pandas.Index.drop_duplicates
bigframes.pandas.Index.dropna
dropna( how: typing.Literal["all", "any"] = "any", ) -> bigframes.core.indexes.base.IndexReturn Index without NA/NaN values.
See more: bigframes.pandas.Index.dropna
bigframes.pandas.Index.fillna
fillna(value=None) -> bigframes.core.indexes.base.IndexFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.pandas.Index.fillna
bigframes.pandas.Index.from_frame
from_frame( frame: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> bigframes.core.indexes.base.IndexMake a MultiIndex from a DataFrame.
See more: bigframes.pandas.Index.from_frame
bigframes.pandas.Index.get_level_values
get_level_values(level) -> bigframes.core.indexes.base.IndexReturn an Index of values for requested level.
See more: bigframes.pandas.Index.get_level_values
bigframes.pandas.Index.get_loc
get_loc(key) -> typing.Union[int, slice, bigframes.series.Series]Get integer location, slice or boolean mask for requested label.
See more: bigframes.pandas.Index.get_loc
bigframes.pandas.Index.isin
isin(values) -> bigframes.core.indexes.base.IndexReturn a boolean array where the index values are in values.
See more: bigframes.pandas.Index.isin
bigframes.pandas.Index.item
item()Return the first element of the underlying data as a Python scalar.
See more: bigframes.pandas.Index.item
bigframes.pandas.Index.max
max() -> typing.AnyReturn the maximum value of the Index.
See more: bigframes.pandas.Index.max
bigframes.pandas.Index.min
min() -> typing.AnyReturn the minimum value of the Index.
See more: bigframes.pandas.Index.min
bigframes.pandas.Index.nunique
nunique() -> intReturn number of unique elements in the object.
See more: bigframes.pandas.Index.nunique
bigframes.pandas.Index.rename
Alter Index or MultiIndex name.
See more: bigframes.pandas.Index.rename
bigframes.pandas.Index.sort_values
sort_values( *, inplace: bool = False, ascending: bool = True, na_position: __builtins__.str = "last" ) -> IndexReturn a sorted copy of the index.
See more: bigframes.pandas.Index.sort_values
bigframes.pandas.Index.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listAPI documentation for to_list method.
See more: bigframes.pandas.Index.to_list
bigframes.pandas.Index.to_numpy
to_numpy(dtype=None, *, allow_large_results=None, **kwargs) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
See more: bigframes.pandas.Index.to_numpy
bigframes.pandas.Index.to_pandas
Gets the Index as a pandas Index.
See more: bigframes.pandas.Index.to_pandas
bigframes.pandas.Index.to_series
to_series( index: typing.Optional[bigframes.core.indexes.base.Index] = None, name: typing.Optional[typing.Hashable] = None, ) -> bigframes.series.SeriesCreate a Series with both index and values equal to the index keys.
See more: bigframes.pandas.Index.to_series
bigframes.pandas.Index.transpose
transpose() -> bigframes.core.indexes.base.IndexReturn the transpose, which is by definition self.
See more: bigframes.pandas.Index.transpose
bigframes.pandas.Index.unique
unique( level: typing.Optional[typing.Union[typing.Hashable, int]] = None, ) -> bigframes.core.indexes.base.IndexReturns unique values in the index.
See more: bigframes.pandas.Index.unique
bigframes.pandas.Index.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
See more: bigframes.pandas.Index.value_counts
bigframes.pandas.MultiIndex.from_arrays
from_arrays( arrays, sortorder: int | None = None, names=None, *, session: Optional[bigframes.session.Session] = None ) -> MultiIndexConvert arrays to MultiIndex.
See more: bigframes.pandas.MultiIndex.from_arrays
bigframes.pandas.MultiIndex.from_tuples
from_tuples( tuples: Iterable[tuple[Hashable, ...]], sortorder: int | None = None, names: Sequence[Hashable] | Hashable | None = None, *, session: Optional[bigframes.session.Session] = None ) -> MultiIndexConvert list of tuples to MultiIndex.
See more: bigframes.pandas.MultiIndex.from_tuples
bigframes.pandas.NamedAgg
NamedAgg(column, aggfunc)Create new instance of NamedAgg(column, aggfunc).
See more: bigframes.pandas.NamedAgg
bigframes.pandas.Series.__abs__
__abs__() -> bigframes.series.SeriesReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.pandas.Series.abs
bigframes.pandas.Series.__add__
__add__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet addition of Series and other, element-wise, using operator +.
See more: bigframes.pandas.Series.add
bigframes.pandas.Series.__and__
__and__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise AND of Series and other, element-wise, using operator &.
See more: bigframes.pandas.Series.and
bigframes.pandas.Series.__array__
__array__(dtype=None, copy: typing.Optional[bool] = None) -> numpy.ndarrayReturns the values as NumPy array.
See more: bigframes.pandas.Series.array
bigframes.pandas.Series.__array_ufunc__
__array_ufunc__( ufunc: numpy.ufunc, method: str, *inputs, **kwargs ) -> bigframes.series.SeriesUsed to support numpy ufuncs.
See more: bigframes.pandas.Series.array_ufunc
bigframes.pandas.Series.__floordiv__
__floordiv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet integer division of Series by other, using arithmetic operator //.
See more: bigframes.pandas.Series.floordiv
bigframes.pandas.Series.__getitem__
__getitem__(indexer)Gets the specified index from the Series.
See more: bigframes.pandas.Series.getitem
bigframes.pandas.Series.__invert__
__invert__() -> bigframes.series.SeriesReturns the logical inversion (binary NOT) of the Series, element-wise using operator ````.
See more: bigframes.pandas.Series.invert
bigframes.pandas.Series.__len__
__len__()Returns number of values in the Series, serves len operator.
See more: bigframes.pandas.Series.len
bigframes.pandas.Series.__matmul__
__matmul__(other)Matrix multiplication using binary @ operator.
See more: bigframes.pandas.Series.matmul
bigframes.pandas.Series.__mod__
__mod__(other) -> bigframes.series.SeriesGet modulo of Series with other, element-wise, using operator %.
See more: bigframes.pandas.Series.mod
bigframes.pandas.Series.__mul__
__mul__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet multiplication of Series with other, element-wise, using operator *.
See more: bigframes.pandas.Series.mul
bigframes.pandas.Series.__or__
__or__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise XOR of Series and other, element-wise, using operator ^.
See more: bigframes.pandas.Series.or
bigframes.pandas.Series.__pow__
__pow__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet exponentiation of Series with other, element-wise, using operator **.
See more: bigframes.pandas.Series.pow
bigframes.pandas.Series.__radd__
__radd__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet addition of Series and other, element-wise, using operator +.
See more: bigframes.pandas.Series.radd
bigframes.pandas.Series.__rand__
__rand__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise AND of Series and other, element-wise, using operator &.
See more: bigframes.pandas.Series.rand
bigframes.pandas.Series.__rfloordiv__
__rfloordiv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet integer division of other by Series, using arithmetic operator //.
See more: bigframes.pandas.Series.rfloordiv
bigframes.pandas.Series.__rmatmul__
__rmatmul__(other)Matrix multiplication using binary @ operator.
See more: bigframes.pandas.Series.rmatmul
bigframes.pandas.Series.__rmod__
__rmod__(other) -> bigframes.series.SeriesGet modulo of other with Series, element-wise, using operator %.
See more: bigframes.pandas.Series.rmod
bigframes.pandas.Series.__rmul__
__rmul__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet multiplication of other with Series, element-wise, using operator *.
See more: bigframes.pandas.Series.rmul
bigframes.pandas.Series.__ror__
__ror__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise XOR of Series and other, element-wise, using operator ^.
See more: bigframes.pandas.Series.ror
bigframes.pandas.Series.__rpow__
__rpow__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet exponentiation of other with Series, element-wise, using operator **.
See more: bigframes.pandas.Series.rpow
bigframes.pandas.Series.__rsub__
__rsub__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet subtraction of Series from other, element-wise, using operator -.
See more: bigframes.pandas.Series.rsub
bigframes.pandas.Series.__rtruediv__
__rtruediv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet division of other by Series, element-wise, using operator /.
See more: bigframes.pandas.Series.rtruediv
bigframes.pandas.Series.__setitem__
__setitem__(key, value) -> NoneSet item using direct assignment, delegating to .loc indexer.
See more: bigframes.pandas.Series.setitem
bigframes.pandas.Series.__sub__
__sub__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet subtraction of other from Series, element-wise, using operator -.
See more: bigframes.pandas.Series.sub
bigframes.pandas.Series.__truediv__
__truediv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet division of Series by other, element-wise, using operator /.
See more: bigframes.pandas.Series.truediv
bigframes.pandas.Series.abs
abs() -> bigframes.series.SeriesReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.pandas.Series.abs
bigframes.pandas.Series.add
add( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn addition of Series and other, element-wise (binary operator add).
See more: bigframes.pandas.Series.add
bigframes.pandas.Series.add_prefix
add_prefix(prefix: str, axis: int | str | None = None) -> bigframes.series.SeriesPrefix labels with string prefix.
See more: bigframes.pandas.Series.add_prefix
bigframes.pandas.Series.add_suffix
add_suffix(suffix: str, axis: int | str | None = None) -> bigframes.series.SeriesSuffix labels with string suffix.
See more: bigframes.pandas.Series.add_suffix
bigframes.pandas.Series.agg
agg( func: typing.Union[str, typing.Sequence[str]], ) -> typing.Union[typing.Any, bigframes.series.Series]Aggregate using one or more operations over the specified axis.
See more: bigframes.pandas.Series.agg
bigframes.pandas.Series.aggregate
aggregate( func: typing.Union[str, typing.Sequence[str]], ) -> typing.Union[typing.Any, bigframes.series.Series]Aggregate using one or more operations over the specified axis.
See more: bigframes.pandas.Series.aggregate
bigframes.pandas.Series.all
all() -> boolReturn whether all elements are True, potentially over an axis.
See more: bigframes.pandas.Series.all
bigframes.pandas.Series.any
any() -> boolReturn whether any element is True, potentially over an axis.
See more: bigframes.pandas.Series.any
bigframes.pandas.Series.apply
apply( func, by_row: typing.Union[typing.Literal["compat"], bool] = "compat", *, args: typing.Tuple = () ) -> bigframes.series.SeriesInvoke function on values of a Series.
See more: bigframes.pandas.Series.apply
bigframes.pandas.Series.area
area( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, stacked: bool = True, **kwargs )Draw a stacked area plot.
See more: bigframes.pandas.Series.area
bigframes.pandas.Series.argmax
argmax() -> intReturn int position of the largest value in the series.
See more: bigframes.pandas.Series.argmax
bigframes.pandas.Series.argmin
argmin() -> intReturn int position of the smallest value in the Series.
See more: bigframes.pandas.Series.argmin
bigframes.pandas.Series.astype
astype( dtype: typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, ], *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.series.SeriesCast a pandas object to a specified dtype dtype.
See more: bigframes.pandas.Series.astype
bigframes.pandas.Series.autocorr
autocorr(lag: int = 1) -> floatCompute the lag-N autocorrelation.
See more: bigframes.pandas.Series.autocorr
bigframes.pandas.Series.bar
bar( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a vertical bar plot.
See more: bigframes.pandas.Series.bar
bigframes.pandas.Series.between
between(left, right, inclusive="both")Return boolean Series equivalent to left <= series <= right.
See more: bigframes.pandas.Series.between
bigframes.pandas.Series.bfill
bfill(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by using the next valid observation to fill the gap.
See more: bigframes.pandas.Series.bfill
bigframes.pandas.Series.cache
cache()Materializes the Series to a temporary table.
See more: bigframes.pandas.Series.cache
bigframes.pandas.Series.case_when
case_when(caselist) -> bigframes.series.SeriesReplace values where the conditions are True.
See more: bigframes.pandas.Series.case_when
bigframes.pandas.Series.clip
clip(lower=None, upper=None)Trim values at input threshold(s).
See more: bigframes.pandas.Series.clip
bigframes.pandas.Series.combine
combine(other, func) -> bigframes.series.SeriesCombine the Series with a Series or scalar according to func.
See more: bigframes.pandas.Series.combine
bigframes.pandas.Series.combine_first
combine_first(other: bigframes.series.Series) -> bigframes.series.SeriesUpdate null elements with value in the same location in 'other'.
See more: bigframes.pandas.Series.combine_first
bigframes.pandas.Series.copy
copy() -> bigframes.series.SeriesMake a copy of this object's indices and data.
See more: bigframes.pandas.Series.copy
bigframes.pandas.Series.corr
corr(other: bigframes.series.Series, method="pearson", min_periods=None) -> floatCompute the correlation with the other Series.
See more: bigframes.pandas.Series.corr
bigframes.pandas.Series.count
count() -> intReturn number of non-NA/null observations in the Series.
See more: bigframes.pandas.Series.count
bigframes.pandas.Series.cov
cov(other: bigframes.series.Series) -> floatCompute covariance with Series, excluding missing values.
See more: bigframes.pandas.Series.cov
bigframes.pandas.Series.cummax
cummax() -> bigframes.series.SeriesReturn cumulative maximum over a DataFrame or Series axis.
See more: bigframes.pandas.Series.cummax
bigframes.pandas.Series.cummin
cummin() -> bigframes.series.SeriesReturn cumulative minimum over a DataFrame or Series axis.
See more: bigframes.pandas.Series.cummin
bigframes.pandas.Series.cumprod
cumprod() -> bigframes.series.SeriesReturn cumulative product over a DataFrame or Series axis.
See more: bigframes.pandas.Series.cumprod
bigframes.pandas.Series.cumsum
cumsum() -> bigframes.series.SeriesReturn cumulative sum over a DataFrame or Series axis.
See more: bigframes.pandas.Series.cumsum
bigframes.pandas.Series.describe
describe() -> bigframes.series.SeriesGenerate descriptive statistics.
See more: bigframes.pandas.Series.describe
bigframes.pandas.Series.diff
diff(periods: int = 1) -> bigframes.series.SeriesFirst discrete difference of element.
See more: bigframes.pandas.Series.diff
bigframes.pandas.Series.div
div( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.pandas.Series.div
bigframes.pandas.Series.divide
divide( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.pandas.Series.divide
bigframes.pandas.Series.divmod
divmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]Return integer division and modulo of Series and other, element-wise (binary operator divmod).
See more: bigframes.pandas.Series.divmod
bigframes.pandas.Series.dot
dot(other)Compute the dot product between the Series and the columns of other.
See more: bigframes.pandas.Series.dot
bigframes.pandas.Series.drop
drop( labels: typing.Any = None, *, axis: typing.Union[int, str] = 0, index: typing.Any = None, columns: typing.Union[typing.Hashable, typing.Iterable[typing.Hashable]] = None, level: typing.Optional[typing.Union[str, int]] = None ) -> bigframes.series.SeriesReturn Series with specified index labels removed.
See more: bigframes.pandas.Series.drop
bigframes.pandas.Series.drop_duplicates
drop_duplicates(*, keep: str = "first") -> bigframes.series.SeriesReturn Series with duplicate values removed.
See more: bigframes.pandas.Series.drop_duplicates
bigframes.pandas.Series.droplevel
droplevel( level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]], axis: int | str = 0, )Return Series with requested index / column level(s) removed.
See more: bigframes.pandas.Series.droplevel
bigframes.pandas.Series.dropna
dropna( *, axis: int = 0, inplace: bool = False, how: typing.Optional[str] = None, ignore_index: bool = False ) -> bigframes.series.SeriesReturn a new Series with missing values removed.
See more: bigframes.pandas.Series.dropna
bigframes.pandas.Series.duplicated
duplicated(keep: str = "first") -> bigframes.series.SeriesIndicate duplicate Series values.
See more: bigframes.pandas.Series.duplicated
bigframes.pandas.Series.eq
eq(other: object) -> bigframes.series.SeriesReturn equal of Series and other, element-wise (binary operator eq).
See more: bigframes.pandas.Series.eq
bigframes.pandas.Series.equals
equals( other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> boolTest whether two objects contain the same elements.
See more: bigframes.pandas.Series.equals
bigframes.pandas.Series.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvide expanding window calculations.
See more: bigframes.pandas.Series.expanding
bigframes.pandas.Series.explode
explode(*, ignore_index: typing.Optional[bool] = False) -> bigframes.series.SeriesTransform each element of a list-like to a row.
See more: bigframes.pandas.Series.explode
bigframes.pandas.Series.ffill
ffill(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.pandas.Series.ffill
bigframes.pandas.Series.fillna
fillna(value=None) -> bigframes.series.SeriesFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.pandas.Series.fillna
bigframes.pandas.Series.filter
filter( items: typing.Optional[typing.Iterable] = None, like: typing.Optional[str] = None, regex: typing.Optional[str] = None, axis: typing.Optional[typing.Union[str, int]] = None, ) -> bigframes.series.SeriesSubset the dataframe rows or columns according to the specified index labels.
See more: bigframes.pandas.Series.filter
bigframes.pandas.Series.floordiv
floordiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn integer division of Series and other, element-wise (binary operator floordiv).
See more: bigframes.pandas.Series.floordiv
bigframes.pandas.Series.ge
ge(other) -> bigframes.series.SeriesGet 'greater than or equal to' of Series and other, element-wise (binary operator ge).
See more: bigframes.pandas.Series.ge
bigframes.pandas.Series.groupby
groupby( by: typing.Union[ typing.Hashable, bigframes.series.Series, typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]], ] = None, axis=0, level: typing.Optional[ typing.Union[int, str, typing.Sequence[int], typing.Sequence[str]] ] = None, as_index: bool = True, *, dropna: bool = True ) -> bigframes.core.groupby.series_group_by.SeriesGroupByGroup Series using a mapper or by a Series of columns.
See more: bigframes.pandas.Series.groupby
bigframes.pandas.Series.gt
gt(other) -> bigframes.series.SeriesReturn Greater than of series and other, element-wise (binary operator gt).
See more: bigframes.pandas.Series.gt
bigframes.pandas.Series.head
head(n: int = 5) -> bigframes.series.SeriesReturn the first n rows.
See more: bigframes.pandas.Series.head
bigframes.pandas.Series.hist
hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)Draw one histogram of the DataFrame’s columns.
See more: bigframes.pandas.Series.hist
bigframes.pandas.Series.idxmax
idxmax() -> typing.HashableReturn the row label of the maximum value.
See more: bigframes.pandas.Series.idxmax
bigframes.pandas.Series.idxmin
idxmin() -> typing.HashableReturn the row label of the minimum value.
See more: bigframes.pandas.Series.idxmin
bigframes.pandas.Series.interpolate
interpolate(method: str = "linear") -> bigframes.series.SeriesFill NaN values using an interpolation method.
See more: bigframes.pandas.Series.interpolate
bigframes.pandas.Series.isin
isin(values) -> bigframes.series.SeriesWhether elements in Series are contained in values.
See more: bigframes.pandas.Series.isin
bigframes.pandas.Series.isna
isna() -> bigframes.series.SeriesDetect missing (NULL) values.
See more: bigframes.pandas.Series.isna
bigframes.pandas.Series.isnull
isnull() -> bigframes.series.SeriesDetect missing (NULL) values.
See more: bigframes.pandas.Series.isnull
bigframes.pandas.Series.item
item()Return the first element of the underlying data as a Python scalar.
See more: bigframes.pandas.Series.item
bigframes.pandas.Series.items
items()Lazily iterate over (index, value) tuples.
See more: bigframes.pandas.Series.items
bigframes.pandas.Series.keys
keys() -> bigframes.core.indexes.base.IndexReturn alias for index.
See more: bigframes.pandas.Series.keys
bigframes.pandas.Series.kurt
kurt()Return unbiased kurtosis over requested axis.
See more: bigframes.pandas.Series.kurt
bigframes.pandas.Series.kurtosis
kurtosis()Return unbiased kurtosis over requested axis.
See more: bigframes.pandas.Series.kurtosis
bigframes.pandas.Series.le
le(other) -> bigframes.series.SeriesGet 'less than or equal to' of Series and other, element-wise (binary operator le).
See more: bigframes.pandas.Series.le
bigframes.pandas.Series.line
line( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Plot Series or DataFrame as lines.
See more: bigframes.pandas.Series.line
bigframes.pandas.Series.lt
lt(other) -> bigframes.series.SeriesGet 'less than' of Series and other, element-wise (binary operator lt).
See more: bigframes.pandas.Series.lt
bigframes.pandas.Series.map
map( arg: typing.Union[typing.Mapping, bigframes.series.Series, typing.Callable], na_action: typing.Optional[str] = None, *, verify_integrity: bool = False ) -> bigframes.series.SeriesMap values of Series according to an input mapping or function.
See more: bigframes.pandas.Series.map
bigframes.pandas.Series.mask
mask(cond, other=None) -> bigframes.series.SeriesReplace values where the condition is True.
See more: bigframes.pandas.Series.mask
bigframes.pandas.Series.max
max() -> typing.AnyReturn the maximum of the values over the requested axis.
See more: bigframes.pandas.Series.max
bigframes.pandas.Series.mean
mean() -> floatReturn the mean of the values over the requested axis.
See more: bigframes.pandas.Series.mean
bigframes.pandas.Series.median
median(*, exact: bool = True) -> floatReturn the median of the values over the requested axis.
See more: bigframes.pandas.Series.median
bigframes.pandas.Series.min
min() -> typing.AnyReturn the maximum of the values over the requested axis.
See more: bigframes.pandas.Series.min
bigframes.pandas.Series.mod
mod(other) -> bigframes.series.SeriesReturn modulo of Series and other, element-wise (binary operator mod).
See more: bigframes.pandas.Series.mod
bigframes.pandas.Series.mode
mode() -> bigframes.series.SeriesReturn the mode(s) of the Series.
See more: bigframes.pandas.Series.mode
bigframes.pandas.Series.mul
mul(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.pandas.Series.mul
bigframes.pandas.Series.multiply
multiply(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.pandas.Series.multiply
bigframes.pandas.Series.ne
ne(other: object) -> bigframes.series.SeriesReturn not equal of Series and other, element-wise (binary operator ne).
See more: bigframes.pandas.Series.ne
bigframes.pandas.Series.nlargest
nlargest(n: int = 5, keep: str = "first") -> bigframes.series.SeriesReturn the largest n elements.
See more: bigframes.pandas.Series.nlargest
bigframes.pandas.Series.notna
notna() -> bigframes.series.SeriesDetect existing (non-missing) values.
See more: bigframes.pandas.Series.notna
bigframes.pandas.Series.notnull
notnull() -> bigframes.series.SeriesDetect existing (non-missing) values.
See more: bigframes.pandas.Series.notnull
bigframes.pandas.Series.nsmallest
nsmallest(n: int = 5, keep: str = "first") -> bigframes.series.SeriesReturn the smallest n elements.
See more: bigframes.pandas.Series.nsmallest
bigframes.pandas.Series.nunique
nunique() -> intReturn number of unique elements in the object.
See more: bigframes.pandas.Series.nunique
bigframes.pandas.Series.pad
pad(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.pandas.Series.pad
bigframes.pandas.Series.pct_change
pct_change(periods: int = 1) -> bigframes.series.SeriesFractional change between the current and a prior element.
See more: bigframes.pandas.Series.pct_change
bigframes.pandas.Series.peek
peek( n: int = 5, *, force: bool = True, allow_large_results=None ) -> pandas.core.series.SeriesPreview n arbitrary elements from the series without guarantees about row selection or ordering.
See more: bigframes.pandas.Series.peek
bigframes.pandas.Series.pow
pow(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn Exponential power of series and other, element-wise (binary operator pow).
See more: bigframes.pandas.Series.pow
bigframes.pandas.Series.prod
prod() -> floatReturn the product of the values over the requested axis.
See more: bigframes.pandas.Series.prod
bigframes.pandas.Series.product
product() -> floatReturn the product of the values over the requested axis.
See more: bigframes.pandas.Series.product
bigframes.pandas.Series.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, ) -> typing.Union[bigframes.series.Series, float]Return value at the given quantile.
See more: bigframes.pandas.Series.quantile
bigframes.pandas.Series.radd
radd( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn addition of Series and other, element-wise (binary operator radd).
See more: bigframes.pandas.Series.radd
bigframes.pandas.Series.rank
rank( axis=0, method: str = "average", numeric_only=False, na_option: str = "keep", ascending: bool = True, pct: bool = False, ) -> bigframes.series.SeriesCompute numerical data ranks (1 through n) along axis.
See more: bigframes.pandas.Series.rank
bigframes.pandas.Series.rdiv
rdiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator rtruediv).
See more: bigframes.pandas.Series.rdiv
bigframes.pandas.Series.rdivmod
rdivmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]Return integer division and modulo of Series and other, element-wise (binary operator rdivmod).
See more: bigframes.pandas.Series.rdivmod
bigframes.pandas.Series.reindex
reindex(index=None, *, validate: typing.Optional[bool] = None)Conform Series to new index with optional filling logic.
See more: bigframes.pandas.Series.reindex
bigframes.pandas.Series.reindex_like
reindex_like( other: bigframes.series.Series, *, validate: typing.Optional[bool] = None )Return an object with matching indices as other object.
See more: bigframes.pandas.Series.reindex_like
bigframes.pandas.Series.rename
Alter Series index labels or name.
See more: bigframes.pandas.Series.rename
bigframes.pandas.Series.rename_axis
Set the name of the axis for the index or columns.
See more: bigframes.pandas.Series.rename_axis
bigframes.pandas.Series.reorder_levels
reorder_levels( order: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]], axis: int | str = 0, )Rearrange index levels using input order.
See more: bigframes.pandas.Series.reorder_levels
bigframes.pandas.Series.replace
replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)Replace values given in to_replace with value.
See more: bigframes.pandas.Series.replace
bigframes.pandas.Series.resample
resample( rule: str, *, closed: typing.Optional[typing.Literal["right", "left"]] = None, label: typing.Optional[typing.Literal["right", "left"]] = None, level: typing.Optional[ typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] ] = None, origin: typing.Union[ pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, numpy.datetime64, int, float, str, typing.Literal["epoch", "start", "start_day", "end", "end_day"], ] = "start_day" ) -> bigframes.core.groupby.series_group_by.SeriesGroupByResample time-series data.
See more: bigframes.pandas.Series.resample
bigframes.pandas.Series.reset_index
Generate a new DataFrame or Series with the index reset.
See more: bigframes.pandas.Series.reset_index
bigframes.pandas.Series.rfloordiv
rfloordiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn integer division of Series and other, element-wise (binary operator rfloordiv).
See more: bigframes.pandas.Series.rfloordiv
bigframes.pandas.Series.rmod
rmod(other) -> bigframes.series.SeriesReturn modulo of Series and other, element-wise (binary operator mod).
See more: bigframes.pandas.Series.rmod
bigframes.pandas.Series.rmul
rmul(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.pandas.Series.rmul
bigframes.pandas.Series.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods: int | None = None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowProvide rolling window calculations.
See more: bigframes.pandas.Series.rolling
bigframes.pandas.Series.round
round(decimals=0) -> bigframes.series.SeriesRound each value in a Series to the given number of decimals.
See more: bigframes.pandas.Series.round
bigframes.pandas.Series.rpow
rpow(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn Exponential power of series and other, element-wise (binary operator rpow).
See more: bigframes.pandas.Series.rpow
bigframes.pandas.Series.rsub
rsub(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator rsub).
See more: bigframes.pandas.Series.rsub
bigframes.pandas.Series.rtruediv
rtruediv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator rtruediv).
See more: bigframes.pandas.Series.rtruediv
bigframes.pandas.Series.sample
sample( n: typing.Optional[int] = None, frac: typing.Optional[float] = None, *, random_state: typing.Optional[int] = None, sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random" ) -> bigframes.series.SeriesReturn a random sample of items from an axis of object.
See more: bigframes.pandas.Series.sample
bigframes.pandas.Series.shift
shift(periods: int = 1) -> bigframes.series.SeriesShift index by desired number of periods.
See more: bigframes.pandas.Series.shift
bigframes.pandas.Series.skew
skew()Return unbiased skew over requested axis.
See more: bigframes.pandas.Series.skew
bigframes.pandas.Series.sort_index
Sort Series by index labels.
See more: bigframes.pandas.Series.sort_index
bigframes.pandas.Series.sort_values
Sort by the values.
See more: bigframes.pandas.Series.sort_values
bigframes.pandas.Series.std
std() -> floatReturn sample standard deviation over requested axis.
See more: bigframes.pandas.Series.std
bigframes.pandas.Series.sub
sub(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator sub).
See more: bigframes.pandas.Series.sub
bigframes.pandas.Series.subtract
subtract(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator sub).
See more: bigframes.pandas.Series.subtract
bigframes.pandas.Series.sum
sum() -> floatReturn the sum of the values over the requested axis.
See more: bigframes.pandas.Series.sum
bigframes.pandas.Series.swaplevel
swaplevel(i: int = -2, j: int = -1)Swap levels i and j in a MultiIndex.
See more: bigframes.pandas.Series.swaplevel
bigframes.pandas.Series.tail
tail(n: int = 5) -> bigframes.series.SeriesReturn the last n rows.
See more: bigframes.pandas.Series.tail
bigframes.pandas.Series.take
take( indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs ) -> bigframes.series.SeriesReturn the elements in the given positional indices along an axis.
See more: bigframes.pandas.Series.take
bigframes.pandas.Series.to_csv
to_csv( path_or_buf=None, sep=",", *, header: bool = True, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Write object to a comma-separated values (csv) file on Cloud Storage.
See more: bigframes.pandas.Series.to_csv
bigframes.pandas.Series.to_dict
to_dict( into: type[dict] = dict, *, allow_large_results: typing.Optional[bool] = None ) -> typing.MappingConvert Series to {label -> value} dict or dict-like object.
See more: bigframes.pandas.Series.to_dict
bigframes.pandas.Series.to_excel
to_excel( excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs ) -> NoneWrite Series to an Excel sheet.
See more: bigframes.pandas.Series.to_excel
bigframes.pandas.Series.to_frame
to_frame(name: typing.Hashable = None) -> bigframes.dataframe.DataFrameConvert Series to DataFrame.
See more: bigframes.pandas.Series.to_frame
bigframes.pandas.Series.to_json
to_json( path_or_buf=None, orient: typing.Optional[ typing.Literal["split", "records", "index", "columns", "values", "table"] ] = None, *, lines: bool = False, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Convert the object to a JSON string, written to Cloud Storage.
See more: bigframes.pandas.Series.to_json
bigframes.pandas.Series.to_latex
to_latex( buf=None, columns=None, header=True, index=True, *, allow_large_results=None, **kwargs ) -> typing.Optional[str]Render object to a LaTeX tabular, longtable, or nested table.
See more: bigframes.pandas.Series.to_latex
bigframes.pandas.Series.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listReturn a list of the values.
See more: bigframes.pandas.Series.to_list
bigframes.pandas.Series.to_markdown
to_markdown( buf: typing.Optional[typing.IO[str]] = None, mode: str = "wt", index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> typing.Optional[str]Print Series in Markdown-friendly format.
See more: bigframes.pandas.Series.to_markdown
bigframes.pandas.Series.to_numpy
to_numpy( dtype=None, copy=False, na_value=_NoDefault.no_default, *, allow_large_results=None, **kwargs ) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
See more: bigframes.pandas.Series.to_numpy
bigframes.pandas.Series.to_pandas
to_pandas( max_download_size: typing.Optional[int] = None, sampling_method: typing.Optional[str] = None, random_state: typing.Optional[int] = None, *, ordered: bool = True, dry_run: bool = False, allow_large_results: typing.Optional[bool] = None ) -> pandas.core.series.SeriesWrites Series to pandas Series.
See more: bigframes.pandas.Series.to_pandas
bigframes.pandas.Series.to_pandas_batches
to_pandas_batches( page_size: typing.Optional[int] = None, max_results: typing.Optional[int] = None, *, allow_large_results: typing.Optional[bool] = None ) -> typing.Iterable[pandas.core.series.Series]Stream Series results to an iterable of pandas Series.
bigframes.pandas.Series.to_pickle
to_pickle(path, *, allow_large_results=None, **kwargs) -> NonePickle (serialize) object to file.
See more: bigframes.pandas.Series.to_pickle
bigframes.pandas.Series.to_string
to_string( buf=None, na_rep="NaN", float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None, min_rows=None, *, allow_large_results=None ) -> typing.Optional[str]Render a string representation of the Series.
See more: bigframes.pandas.Series.to_string
bigframes.pandas.Series.to_xarray
to_xarray(*, allow_large_results: typing.Optional[bool] = None)Return an xarray object from the pandas object.
See more: bigframes.pandas.Series.to_xarray
bigframes.pandas.Series.tolist
tolist(*, allow_large_results: typing.Optional[bool] = None) -> listReturn a list of the values.
See more: bigframes.pandas.Series.tolist
bigframes.pandas.Series.transpose
transpose() -> bigframes.series.SeriesReturn the transpose, which is by definition self.
See more: bigframes.pandas.Series.transpose
bigframes.pandas.Series.truediv
truediv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.pandas.Series.truediv
bigframes.pandas.Series.unique
unique(keep_order=True) -> bigframes.series.SeriesReturn unique values of Series object.
See more: bigframes.pandas.Series.unique
bigframes.pandas.Series.unstack
unstack( level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1, )Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
See more: bigframes.pandas.Series.unstack
bigframes.pandas.Series.update
update( other: typing.Union[bigframes.series.Series, typing.Sequence, typing.Mapping], ) -> NoneModify Series in place using values from passed Series.
See more: bigframes.pandas.Series.update
bigframes.pandas.Series.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
See more: bigframes.pandas.Series.value_counts
bigframes.pandas.Series.var
var() -> floatReturn unbiased variance over requested axis.
See more: bigframes.pandas.Series.var
bigframes.pandas.Series.where
where(cond, other=None)Replace values where the condition is False.
See more: bigframes.pandas.Series.where
bigframes.pandas.StringDtype.__from_arrow__
__from_arrow__(array: pyarrow.Array | pyarrow.ChunkedArray) -> BaseStringArrayConstruct StringArray from pyarrow Array/ChunkedArray.
See more: bigframes.pandas.StringDtype.from_arrow
bigframes.pandas.StringDtype.construct_array_type
construct_array_type() -> type_t[BaseStringArray]Return the array type associated with this dtype.
bigframes.pandas.StringDtype.construct_from_string
construct_from_string(string) -> SelfConstruct a StringDtype from a string.
See more: bigframes.pandas.StringDtype.construct_from_string
bigframes.series.Series.__abs__
__abs__() -> bigframes.series.SeriesReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.series.Series.abs
bigframes.series.Series.__add__
__add__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet addition of Series and other, element-wise, using operator +.
See more: bigframes.series.Series.add
bigframes.series.Series.__and__
__and__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise AND of Series and other, element-wise, using operator &.
See more: bigframes.series.Series.and
bigframes.series.Series.__array__
__array__(dtype=None, copy: typing.Optional[bool] = None) -> numpy.ndarrayReturns the values as NumPy array.
See more: bigframes.series.Series.array
bigframes.series.Series.__array_ufunc__
__array_ufunc__( ufunc: numpy.ufunc, method: str, *inputs, **kwargs ) -> bigframes.series.SeriesUsed to support numpy ufuncs.
See more: bigframes.series.Series.array_ufunc
bigframes.series.Series.__bool__
__bool__()Returns the truth value of the object.
See more: bigframes.series.Series.bool
bigframes.series.Series.__floordiv__
__floordiv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet integer division of Series by other, using arithmetic operator //.
See more: bigframes.series.Series.floordiv
bigframes.series.Series.__getitem__
__getitem__(indexer)Gets the specified index from the Series.
See more: bigframes.series.Series.getitem
bigframes.series.Series.__invert__
__invert__() -> bigframes.series.SeriesReturns the logical inversion (binary NOT) of the Series, element-wise using operator ````.
See more: bigframes.series.Series.invert
bigframes.series.Series.__len__
__len__()Returns number of values in the Series, serves len operator.
See more: bigframes.series.Series.len
bigframes.series.Series.__matmul__
__matmul__(other)Matrix multiplication using binary @ operator.
See more: bigframes.series.Series.matmul
bigframes.series.Series.__mod__
__mod__(other) -> bigframes.series.SeriesGet modulo of Series with other, element-wise, using operator %.
See more: bigframes.series.Series.mod
bigframes.series.Series.__mul__
__mul__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet multiplication of Series with other, element-wise, using operator *.
See more: bigframes.series.Series.mul
bigframes.series.Series.__nonzero__
__nonzero__()Returns the truth value of the object.
See more: bigframes.series.Series.nonzero
bigframes.series.Series.__or__
__or__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise XOR of Series and other, element-wise, using operator ^.
See more: bigframes.series.Series.or
bigframes.series.Series.__pow__
__pow__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet exponentiation of Series with other, element-wise, using operator **.
See more: bigframes.series.Series.pow
bigframes.series.Series.__radd__
__radd__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet addition of Series and other, element-wise, using operator +.
See more: bigframes.series.Series.radd
bigframes.series.Series.__rand__
__rand__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise AND of Series and other, element-wise, using operator &.
See more: bigframes.series.Series.rand
bigframes.series.Series.__rfloordiv__
__rfloordiv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet integer division of other by Series, using arithmetic operator //.
See more: bigframes.series.Series.rfloordiv
bigframes.series.Series.__rmatmul__
__rmatmul__(other)Matrix multiplication using binary @ operator.
See more: bigframes.series.Series.rmatmul
bigframes.series.Series.__rmod__
__rmod__(other) -> bigframes.series.SeriesGet modulo of other with Series, element-wise, using operator %.
See more: bigframes.series.Series.rmod
bigframes.series.Series.__rmul__
__rmul__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet multiplication of other with Series, element-wise, using operator *.
See more: bigframes.series.Series.rmul
bigframes.series.Series.__ror__
__ror__(other: bool | int | bigframes.series.Series) -> bigframes.series.SeriesGet bitwise XOR of Series and other, element-wise, using operator ^.
See more: bigframes.series.Series.ror
bigframes.series.Series.__rpow__
__rpow__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet exponentiation of other with Series, element-wise, using operator **.
See more: bigframes.series.Series.rpow
bigframes.series.Series.__rsub__
__rsub__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet subtraction of Series from other, element-wise, using operator -.
See more: bigframes.series.Series.rsub
bigframes.series.Series.__rtruediv__
__rtruediv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet division of other by Series, element-wise, using operator /.
See more: bigframes.series.Series.rtruediv
bigframes.series.Series.__setitem__
__setitem__(key, value) -> NoneSet item using direct assignment, delegating to .loc indexer.
See more: bigframes.series.Series.setitem
bigframes.series.Series.__sub__
__sub__(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesGet subtraction of other from Series, element-wise, using operator -.
See more: bigframes.series.Series.sub
bigframes.series.Series.__truediv__
__truediv__( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesGet division of Series by other, element-wise, using operator /.
See more: bigframes.series.Series.truediv
bigframes.series.Series.abs
abs() -> bigframes.series.SeriesReturn a Series/DataFrame with absolute numeric value of each element.
See more: bigframes.series.Series.abs
bigframes.series.Series.add
add( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn addition of Series and other, element-wise (binary operator add).
See more: bigframes.series.Series.add
bigframes.series.Series.add_prefix
add_prefix(prefix: str, axis: int | str | None = None) -> bigframes.series.SeriesPrefix labels with string prefix.
See more: bigframes.series.Series.add_prefix
bigframes.series.Series.add_suffix
add_suffix(suffix: str, axis: int | str | None = None) -> bigframes.series.SeriesSuffix labels with string suffix.
See more: bigframes.series.Series.add_suffix
bigframes.series.Series.agg
agg( func: typing.Union[str, typing.Sequence[str]], ) -> typing.Union[typing.Any, bigframes.series.Series]Aggregate using one or more operations over the specified axis.
See more: bigframes.series.Series.agg
bigframes.series.Series.aggregate
aggregate( func: typing.Union[str, typing.Sequence[str]], ) -> typing.Union[typing.Any, bigframes.series.Series]Aggregate using one or more operations over the specified axis.
See more: bigframes.series.Series.aggregate
bigframes.series.Series.all
all() -> boolReturn whether all elements are True, potentially over an axis.
See more: bigframes.series.Series.all
bigframes.series.Series.any
any() -> boolReturn whether any element is True, potentially over an axis.
See more: bigframes.series.Series.any
bigframes.series.Series.apply
apply( func, by_row: typing.Union[typing.Literal["compat"], bool] = "compat", *, args: typing.Tuple = () ) -> bigframes.series.SeriesInvoke function on values of a Series.
See more: bigframes.series.Series.apply
bigframes.series.Series.area
area( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, stacked: bool = True, **kwargs )Draw a stacked area plot.
See more: bigframes.series.Series.area
bigframes.series.Series.argmax
argmax() -> intReturn int position of the largest value in the series.
See more: bigframes.series.Series.argmax
bigframes.series.Series.argmin
argmin() -> intReturn int position of the smallest value in the Series.
See more: bigframes.series.Series.argmin
bigframes.series.Series.astype
astype( dtype: typing.Union[ typing.Literal[ "boolean", "Float64", "Int64", "int64[pyarrow]", "string", "string[pyarrow]", "timestamp[us, tz=UTC][pyarrow]", "timestamp[us][pyarrow]", "date32[day][pyarrow]", "time64[us][pyarrow]", "decimal128(38, 9)[pyarrow]", "decimal256(76, 38)[pyarrow]", "binary[pyarrow]", "duration[us][pyarrow]", ], pandas.core.arrays.boolean.BooleanDtype, pandas.core.arrays.floating.Float64Dtype, pandas.core.arrays.integer.Int64Dtype, pandas.core.arrays.string_.StringDtype, pandas.core.dtypes.dtypes.ArrowDtype, geopandas.array.GeometryDtype, ], *, errors: typing.Literal["raise", "null"] = "raise" ) -> bigframes.series.SeriesCast a pandas object to a specified dtype dtype.
See more: bigframes.series.Series.astype
bigframes.series.Series.autocorr
autocorr(lag: int = 1) -> floatCompute the lag-N autocorrelation.
See more: bigframes.series.Series.autocorr
bigframes.series.Series.bar
bar( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Draw a vertical bar plot.
See more: bigframes.series.Series.bar
bigframes.series.Series.between
between(left, right, inclusive="both")Return boolean Series equivalent to left <= series <= right.
See more: bigframes.series.Series.between
bigframes.series.Series.bfill
bfill(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by using the next valid observation to fill the gap.
See more: bigframes.series.Series.bfill
bigframes.series.Series.cache
cache()Materializes the Series to a temporary table.
See more: bigframes.series.Series.cache
bigframes.series.Series.case_when
case_when(caselist) -> bigframes.series.SeriesReplace values where the conditions are True.
See more: bigframes.series.Series.case_when
bigframes.series.Series.clip
clip(lower=None, upper=None)Trim values at input threshold(s).
See more: bigframes.series.Series.clip
bigframes.series.Series.combine
combine(other, func) -> bigframes.series.SeriesCombine the Series with a Series or scalar according to func.
See more: bigframes.series.Series.combine
bigframes.series.Series.combine_first
combine_first(other: bigframes.series.Series) -> bigframes.series.SeriesUpdate null elements with value in the same location in 'other'.
See more: bigframes.series.Series.combine_first
bigframes.series.Series.copy
copy() -> bigframes.series.SeriesMake a copy of this object's indices and data.
See more: bigframes.series.Series.copy
bigframes.series.Series.corr
corr(other: bigframes.series.Series, method="pearson", min_periods=None) -> floatCompute the correlation with the other Series.
See more: bigframes.series.Series.corr
bigframes.series.Series.count
count() -> intReturn number of non-NA/null observations in the Series.
See more: bigframes.series.Series.count
bigframes.series.Series.cov
cov(other: bigframes.series.Series) -> floatCompute covariance with Series, excluding missing values.
See more: bigframes.series.Series.cov
bigframes.series.Series.cummax
cummax() -> bigframes.series.SeriesReturn cumulative maximum over a DataFrame or Series axis.
See more: bigframes.series.Series.cummax
bigframes.series.Series.cummin
cummin() -> bigframes.series.SeriesReturn cumulative minimum over a DataFrame or Series axis.
See more: bigframes.series.Series.cummin
bigframes.series.Series.cumprod
cumprod() -> bigframes.series.SeriesReturn cumulative product over a DataFrame or Series axis.
See more: bigframes.series.Series.cumprod
bigframes.series.Series.cumsum
cumsum() -> bigframes.series.SeriesReturn cumulative sum over a DataFrame or Series axis.
See more: bigframes.series.Series.cumsum
bigframes.series.Series.describe
describe() -> bigframes.series.SeriesGenerate descriptive statistics.
See more: bigframes.series.Series.describe
bigframes.series.Series.diff
diff(periods: int = 1) -> bigframes.series.SeriesFirst discrete difference of element.
See more: bigframes.series.Series.diff
bigframes.series.Series.div
div( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.series.Series.div
bigframes.series.Series.divide
divide( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.series.Series.divide
bigframes.series.Series.divmod
divmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]Return integer division and modulo of Series and other, element-wise (binary operator divmod).
See more: bigframes.series.Series.divmod
bigframes.series.Series.dot
dot(other)Compute the dot product between the Series and the columns of other.
See more: bigframes.series.Series.dot
bigframes.series.Series.drop
drop( labels: typing.Any = None, *, axis: typing.Union[int, str] = 0, index: typing.Any = None, columns: typing.Union[typing.Hashable, typing.Iterable[typing.Hashable]] = None, level: typing.Optional[typing.Union[str, int]] = None ) -> bigframes.series.SeriesReturn Series with specified index labels removed.
See more: bigframes.series.Series.drop
bigframes.series.Series.drop_duplicates
drop_duplicates(*, keep: str = "first") -> bigframes.series.SeriesReturn Series with duplicate values removed.
See more: bigframes.series.Series.drop_duplicates
bigframes.series.Series.droplevel
droplevel( level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]], axis: int | str = 0, )Return Series with requested index / column level(s) removed.
See more: bigframes.series.Series.droplevel
bigframes.series.Series.dropna
dropna( *, axis: int = 0, inplace: bool = False, how: typing.Optional[str] = None, ignore_index: bool = False ) -> bigframes.series.SeriesReturn a new Series with missing values removed.
See more: bigframes.series.Series.dropna
bigframes.series.Series.duplicated
duplicated(keep: str = "first") -> bigframes.series.SeriesIndicate duplicate Series values.
See more: bigframes.series.Series.duplicated
bigframes.series.Series.eq
eq(other: object) -> bigframes.series.SeriesReturn equal of Series and other, element-wise (binary operator eq).
See more: bigframes.series.Series.eq
bigframes.series.Series.equals
equals( other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame], ) -> boolTest whether two objects contain the same elements.
See more: bigframes.series.Series.equals
bigframes.series.Series.expanding
expanding(min_periods: int = 1) -> bigframes.core.window.rolling.WindowProvide expanding window calculations.
See more: bigframes.series.Series.expanding
bigframes.series.Series.explode
explode(*, ignore_index: typing.Optional[bool] = False) -> bigframes.series.SeriesTransform each element of a list-like to a row.
See more: bigframes.series.Series.explode
bigframes.series.Series.ffill
ffill(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.series.Series.ffill
bigframes.series.Series.fillna
fillna(value=None) -> bigframes.series.SeriesFill NA (NULL in BigQuery) values using the specified method.
See more: bigframes.series.Series.fillna
bigframes.series.Series.filter
filter( items: typing.Optional[typing.Iterable] = None, like: typing.Optional[str] = None, regex: typing.Optional[str] = None, axis: typing.Optional[typing.Union[str, int]] = None, ) -> bigframes.series.SeriesSubset the dataframe rows or columns according to the specified index labels.
See more: bigframes.series.Series.filter
bigframes.series.Series.floordiv
floordiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn integer division of Series and other, element-wise (binary operator floordiv).
See more: bigframes.series.Series.floordiv
bigframes.series.Series.ge
ge(other) -> bigframes.series.SeriesGet 'greater than or equal to' of Series and other, element-wise (binary operator ge).
See more: bigframes.series.Series.ge
bigframes.series.Series.get
get(key, default=None)Get item from object for given key (ex: DataFrame column).
See more: bigframes.series.Series.get
bigframes.series.Series.groupby
groupby( by: typing.Union[ typing.Hashable, bigframes.series.Series, typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]], ] = None, axis=0, level: typing.Optional[ typing.Union[int, str, typing.Sequence[int], typing.Sequence[str]] ] = None, as_index: bool = True, *, dropna: bool = True ) -> bigframes.core.groupby.series_group_by.SeriesGroupByGroup Series using a mapper or by a Series of columns.
See more: bigframes.series.Series.groupby
bigframes.series.Series.gt
gt(other) -> bigframes.series.SeriesReturn Greater than of series and other, element-wise (binary operator gt).
See more: bigframes.series.Series.gt
bigframes.series.Series.head
head(n: int = 5) -> bigframes.series.SeriesReturn the first n rows.
See more: bigframes.series.Series.head
bigframes.series.Series.hist
hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)Draw one histogram of the DataFrame’s columns.
See more: bigframes.series.Series.hist
bigframes.series.Series.idxmax
idxmax() -> typing.HashableReturn the row label of the maximum value.
See more: bigframes.series.Series.idxmax
bigframes.series.Series.idxmin
idxmin() -> typing.HashableReturn the row label of the minimum value.
See more: bigframes.series.Series.idxmin
bigframes.series.Series.interpolate
interpolate(method: str = "linear") -> bigframes.series.SeriesFill NaN values using an interpolation method.
See more: bigframes.series.Series.interpolate
bigframes.series.Series.isin
isin(values) -> bigframes.series.SeriesWhether elements in Series are contained in values.
See more: bigframes.series.Series.isin
bigframes.series.Series.isna
isna() -> bigframes.series.SeriesDetect missing (NULL) values.
See more: bigframes.series.Series.isna
bigframes.series.Series.isnull
isnull() -> bigframes.series.SeriesDetect missing (NULL) values.
See more: bigframes.series.Series.isnull
bigframes.series.Series.item
item()Return the first element of the underlying data as a Python scalar.
See more: bigframes.series.Series.item
bigframes.series.Series.items
items()Lazily iterate over (index, value) tuples.
See more: bigframes.series.Series.items
bigframes.series.Series.keys
keys() -> bigframes.core.indexes.base.IndexReturn alias for index.
See more: bigframes.series.Series.keys
bigframes.series.Series.kurt
kurt()Return unbiased kurtosis over requested axis.
See more: bigframes.series.Series.kurt
bigframes.series.Series.kurtosis
kurtosis()Return unbiased kurtosis over requested axis.
See more: bigframes.series.Series.kurtosis
bigframes.series.Series.le
le(other) -> bigframes.series.SeriesGet 'less than or equal to' of Series and other, element-wise (binary operator le).
See more: bigframes.series.Series.le
bigframes.series.Series.line
line( x: typing.Optional[typing.Hashable] = None, y: typing.Optional[typing.Hashable] = None, **kwargs )Plot Series or DataFrame as lines.
See more: bigframes.series.Series.line
bigframes.series.Series.lt
lt(other) -> bigframes.series.SeriesGet 'less than' of Series and other, element-wise (binary operator lt).
See more: bigframes.series.Series.lt
bigframes.series.Series.map
map( arg: typing.Union[typing.Mapping, bigframes.series.Series, typing.Callable], na_action: typing.Optional[str] = None, *, verify_integrity: bool = False ) -> bigframes.series.SeriesMap values of Series according to an input mapping or function.
See more: bigframes.series.Series.map
bigframes.series.Series.mask
mask(cond, other=None) -> bigframes.series.SeriesReplace values where the condition is True.
See more: bigframes.series.Series.mask
bigframes.series.Series.max
max() -> typing.AnyReturn the maximum of the values over the requested axis.
See more: bigframes.series.Series.max
bigframes.series.Series.mean
mean() -> floatReturn the mean of the values over the requested axis.
See more: bigframes.series.Series.mean
bigframes.series.Series.median
median(*, exact: bool = True) -> floatReturn the median of the values over the requested axis.
See more: bigframes.series.Series.median
bigframes.series.Series.min
min() -> typing.AnyReturn the maximum of the values over the requested axis.
See more: bigframes.series.Series.min
bigframes.series.Series.mod
mod(other) -> bigframes.series.SeriesReturn modulo of Series and other, element-wise (binary operator mod).
See more: bigframes.series.Series.mod
bigframes.series.Series.mode
mode() -> bigframes.series.SeriesReturn the mode(s) of the Series.
See more: bigframes.series.Series.mode
bigframes.series.Series.mul
mul(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.series.Series.mul
bigframes.series.Series.multiply
multiply(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.series.Series.multiply
bigframes.series.Series.ne
ne(other: object) -> bigframes.series.SeriesReturn not equal of Series and other, element-wise (binary operator ne).
See more: bigframes.series.Series.ne
bigframes.series.Series.nlargest
nlargest(n: int = 5, keep: str = "first") -> bigframes.series.SeriesReturn the largest n elements.
See more: bigframes.series.Series.nlargest
bigframes.series.Series.notna
notna() -> bigframes.series.SeriesDetect existing (non-missing) values.
See more: bigframes.series.Series.notna
bigframes.series.Series.notnull
notnull() -> bigframes.series.SeriesDetect existing (non-missing) values.
See more: bigframes.series.Series.notnull
bigframes.series.Series.nsmallest
nsmallest(n: int = 5, keep: str = "first") -> bigframes.series.SeriesReturn the smallest n elements.
See more: bigframes.series.Series.nsmallest
bigframes.series.Series.nunique
nunique() -> intReturn number of unique elements in the object.
See more: bigframes.series.Series.nunique
bigframes.series.Series.pad
pad(*, limit: typing.Optional[int] = None) -> bigframes.series.SeriesFill NA/NaN values by propagating the last valid observation to next valid.
See more: bigframes.series.Series.pad
bigframes.series.Series.pct_change
pct_change(periods: int = 1) -> bigframes.series.SeriesFractional change between the current and a prior element.
See more: bigframes.series.Series.pct_change
bigframes.series.Series.peek
peek( n: int = 5, *, force: bool = True, allow_large_results=None ) -> pandas.core.series.SeriesPreview n arbitrary elements from the series without guarantees about row selection or ordering.
See more: bigframes.series.Series.peek
bigframes.series.Series.pipe
pipe(func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs) -> TApply chainable functions that expect Series or DataFrames.
See more: bigframes.series.Series.pipe
bigframes.series.Series.pow
pow(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn Exponential power of series and other, element-wise (binary operator pow).
See more: bigframes.series.Series.pow
bigframes.series.Series.prod
prod() -> floatReturn the product of the values over the requested axis.
See more: bigframes.series.Series.prod
bigframes.series.Series.product
product() -> floatReturn the product of the values over the requested axis.
See more: bigframes.series.Series.product
bigframes.series.Series.quantile
quantile( q: typing.Union[float, typing.Sequence[float]] = 0.5, ) -> typing.Union[bigframes.series.Series, float]Return value at the given quantile.
See more: bigframes.series.Series.quantile
bigframes.series.Series.radd
radd( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn addition of Series and other, element-wise (binary operator radd).
See more: bigframes.series.Series.radd
bigframes.series.Series.rank
rank( axis=0, method: str = "average", numeric_only=False, na_option: str = "keep", ascending: bool = True, pct: bool = False, ) -> bigframes.series.SeriesCompute numerical data ranks (1 through n) along axis.
See more: bigframes.series.Series.rank
bigframes.series.Series.rdiv
rdiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator rtruediv).
See more: bigframes.series.Series.rdiv
bigframes.series.Series.rdivmod
rdivmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]Return integer division and modulo of Series and other, element-wise (binary operator rdivmod).
See more: bigframes.series.Series.rdivmod
bigframes.series.Series.reindex
reindex(index=None, *, validate: typing.Optional[bool] = None)Conform Series to new index with optional filling logic.
See more: bigframes.series.Series.reindex
bigframes.series.Series.reindex_like
reindex_like( other: bigframes.series.Series, *, validate: typing.Optional[bool] = None )Return an object with matching indices as other object.
See more: bigframes.series.Series.reindex_like
bigframes.series.Series.rename
Alter Series index labels or name.
See more: bigframes.series.Series.rename
bigframes.series.Series.rename_axis
Set the name of the axis for the index or columns.
See more: bigframes.series.Series.rename_axis
bigframes.series.Series.reorder_levels
reorder_levels( order: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]], axis: int | str = 0, )Rearrange index levels using input order.
See more: bigframes.series.Series.reorder_levels
bigframes.series.Series.replace
replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)Replace values given in to_replace with value.
See more: bigframes.series.Series.replace
bigframes.series.Series.resample
resample( rule: str, *, closed: typing.Optional[typing.Literal["right", "left"]] = None, label: typing.Optional[typing.Literal["right", "left"]] = None, level: typing.Optional[ typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] ] = None, origin: typing.Union[ pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, numpy.datetime64, int, float, str, typing.Literal["epoch", "start", "start_day", "end", "end_day"], ] = "start_day" ) -> bigframes.core.groupby.series_group_by.SeriesGroupByResample time-series data.
See more: bigframes.series.Series.resample
bigframes.series.Series.reset_index
Generate a new DataFrame or Series with the index reset.
See more: bigframes.series.Series.reset_index
bigframes.series.Series.rfloordiv
rfloordiv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn integer division of Series and other, element-wise (binary operator rfloordiv).
See more: bigframes.series.Series.rfloordiv
bigframes.series.Series.rmod
rmod(other) -> bigframes.series.SeriesReturn modulo of Series and other, element-wise (binary operator mod).
See more: bigframes.series.Series.rmod
bigframes.series.Series.rmul
rmul(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn multiplication of Series and other, element-wise (binary operator mul).
See more: bigframes.series.Series.rmul
bigframes.series.Series.rolling
rolling( window: ( int | pandas._libs.tslibs.timedeltas.Timedelta | numpy.timedelta64 | datetime.timedelta | str ), min_periods: int | None = None, closed: typing.Literal["right", "left", "both", "neither"] = "right", ) -> bigframes.core.window.rolling.WindowProvide rolling window calculations.
See more: bigframes.series.Series.rolling
bigframes.series.Series.round
round(decimals=0) -> bigframes.series.SeriesRound each value in a Series to the given number of decimals.
See more: bigframes.series.Series.round
bigframes.series.Series.rpow
rpow(other: float | int | bigframes.series.Series) -> bigframes.series.SeriesReturn Exponential power of series and other, element-wise (binary operator rpow).
See more: bigframes.series.Series.rpow
bigframes.series.Series.rsub
rsub(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator rsub).
See more: bigframes.series.Series.rsub
bigframes.series.Series.rtruediv
rtruediv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator rtruediv).
See more: bigframes.series.Series.rtruediv
bigframes.series.Series.sample
sample( n: typing.Optional[int] = None, frac: typing.Optional[float] = None, *, random_state: typing.Optional[int] = None, sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random" ) -> bigframes.series.SeriesReturn a random sample of items from an axis of object.
See more: bigframes.series.Series.sample
bigframes.series.Series.shift
shift(periods: int = 1) -> bigframes.series.SeriesShift index by desired number of periods.
See more: bigframes.series.Series.shift
bigframes.series.Series.skew
skew()Return unbiased skew over requested axis.
See more: bigframes.series.Series.skew
bigframes.series.Series.sort_index
Sort Series by index labels.
See more: bigframes.series.Series.sort_index
bigframes.series.Series.sort_values
Sort by the values.
See more: bigframes.series.Series.sort_values
bigframes.series.Series.std
std() -> floatReturn sample standard deviation over requested axis.
See more: bigframes.series.Series.std
bigframes.series.Series.sub
sub(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator sub).
See more: bigframes.series.Series.sub
bigframes.series.Series.subtract
subtract(other) -> bigframes.series.SeriesReturn subtraction of Series and other, element-wise (binary operator sub).
See more: bigframes.series.Series.subtract
bigframes.series.Series.sum
sum() -> floatReturn the sum of the values over the requested axis.
See more: bigframes.series.Series.sum
bigframes.series.Series.swaplevel
swaplevel(i: int = -2, j: int = -1)Swap levels i and j in a MultiIndex.
See more: bigframes.series.Series.swaplevel
bigframes.series.Series.tail
tail(n: int = 5) -> bigframes.series.SeriesReturn the last n rows.
See more: bigframes.series.Series.tail
bigframes.series.Series.take
take( indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs ) -> bigframes.series.SeriesReturn the elements in the given positional indices along an axis.
See more: bigframes.series.Series.take
bigframes.series.Series.to_csv
to_csv( path_or_buf=None, sep=",", *, header: bool = True, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Write object to a comma-separated values (csv) file on Cloud Storage.
See more: bigframes.series.Series.to_csv
bigframes.series.Series.to_dict
to_dict( into: type[dict] = dict, *, allow_large_results: typing.Optional[bool] = None ) -> typing.MappingConvert Series to {label -> value} dict or dict-like object.
See more: bigframes.series.Series.to_dict
bigframes.series.Series.to_excel
to_excel( excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **kwargs ) -> NoneWrite Series to an Excel sheet.
See more: bigframes.series.Series.to_excel
bigframes.series.Series.to_frame
to_frame(name: typing.Hashable = None) -> bigframes.dataframe.DataFrameConvert Series to DataFrame.
See more: bigframes.series.Series.to_frame
bigframes.series.Series.to_json
to_json( path_or_buf=None, orient: typing.Optional[ typing.Literal["split", "records", "index", "columns", "values", "table"] ] = None, *, lines: bool = False, index: bool = True, allow_large_results: typing.Optional[bool] = None ) -> typing.Optional[str]Convert the object to a JSON string, written to Cloud Storage.
See more: bigframes.series.Series.to_json
bigframes.series.Series.to_latex
to_latex( buf=None, columns=None, header=True, index=True, *, allow_large_results=None, **kwargs ) -> typing.Optional[str]Render object to a LaTeX tabular, longtable, or nested table.
See more: bigframes.series.Series.to_latex
bigframes.series.Series.to_list
to_list(*, allow_large_results: typing.Optional[bool] = None) -> listReturn a list of the values.
See more: bigframes.series.Series.to_list
bigframes.series.Series.to_markdown
to_markdown( buf: typing.Optional[typing.IO[str]] = None, mode: str = "wt", index: bool = True, *, allow_large_results: typing.Optional[bool] = None, **kwargs ) -> typing.Optional[str]Print Series in Markdown-friendly format.
See more: bigframes.series.Series.to_markdown
bigframes.series.Series.to_numpy
to_numpy( dtype=None, copy=False, na_value=_NoDefault.no_default, *, allow_large_results=None, **kwargs ) -> numpy.ndarrayA NumPy ndarray representing the values in this Series or Index.
See more: bigframes.series.Series.to_numpy
bigframes.series.Series.to_pandas
to_pandas( max_download_size: typing.Optional[int] = None, sampling_method: typing.Optional[str] = None, random_state: typing.Optional[int] = None, *, ordered: bool = True, dry_run: bool = False, allow_large_results: typing.Optional[bool] = None ) -> pandas.core.series.SeriesWrites Series to pandas Series.
See more: bigframes.series.Series.to_pandas
bigframes.series.Series.to_pandas_batches
to_pandas_batches( page_size: typing.Optional[int] = None, max_results: typing.Optional[int] = None, *, allow_large_results: typing.Optional[bool] = None ) -> typing.Iterable[pandas.core.series.Series]Stream Series results to an iterable of pandas Series.
bigframes.series.Series.to_pickle
to_pickle(path, *, allow_large_results=None, **kwargs) -> NonePickle (serialize) object to file.
See more: bigframes.series.Series.to_pickle
bigframes.series.Series.to_string
to_string( buf=None, na_rep="NaN", float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None, min_rows=None, *, allow_large_results=None ) -> typing.Optional[str]Render a string representation of the Series.
See more: bigframes.series.Series.to_string
bigframes.series.Series.to_xarray
to_xarray(*, allow_large_results: typing.Optional[bool] = None)Return an xarray object from the pandas object.
See more: bigframes.series.Series.to_xarray
bigframes.series.Series.tolist
tolist(*, allow_large_results: typing.Optional[bool] = None) -> listReturn a list of the values.
See more: bigframes.series.Series.tolist
bigframes.series.Series.transpose
transpose() -> bigframes.series.SeriesReturn the transpose, which is by definition self.
See more: bigframes.series.Series.transpose
bigframes.series.Series.truediv
truediv( other: ( float | int | pandas._libs.tslibs.timedeltas.Timedelta | bigframes.series.Series ), ) -> bigframes.series.SeriesReturn floating division of Series and other, element-wise (binary operator truediv).
See more: bigframes.series.Series.truediv
bigframes.series.Series.unique
unique(keep_order=True) -> bigframes.series.SeriesReturn unique values of Series object.
See more: bigframes.series.Series.unique
bigframes.series.Series.unstack
unstack( level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1, )Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
See more: bigframes.series.Series.unstack
bigframes.series.Series.update
update( other: typing.Union[bigframes.series.Series, typing.Sequence, typing.Mapping], ) -> NoneModify Series in place using values from passed Series.
See more: bigframes.series.Series.update
bigframes.series.Series.value_counts
value_counts( normalize: bool = False, sort: bool = True, ascending: bool = False, *, dropna: bool = True )Return a Series containing counts of unique values.
See more: bigframes.series.Series.value_counts
bigframes.series.Series.var
var() -> floatReturn unbiased variance over requested axis.
See more: bigframes.series.Series.var
bigframes.series.Series.where
where(cond, other=None)Replace values where the condition is False.
See more: bigframes.series.Series.where
bigframes.session.Session.DataFrame
DataFrame(*args, **kwargs)Constructs a DataFrame.
See more: bigframes.session.Session.DataFrame
bigframes.session.Session.Index
Index(*args, **kwargs)Constructs a Index.
See more: bigframes.session.Session.Index
bigframes.session.Session.Series
Series(*args, **kwargs)Constructs a Series.
See more: bigframes.session.Session.Series
bigframes.session.Session.__del__
__del__()Automatic cleanup of internal resources.
See more: bigframes.session.Session.del
bigframes.session.Session.__enter__
__enter__()Enter the runtime context of the Session object.
See more: bigframes.session.Session.enter
bigframes.session.Session.__exit__
__exit__(*_)Exit the runtime context of the Session object.
See more: bigframes.session.Session.exit
bigframes.session.Session.close
close()Delete resources that were created with this session's session_id.
See more: bigframes.session.Session.close
bigframes.session.Session.cut
cut(*args, **kwargs) -> bigframes.series.SeriesCuts a BigQuery DataFrames object.
See more: bigframes.session.Session.cut
bigframes.session.Session.deploy_remote_function
deploy_remote_function(func, **kwargs)Orchestrates the creation of a BigQuery remote function that deploys immediately.
bigframes.session.Session.deploy_udf
deploy_udf(func, **kwargs)Orchestrates the creation of a BigQuery UDF that deploys immediately.
See more: bigframes.session.Session.deploy_udf
bigframes.session.Session.from_glob_path
from_glob_path( path: str, *, connection: Optional[str] = None, name: Optional[str] = None ) -> dataframe.DataFrameCreate a BigFrames DataFrame that contains a BigFrames Blob column from a global wildcard path.
See more: bigframes.session.Session.from_glob_path
bigframes.session.Session.read_arrow
read_arrow(pa_table: pyarrow.lib.Table) -> bigframes.dataframe.DataFrameLoad a PyArrow Table to a BigQuery DataFrames DataFrame.
See more: bigframes.session.Session.read_arrow
bigframes.session.Session.read_csv
read_csv( filepath_or_buffer: str | IO["bytes"], *, sep: Optional[str] = ",", header: Optional[int] = 0, names: Optional[ Union[MutableSequence[Any], np.ndarray[Any, Any], Tuple[Any, ...], range] ] = None, index_col: Optional[ Union[ int, str, Sequence[Union[str, int]], bigframes.enums.DefaultIndexKind, Literal[False], ] ] = None, usecols: Optional[ Union[ MutableSequence[str], Tuple[str, ...], Sequence[int], pandas.Series, pandas.Index, np.ndarray[Any, Any], Callable[[Any], bool], ] ] = None, dtype: Optional[Dict] = None, engine: Optional[ Literal["c", "python", "pyarrow", "python-fwf", "bigquery"] ] = None, encoding: Optional[str] = None, write_engine: constants.WriteEngineType = "default", **kwargs ) -> dataframe.DataFrameLoads data from a comma-separated values (csv) file into a DataFrame.
See more: bigframes.session.Session.read_csv
bigframes.session.Session.read_gbq
Loads a DataFrame from BigQuery.
See more: bigframes.session.Session.read_gbq
bigframes.session.Session.read_gbq_function
read_gbq_function(function_name: str, is_row_processor: bool = False)Loads a BigQuery function from BigQuery.
bigframes.session.Session.read_gbq_model
read_gbq_model(model_name: str)Loads a BigQuery ML model from BigQuery.
See more: bigframes.session.Session.read_gbq_model
bigframes.session.Session.read_gbq_object_table
read_gbq_object_table( object_table: str, *, name: Optional[str] = None ) -> dataframe.DataFrameRead an existing object table to create a BigFrames Blob DataFrame.
bigframes.session.Session.read_gbq_query
Turn a SQL query into a DataFrame.
See more: bigframes.session.Session.read_gbq_query
bigframes.session.Session.read_gbq_table
Turn a BigQuery table into a DataFrame.
See more: bigframes.session.Session.read_gbq_table
bigframes.session.Session.read_gbq_table_streaming
read_gbq_table_streaming(table: str) -> streaming_dataframe.StreamingDataFrameTurn a BigQuery table into a StreamingDataFrame.
See more: bigframes.session.Session.read_gbq_table_streaming
bigframes.session.Session.read_json
read_json( path_or_buf: str | IO["bytes"], *, orient: Literal[ "split", "records", "index", "columns", "values", "table" ] = "columns", dtype: Optional[Dict] = None, encoding: Optional[str] = None, lines: bool = False, engine: Literal["ujson", "pyarrow", "bigquery"] = "ujson", write_engine: constants.WriteEngineType = "default", **kwargs ) -> dataframe.DataFrameConvert a JSON string to DataFrame object.
See more: bigframes.session.Session.read_json
bigframes.session.Session.read_pandas
Loads DataFrame from a pandas DataFrame.
See more: bigframes.session.Session.read_pandas
bigframes.session.Session.read_parquet
read_parquet( path: str | IO["bytes"], *, engine: str = "auto", write_engine: constants.WriteEngineType = "default" ) -> dataframe.DataFrameLoad a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.
See more: bigframes.session.Session.read_parquet
bigframes.session.Session.read_pickle
read_pickle( filepath_or_buffer: FilePath | ReadPickleBuffer, compression: CompressionOptions = "infer", storage_options: StorageOptions = None, *, write_engine: constants.WriteEngineType = "default" )Load pickled BigFrames object (or any object) from file.
See more: bigframes.session.Session.read_pickle
bigframes.session.Session.remote_function
remote_function( input_types: typing.Union[None, type, typing.Sequence[type]] = None, output_type: typing.Optional[type] = None, dataset: typing.Optional[str] = None, *, bigquery_connection: typing.Optional[str] = None, reuse: bool = True, name: typing.Optional[str] = None, packages: typing.Optional[typing.Sequence[str]] = None, cloud_function_service_account: str, cloud_function_kms_key_name: typing.Optional[str] = None, cloud_function_docker_repository: typing.Optional[str] = None, max_batching_rows: typing.Optional[int] = 1000, cloud_function_timeout: typing.Optional[int] = 600, cloud_function_max_instances: typing.Optional[int] = None, cloud_function_vpc_connector: typing.Optional[str] = None, cloud_function_vpc_connector_egress_settings: typing.Optional[ typing.Literal["all", "private-ranges-only", "unspecified"] ] = None, cloud_function_memory_mib: typing.Optional[int] = 1024, cloud_function_ingress_settings: typing.Literal[ "all", "internal-only", "internal-and-gclb" ] = "internal-only", cloud_build_service_account: typing.Optional[str] = None )Decorator to turn a user defined function into a BigQuery remote function.
bigframes.session.Session.to_datetime
to_datetime( *args, **kwargs ) -> typing.Union[ pandas._libs.tslibs.timestamps.Timestamp, datetime.datetime, bigframes.series.Series ]Converts a BigQuery DataFrames object to datetime dtype.
See more: bigframes.session.Session.to_datetime
bigframes.session.Session.to_timedelta
to_timedelta(*args, **kwargs)Converts a BigQuery DataFrames object to timedelta/duration dtype.
See more: bigframes.session.Session.to_timedelta
bigframes.session.Session.udf
udf( *, input_types: typing.Union[None, type, typing.Sequence[type]] = None, output_type: typing.Optional[type] = None, dataset: str, bigquery_connection: typing.Optional[str] = None, name: str, packages: typing.Optional[typing.Sequence[str]] = None, max_batching_rows: typing.Optional[int] = None, container_cpu: typing.Optional[float] = None, container_memory: typing.Optional[str] = None )Decorator to turn a Python user defined function (udf) into a BigQuery managed user-defined function.
See more: bigframes.session.Session.udf
bigframes.streaming.dataframe.StreamingDataFrame.__getitem__
__getitem__(*args, **kwargs)Gets the specified column(s) from the StreamingDataFrame.
See more: bigframes.streaming.dataframe.StreamingDataFrame.getitem
bigframes.streaming.dataframe.StreamingDataFrame.__repr__
__repr__(*args, **kwargs)Converts a StreamingDataFrame to a string.
See more: bigframes.streaming.dataframe.StreamingDataFrame.repr
bigframes.streaming.dataframe.StreamingDataFrame.__setitem__
__setitem__(*args, **kwargs)Modify or insert a column into the StreamingDataFrame.
See more: bigframes.streaming.dataframe.StreamingDataFrame.setitem
bigframes.streaming.dataframe.StreamingDataFrame.rename
rename(*args, **kwargs)Rename columns.
See more: bigframes.streaming.dataframe.StreamingDataFrame.rename
bigframes.streaming.dataframe.StreamingDataFrame.to_bigtable
to_bigtable( *, instance: str, table: str, service_account_email: typing.Optional[str] = None, app_profile: typing.Optional[str] = None, truncate: bool = False, overwrite: bool = False, auto_create_column_families: bool = False, bigtable_options: typing.Optional[dict] = None, job_id: typing.Optional[str] = None, job_id_prefix: typing.Optional[str] = None, start_timestamp: typing.Optional[ typing.Union[int, float, str, datetime.datetime, datetime.date] ] = None, end_timestamp: typing.Optional[ typing.Union[int, float, str, datetime.datetime, datetime.date] ] = None ) -> google.cloud.bigquery.job.query.QueryJobExport the StreamingDataFrame as a continue job and returns a QueryJob object for some management functionality.
See more: bigframes.streaming.dataframe.StreamingDataFrame.to_bigtable
bigframes.streaming.dataframe.StreamingDataFrame.to_pubsub
to_pubsub( *, topic: str, service_account_email: str, job_id: typing.Optional[str] = None, job_id_prefix: typing.Optional[str] = None, start_timestamp: typing.Optional[ typing.Union[int, float, str, datetime.datetime, datetime.date] ] = None ) -> google.cloud.bigquery.job.query.QueryJobExport the StreamingDataFrame as a continue job and returns a QueryJob object for some management functionality.
See more: bigframes.streaming.dataframe.StreamingDataFrame.to_pubsub