DataFrame.quantile(self, q=0.5, axis=0, numeric_only=True, interpolation='linear')[source]

Return values at the given quantile over requested axis.

qfloat 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_onlybool, 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 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.

New in version 0.18.0.

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


Rolling quantile.


Numpy function to compute the percentile.


>>> 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
>>> 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
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