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DOC: update the DataFrame.count docstring #20221

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Merged
merged 5 commits into from
Mar 12, 2018
Merged

DOC: update the DataFrame.count docstring #20221

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83a7c88
DataFrame.count docstring reworked, examples added
joders Mar 10, 2018
8d76f60
review corrections
joders Mar 10, 2018
fd11167
review fixes + better description
joders Mar 11, 2018
bbe96aa
review fixes, full stop and backticks
joders Mar 11, 2018
dbb84eb
review fix: changed summary, added quoting
joders Mar 12, 2018
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62 changes: 54 additions & 8 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5592,22 +5592,68 @@ def corrwith(self, other, axis=0, drop=False):

def count(self, axis=0, level=None, numeric_only=False):
"""
Return Series with number of non-NA/null observations over requested
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
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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.

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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.

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

axis. Works with non-floating point data as well (detects `None`,
`NaN` and `NaT`)
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End with a .


Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
0 or 'index' for row-wise, 1 or 'columns' for column-wise
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a DataFrame
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`.
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backticks around the `level` parameter.

A `str` specifies the level name.
numeric_only : boolean, default False
Include only float, int, boolean data
Include only `float`, `int` or `boolean` data.

Returns
-------
count : Series (or DataFrame if level specified)
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
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refer to isna instead

elements

Examples
--------
>>> df = pd.DataFrame({"Person":
... ["John", "Myla", None, "John", "Myla"],
... "Age": [24., np.nan, 21., 33, 26],
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PEP8: indendt one more space. smae with line below.

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@joders joders Mar 11, 2018 •
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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'

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Sorry I misread.

... "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
>>> df.count()
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blank line between cases

Person 4
Age 4
Single 5
dtype: int64
>>> df.count(axis=1)
0 3
1 2
2 2
3 3
4 3
dtype: int64
>>> df.set_index(["Person", "Single"]).count(level="Person")
Age
Person
John 2
Myla 1
"""
axis = self._get_axis_number(axis)
if level is not None:
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