DOC: Fix code block line length (#36773)

Author: dsaxton

doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

@@ -123,7 +123,10 @@ aggregating statistics for given columns can be defined using the
 .. ipython:: python
 
  titanic.agg(
- {"Age": ["min", "max", "median", "skew"], "Fare": ["min", "max", "median", "mean"]}
+ {
+ "Age": ["min", "max", "median", "skew"],
+ "Fare": ["min", "max", "median", "mean"],
+ }
  )
 
 .. raw:: html

doc/source/user_guide/advanced.rst

@@ -304,7 +304,8 @@ whereas a tuple of lists refer to several values within a level:
 .. ipython:: python
 
  s = pd.Series(
- [1, 2, 3, 4, 5, 6], index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]])
+ [1, 2, 3, 4, 5, 6],
+ index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]]),
  )
  s.loc[[("A", "c"), ("B", "d")]] # list of tuples
  s.loc[(["A", "B"], ["c", "d"])] # tuple of lists
@@ -819,7 +820,9 @@ values **not** in the categories, similarly to how you can reindex **any** panda
 
 .. ipython:: python
 
- df3 = pd.DataFrame({"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")})
+ df3 = pd.DataFrame(
+ {"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")}
+ )
  df3 = df3.set_index("B")
  df3
 
@@ -934,7 +937,9 @@ example, be millisecond offsets.
  np.random.randn(5, 2), index=np.arange(5) * 250.0, columns=list("AB")
  ),
  pd.DataFrame(
- np.random.randn(6, 2), index=np.arange(4, 10) * 250.1, columns=list("AB")
+ np.random.randn(6, 2),
+ index=np.arange(4, 10) * 250.1,
+ columns=list("AB"),
  ),
  ]
  )

doc/source/user_guide/basics.rst

@@ -464,7 +464,10 @@ which we illustrate:
  {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
  )
  df2 = pd.DataFrame(
- {"A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0], "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0]}
+ {
+ "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0],
+ "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0],
+ }
  )
  df1
  df2
@@ -712,7 +715,10 @@ Similarly, you can get the most frequently occurring value(s), i.e. the mode, of
  s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
  s5.mode()
  df5 = pd.DataFrame(
- {"A": np.random.randint(0, 7, size=50), "B": np.random.randint(-10, 15, size=50)}
+ {
+ "A": np.random.randint(0, 7, size=50),
+ "B": np.random.randint(-10, 15, size=50),
+ }
  )
  df5.mode()
 
@@ -1192,7 +1198,9 @@ to :ref:`merging/joining functionality <merging>`:
 
 .. ipython:: python
 
- s = pd.Series(["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"])
+ s = pd.Series(
+ ["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"]
+ )
  t = pd.Series({"six": 6.0, "seven": 7.0})
  s
  s.map(t)
@@ -1494,7 +1502,9 @@ labels).
 
  df = pd.DataFrame(
  {"x": [1, 2, 3, 4, 5, 6], "y": [10, 20, 30, 40, 50, 60]},
- index=pd.MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["let", "num"]),
+ index=pd.MultiIndex.from_product(
+ [["a", "b", "c"], [1, 2]], names=["let", "num"]
+ ),
  )
  df
  df.rename_axis(index={"let": "abc"})
@@ -1803,7 +1813,9 @@ used to sort a pandas object by its index levels.
  }
  )
 
- unsorted_df = df.reindex(index=["a", "d", "c", "b"], columns=["three", "two", "one"])
+ unsorted_df = df.reindex(
+ index=["a", "d", "c", "b"], columns=["three", "two", "one"]
+ )
  unsorted_df
 
  # DataFrame
@@ -1849,7 +1861,9 @@ to use to determine the sorted order.
 
 .. ipython:: python
 
- df1 = pd.DataFrame({"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]})
+ df1 = pd.DataFrame(
+ {"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]}
+ )
  df1.sort_values(by="two")
 
 The ``by`` parameter can take a list of column names, e.g.:
@@ -1994,7 +2008,9 @@ all levels to ``by``.
 
 .. ipython:: python
 
- df1.columns = pd.MultiIndex.from_tuples([("a", "one"), ("a", "two"), ("b", "three")])
+ df1.columns = pd.MultiIndex.from_tuples(
+ [("a", "one"), ("a", "two"), ("b", "three")]
+ )
  df1.sort_values(by=("a", "two"))
 
 
@@ -2245,7 +2261,11 @@ to the correct type.
  import datetime
 
  df = pd.DataFrame(
- [[1, 2], ["a", "b"], [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)]]
+ [
+ [1, 2],
+ ["a", "b"],
+ [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)],
+ ]
  )
  df = df.T
  df

doc/source/user_guide/categorical.rst

@@ -513,7 +513,11 @@ The ordering of the categorical is determined by the ``categories`` of that colu
 
  dfs = pd.DataFrame(
  {
- "A": pd.Categorical(list("bbeebbaa"), categories=["e", "a", "b"], ordered=True),
+ "A": pd.Categorical(
+ list("bbeebbaa"),
+ categories=["e", "a", "b"],
+ ordered=True,
+ ),
  "B": [1, 2, 1, 2, 2, 1, 2, 1],
  }
  )
@@ -642,7 +646,13 @@ Groupby will also show "unused" categories:
  df.groupby("cats").mean()
 
  cats2 = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])
- df2 = pd.DataFrame({"cats": cats2, "B": ["c", "d", "c", "d"], "values": [1, 2, 3, 4]})
+ df2 = pd.DataFrame(
+ {
+ "cats": cats2,
+ "B": ["c", "d", "c", "d"],
+ "values": [1, 2, 3, 4],
+ }
+ )
  df2.groupby(["cats", "B"]).mean()
 
 
@@ -1115,7 +1125,11 @@ You can use ``fillna`` to handle missing values before applying a function.
 .. ipython:: python
 
  df = pd.DataFrame(
- {"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"], "cats": pd.Categorical([1, 2, 3, 2])}
+ {
+ "a": [1, 2, 3, 4],
+ "b": ["a", "b", "c", "d"],
+ "cats": pd.Categorical([1, 2, 3, 2]),
+ }
  )
  df.apply(lambda row: type(row["cats"]), axis=1)
  df.apply(lambda col: col.dtype, axis=0)

doc/source/user_guide/computation.rst

@@ -787,7 +787,11 @@ can even be omitted:
 
 .. ipython:: python
 
- covs = df[["B", "C", "D"]].rolling(window=50).cov(df[["A", "B", "C"]], pairwise=True)
+ covs = (
+ df[["B", "C", "D"]]
+ .rolling(window=50)
+ .cov(df[["A", "B", "C"]], pairwise=True)
+ )
  covs.loc["2002-09-22":]
 
 .. ipython:: python

doc/source/user_guide/cookbook.rst

@@ -266,7 +266,9 @@ New columns
 
 .. ipython:: python
 
- df = pd.DataFrame({"AAA": [1, 1, 1, 2, 2, 2, 3, 3], "BBB": [2, 1, 3, 4, 5, 1, 2, 3]})
+ df = pd.DataFrame(
+ {"AAA": [1, 1, 1, 2, 2, 2, 3, 3], "BBB": [2, 1, 3, 4, 5, 1, 2, 3]}
+ )
  df
 
 Method 1 : idxmin() to get the index of the minimums
@@ -327,7 +329,9 @@ Arithmetic
 
 .. ipython:: python
 
- cols = pd.MultiIndex.from_tuples([(x, y) for x in ["A", "B", "C"] for y in ["O", "I"]])
+ cols = pd.MultiIndex.from_tuples(
+ [(x, y) for x in ["A", "B", "C"] for y in ["O", "I"]]
+ )
  df = pd.DataFrame(np.random.randn(2, 6), index=["n", "m"], columns=cols)
  df
  df = df.div(df["C"], level=1)
@@ -566,7 +570,9 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
 
 .. ipython:: python
 
- df = pd.DataFrame({"Color": "Red Red Red Blue".split(), "Value": [100, 150, 50, 50]})
+ df = pd.DataFrame(
+ {"Color": "Red Red Red Blue".split(), "Value": [100, 150, 50, 50]}
+ )
  df
  df["Counts"] = df.groupby(["Color"]).transform(len)
  df
@@ -648,7 +654,10 @@ Create a list of dataframes, split using a delineation based on logic included i
  dfs = list(
  zip(
  *df.groupby(
- (1 * (df["Case"] == "B")).cumsum().rolling(window=3, min_periods=1).median()
+ (1 * (df["Case"] == "B"))
+ .cumsum()
+ .rolling(window=3, min_periods=1)
+ .median()
  )
  )
  )[-1]
@@ -740,7 +749,18 @@ The :ref:`Pivot <reshaping.pivot>` docs.
  "yes",
  ],
  "Passed": ["yes" if x > 50 else "no" for x in grades],
- "Employed": [True, True, True, False, False, False, False, True, True, False],
+ "Employed": [
+ True,
+ True,
+ True,
+ False,
+ False,
+ False,
+ False,
+ True,
+ True,
+ False,
+ ],
  "Grade": grades,
  }
  )
@@ -791,7 +811,9 @@ Apply
  return pd.Series(aList)
 
 
- df_orgz = pd.concat({ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()})
+ df_orgz = pd.concat(
+ {ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()}
+ )
  df_orgz
 
 `Rolling apply with a DataFrame returning a Series
@@ -1162,7 +1184,12 @@ Option 1: pass rows explicitly to skip rows
  from io import StringIO
 
  pd.read_csv(
- StringIO(data), sep=";", skiprows=[11, 12], index_col=0, parse_dates=True, header=10
+ StringIO(data),
+ sep=";",
+ skiprows=[11, 12],
+ index_col=0,
+ parse_dates=True,
+ header=10,
  )
 
 Option 2: read column names and then data

doc/source/user_guide/groupby.rst

@@ -267,7 +267,9 @@ the length of the ``groups`` dict, so it is largely just a convenience:
  height = np.random.normal(60, 10, size=n)
  time = pd.date_range("1/1/2000", periods=n)
  gender = np.random.choice(["male", "female"], size=n)
- df = pd.DataFrame({"height": height, "weight": weight, "gender": gender}, index=time)
+ df = pd.DataFrame(
+ {"height": height, "weight": weight, "gender": gender}, index=time
+ )
 
 .. ipython:: python
 
@@ -767,7 +769,10 @@ For example, suppose we wished to standardize the data within each group:
  ts.head()
  ts.tail()
 
- transformed = ts.groupby(lambda x: x.year).transform(lambda x: (x - x.mean()) / x.std())
+ transformed = ts.groupby(lambda x: x.year).transform(
+ lambda x: (x - x.mean()) / x.std()
+ )
+
 
 We would expect the result to now have mean 0 and standard deviation 1 within
 each group, which we can easily check: