pandas.core.groupby.DataFrameGroupBy.quantile

DataFrameGroupBy.quantile

Return values at the given quantile over requested axis, a la numpy.percentile.

Parameters:

q : float or array-like, default 0.5 (50% quantile) 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’} New in version 0.18.0. This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j: linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j. lower: i. higher: j. nearest: i or j whichever is nearest. midpoint: (i + j) / 2.

Returns:

quantiles : Series or DataFrame If q is an array, a DataFrame will be returned where the index is q, the columns are the columns of self, and the values are the quantiles. If q is a float, a Series will be returned where the index is the columns of self and the values are the quantiles.

See also

pandas.core.window.Rolling.quantile

Examples

>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
                      columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
dtype: float64
>>> df.quantile([.1, .5])
       a     b
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