xorbits.pandas.DataFrame.quantile#

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

Return values at the given quantile over requested axis.

参数

  • q (float or array-like, default 0.5 (50% quantile)) – Value between 0 <= q <= 1, the quantile(s) to compute.

  • axis ({0 or 'index', 1 or 'columns'}, default 0) – Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

  • numeric_only (bool, default False) –

    Include only float, int or boolean data.

    在 2.0.0(pandas) 版更改: The default value of numeric_only is now False.

  • 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.

  • method ({'single', 'table'}, default 'single' (Not supported yet)) – Whether to compute quantiles per-column (‘single’) or over all columns (‘table’). When ‘table’, the only allowed interpolation methods are ‘nearest’, ‘lower’, and ‘higher’.

返回

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.

返回类型

Series or DataFrame

参见

core.window.rolling.Rolling.quantile

Rolling quantile.

numpy.percentile

Numpy function to compute the percentile.

实际案例

>>> 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 method=’table’ will compute the quantile over all columns.

>>> df.quantile(.1, method="table", interpolation="nearest")  
a    1
b    1
Name: 0.1, dtype: int64
>>> df.quantile([.1, .5], method="table", interpolation="nearest")  
     a    b
0.1  1    1
0.5  3  100

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

This docstring was copied from pandas.core.frame.DataFrame.