quantile(self, q=0.5, axis: 'Axis' = 0, numeric_only: 'bool' = True, interpolation: 'str' = 'linear')

Parameters

q : float or array-like, default 0.5 (50% quantile)

Value between 0 <= q <= 1, the quantile(s) to compute.

axis : {0, 1, 'index', 'columns'}, default 0

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.

interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}

This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and :None:None:`j`:

• linear: :None:None:`i + (j - i) * fraction`, where :None:None:`fraction` is the fractional part of the index surrounded by i and :None:None:`j`.

• lower: i.

• higher: :None:None:`j`.

• nearest: i or :None:None:`j` whichever is nearest.

• midpoint: (i + :None:None:`j`) / 2.

Returns

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.

Return values at the given quantile over requested axis.

See Also

core.window.Rolling.quantile

Rolling quantile.

numpy.percentile

Numpy function to compute the percentile.

Examples

This example is valid syntax, but we were not able to check execution

>>> 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
Name: 0.1, dtype: float64

This example is valid syntax, but we were not able to check execution

>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying :None:None:`numeric_only=False` will also compute the quantile of datetime and timedelta data.

This example is valid syntax, but we were not able to check execution

>>> 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 :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.window.expanding.Expanding.quantile pandas.core.groupby.groupby.GroupBy.quantile pandas.core.window.rolling.Rolling.quantile

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