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DOC: Add numeric_only to DataFrame.quantile #21214

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May 29, 2018
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DOC: Add numeric_only to DataFrame.quantile #21214

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19 changes: 18 additions & 1 deletion pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7089,6 +7089,9 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
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
If False, the quantile of datetime and timedelta data will be
computed as well
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
.. versionadded:: 0.18.0

Expand Down Expand Up @@ -7116,7 +7119,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
--------

>>> 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
Expand All @@ -7126,6 +7129,20 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
0.1 1.3 3.7
0.5 2.5 55.0

Specifying `numeric_only=False` will also compute the quantile of
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')]})
>>> df.quantile(0.5, numeric_only=False)
A 1.5
B 2010-07-02 12:00:00
C 1 days 12:00:00
Name: 0.5, dtype: object

See Also
--------
pandas.core.window.Rolling.quantile
Expand Down