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pandas.DataFrame.quantile

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

Return values at the given quantile over requested axis.

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 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.

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

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'])
>>> 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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