mars.dataframe.DataFrame.quantile#

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

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.

Returns

If q is an array or a tensor, 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 type

Series or DataFrame

See also

core.window.Rolling.quantile

Rolling quantile.

numpy.percentile

Numpy function to compute the percentile.

Examples

>>> import mars.dataframe as md
>>> df = md.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df.quantile(.1).execute()
a    1.3
b    3.7
Name: 0.1, dtype: float64

>>> df.quantile([.1, .5]).execute()
       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 = md.DataFrame({'A': [1, 2],
...                    'B': [md.Timestamp('2010'),
...                          md.Timestamp('2011')],
...                    'C': [md.Timedelta('1 days'),
...                          md.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False).execute()
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object