DOC: update the DataFrame.count docstring #20221

joders · 2018-03-10T19:17:07Z

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

PR title is "DOC: update the docstring"
The validation script passes: scripts/validate_docstrings.py <your-function-or-method>
The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
The html version looks good: python doc/make.py --single <your-function-or-method>
It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

################################################################################ ###################### Docstring (pandas.DataFrame.count) ###################### ################################################################################ Count non-NA cells for each column or row. Return Series with number of non-NA observations over requested axis. Works with non-floating point data as well (detects `NaN` and `None`) Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 If equal 0 or 'index' counts are generated for each column. If equal 1 or 'columns' counts are generated for each row. level : int or str, optional If the axis is a `MultiIndex` (hierarchical), count along a particular level, collapsing into a `DataFrame`. A `str` specifies the level name. numeric_only : boolean, default False Include only `float`, `int` or `boolean` data. Returns ------- Series or DataFrame For each column/row the number of non-NA/null entries. If level is specified returns a `DataFrame`. See Also -------- Series.count: number of non-NA elements in a Series DataFrame.shape: number of DataFrame rows and columns (including NA elements) DataFrame.isnull: boolean same-sized DataFrame showing places of NA elements Examples -------- >>> df=pd.DataFrame({ "Person":["John","Myla",None], ... "Age":[24.,np.nan,21.], ... "Single":[False,True,True] }) >>> df Person Age Single 0 John 24.0 False 1 Myla NaN True 2 None 21.0 True >>> df.count() Person 2 Age 2 Single 3 dtype: int64 >>> df.count(axis=1) 0 3 1 2 2 2 dtype: int64 ################################################################################ ################################## Validation ################################## ################################################################################ Docstring for "pandas.DataFrame.count" correct. :)

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.

TomAugspurger · 2018-03-10T19:41:53Z

pandas/core/frame.py

+
+ Return Series with number of non-NA observations over requested
+ axis. Works with non-floating point data as well (detects `NaN` and
+ `None`)


Could you also add NaT here (that's our missing value for datetime data)

TomAugspurger · 2018-03-10T19:42:28Z

pandas/core/frame.py

- level : int or level name, default None
- If the axis is a MultiIndex (hierarchical), count along a
- particular level, collapsing into a DataFrame
+ If equal 0 or 'index' counts are generated for each column.


I think you can remove "equal" from this line and the next.

TomAugspurger · 2018-03-10T19:46:01Z

pandas/core/frame.py

+
+ Examples
+ --------
+ >>> df=pd.DataFrame({ "Person":["John","Myla",None],


Pep8 on this example. space around =, no space after {, space after :, space after ,.

Could you also add an example with level=? I think you could

Make the dataframe 2 items longer and repeat John an Myla.

Update the df output and df.count examples

show df.set_index(['Person', 'Single']).count(level='Person')

jreback · 2018-03-11T14:40:03Z

pandas/core/frame.py

+ Series.count: number of non-NA elements in a Series
+ DataFrame.shape: number of DataFrame rows and columns (including NA
+ elements)
+ DataFrame.isnull: boolean same-sized DataFrame showing places of NA


refer to isna instead

jreback · 2018-03-11T14:40:13Z

pandas/core/frame.py

+ 2 None 21.0 True
+ 3 John 33.0 True
+ 4 Myla 26.0 False
+ >>> df.count()


blank line between cases

TomAugspurger

Can you paste the output of the doc validation script again?

TomAugspurger · 2018-03-11T16:32:41Z

pandas/core/frame.py

+
+ Return Series with number of non-NA observations over requested
+ axis. Works with non-floating point data as well (detects `None`,
+ `NaN` and `NaT`)


End with a .

TomAugspurger · 2018-03-11T16:32:53Z

pandas/core/frame.py

+ If 1 or 'columns' counts are generated for each **row**.
+ level : int or str, optional
+ If the axis is a `MultiIndex` (hierarchical), count along a
+ particular level, collapsing into a `DataFrame`.


backticks around the `level` parameter.

joders · 2018-03-11T17:12:22Z

################################################################################ ###################### Docstring (pandas.DataFrame.count) ###################### ################################################################################ Count non-NA cells for each column or row. Return Series with number of non-NA observations over requested axis. Works with non-floating point data as well (detects `None`, `NaN` and `NaT`). Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 If 0 or 'index' counts are generated for each column. If 1 or 'columns' counts are generated for each **row**. level : int or str, optional If the axis is a `MultiIndex` (hierarchical), count along a particular `level`, collapsing into a `DataFrame`. A `str` specifies the level name. numeric_only : boolean, default False Include only `float`, `int` or `boolean` data. Returns ------- Series or DataFrame For each column/row the number of non-NA/null entries. If `level` is specified returns a `DataFrame`. See Also -------- Series.count: number of non-NA elements in a Series DataFrame.shape: number of DataFrame rows and columns (including NA elements) DataFrame.isna: boolean same-sized DataFrame showing places of NA elements Examples -------- Constructing DataFrame from a dictionary: >>> df = pd.DataFrame({"Person": ... ["John", "Myla", None, "John", "Myla"], ... "Age": [24., np.nan, 21., 33, 26], ... "Single": [False, True, True, True, False]}) >>> df Person Age Single 0 John 24.0 False 1 Myla NaN True 2 None 21.0 True 3 John 33.0 True 4 Myla 26.0 False Notice the uncounted NA values: >>> df.count() Person 4 Age 4 Single 5 dtype: int64 Counts for each **row**: >>> df.count(axis='columns') 0 3 1 2 2 2 3 3 4 3 dtype: int64 Counts for one level of a `MultiIndex`: >>> df.set_index(["Person", "Single"]).count(level="Person") Age Person John 2 Myla 1 ################################################################################ ################################## Validation ################################## ################################################################################ Docstring for "pandas.DataFrame.count" correct. :)

TomAugspurger · 2018-03-11T17:15:35Z

pandas/core/frame.py

- axis. Works with non-floating point data as well (detects NaN and None)
+ Count non-NA cells for each column or row.
+
+ Return Series with number of non-NA observations over requested


One last change, maybe remove the first sentence since this can return a DataFrame with level.

I think just use the extended summary to say what counts as non-null data.

The values None, NaN, NaT, and optionally np.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Do you mean the first sentence in the extended summary, i.e. :
"Return Series with number of non-NA observations over requested axis."

If I understand you right I would change the entire summary (i.e. short and extended summary) to look like the following:

Count non-NA cells for each column or row. The values None, NaN, NaT, and optionally np.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Yeah. np.inf to `numpy.inf` and single backticks around pandas.options.mode.use_inf_as_na.

TomAugspurger · 2018-03-11T17:15:51Z

pandas/core/frame.py

+
+ >>> df = pd.DataFrame({"Person":
+ ... ["John", "Myla", None, "John", "Myla"],
+ ... "Age": [24., np.nan, 21., 33, 26],


PEP8: indendt one more space. smae with line below.

For me flake complains if I change that. on my system flake doesn't check the examples, so I copy it in the code:

df = pd.DataFrame({"Person": ["John", "Myla", None, "John", "Myla"], "Age": [24., np.nan, 21., 33, 26], "Single": [False, True, True, True, False]}) df

If I have it like it like this flake only complains about the pd not being defined:
pandas/core/frame.py:5672:14: F821 undefined name 'pd'

Sorry I misread.

joders · 2018-03-12T13:15:50Z

################################################################################ ###################### Docstring (pandas.DataFrame.count) ###################### ################################################################################ Count non-NA cells for each column or row. The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending on `pandas.options.mode.use_inf_as_na`) are considered NA. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 If 0 or 'index' counts are generated for each column. If 1 or 'columns' counts are generated for each **row**. level : int or str, optional If the axis is a `MultiIndex` (hierarchical), count along a particular `level`, collapsing into a `DataFrame`. A `str` specifies the level name. numeric_only : boolean, default False Include only `float`, `int` or `boolean` data. Returns ------- Series or DataFrame For each column/row the number of non-NA/null entries. If `level` is specified returns a `DataFrame`. See Also -------- Series.count: number of non-NA elements in a Series DataFrame.shape: number of DataFrame rows and columns (including NA elements) DataFrame.isna: boolean same-sized DataFrame showing places of NA elements Examples -------- Constructing DataFrame from a dictionary: >>> df = pd.DataFrame({"Person": ... ["John", "Myla", None, "John", "Myla"], ... "Age": [24., np.nan, 21., 33, 26], ... "Single": [False, True, True, True, False]}) >>> df Person Age Single 0 John 24.0 False 1 Myla NaN True 2 None 21.0 True 3 John 33.0 True 4 Myla 26.0 False Notice the uncounted NA values: >>> df.count() Person 4 Age 4 Single 5 dtype: int64 Counts for each **row**: >>> df.count(axis='columns') 0 3 1 2 2 2 3 3 4 3 dtype: int64 Counts for one level of a `MultiIndex`: >>> df.set_index(["Person", "Single"]).count(level="Person") Age Person John 2 Myla 1 ################################################################################ ################################## Validation ################################## ################################################################################ Docstring for "pandas.DataFrame.count" correct. :)

TomAugspurger · 2018-03-12T14:24:54Z

Thanks @joders!

joders · 2018-03-13T20:00:49Z

thanks for providing pandas

DataFrame.count docstring reworked, examples added

83a7c88

aterrel approved these changes Mar 10, 2018

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TomAugspurger added the Docs label Mar 10, 2018

TomAugspurger reviewed Mar 10, 2018

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review corrections

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jreback requested changes Mar 11, 2018

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review fixes + better description

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TomAugspurger reviewed Mar 11, 2018

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review fixes, full stop and backticks

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TomAugspurger reviewed Mar 11, 2018

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review fix: changed summary, added quoting

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TomAugspurger added this to the 0.23.0 milestone Mar 12, 2018

TomAugspurger merged commit 0596cb1 into pandas-dev:master Mar 12, 2018

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