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DOC: fix DataFrame.quantile docstring and doctests #23936

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Merged
jreback merged 3 commits into from
Dec 9, 2018
Merged

DOC: fix DataFrame.quantile docstring and doctests #23936

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0f6592f
DOC: fix pandas.DataFrame.quantile docstring and doctests
Moisan Nov 27, 2018
d874e14
Merge branch 'master' into doctest_quantile
Moisan Dec 8, 2018
2c972ba
Merge branch 'master' of https://github.com/pandas-dev/pandas into do…
Moisan Dec 8, 2018
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2 changes: 1 addition & 1 deletion ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -175,7 +175,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then

MSG='Doctests frame.py' ; echo $MSG
pytest -q --doctest-modules pandas/core/frame.py \
-k"-axes -combine -itertuples -join -pivot_table -quantile -query -reindex -reindex_axis -round"
-k"-axes -combine -itertuples -join -pivot_table -query -reindex -reindex_axis -round"
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Doctests series.py' ; echo $MSG
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29 changes: 14 additions & 15 deletions pandas/core/frame.py
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Expand Up @@ -7463,15 +7463,13 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute
Value between 0 <= q <= 1, the quantile(s) to compute.
axis : {0, 1, 'index', 'columns'} (default 0)
0 or 'index' for row-wise, 1 or 'columns' for column-wise
numeric_only : boolean, default True
Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
numeric_only : bool, default True
If False, the quantile of datetime and timedelta data will be
computed as well
computed as well.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
.. versionadded:: 0.18.0

This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:

Expand All @@ -7482,6 +7480,8 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
* nearest: `i` or `j` whichever is nearest.
* midpoint: (`i` + `j`) / 2.

.. versionadded:: 0.18.0

Returns
-------
quantiles : Series or DataFrame
Expand All @@ -7494,18 +7494,17 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,

See Also
--------
pandas.core.window.Rolling.quantile
numpy.percentile
core.window.Rolling.quantile: Rolling quantile.
numpy.percentile: Numpy function to compute the percentile.

Examples
--------

>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
columns=['a', 'b'])
... columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
dtype: float64
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
Expand All @@ -7515,10 +7514,10 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
'B': [pd.Timestamp('2010'),
pd.Timestamp('2011')],
'C': [pd.Timedelta('1 days'),
pd.Timedelta('2 days')]})
... 'B': [pd.Timestamp('2010'),
... pd.Timestamp('2011')],
... 'C': [pd.Timedelta('1 days'),
... pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A 1.5
B 2010-07-02 12:00:00
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