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
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.

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.

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
pandas.DataFrame.product pandas.DataFrame.query