DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear', columns=None, exact=True)#

Return values at the given quantile.

qfloat or array-like

0 <= q <= 1, the quantile(s) to compute


axis is a NON-FUNCTIONAL parameter

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 parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j. Default linear.

columnslist of str

List of column names to include.


Whether to use approximate or exact quantile algorithm.

Series or DataFrame

If q is an array or numeric_only is set to False, a DataFrame will be returned where index is q, the columns are the columns of self, and the values are the quantile.

If q is a float, a Series will be returned where the index is the columns of self and the values are the quantiles.


One notable difference from Pandas is when DataFrame is of non-numeric types and result is expected to be a Series in case of Pandas. cuDF will return a DataFrame as it doesn’t support mixed types under Series.


>>> import cupy as cp
>>> import cudf
>>> df = cudf.DataFrame(cp.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df
   a    b
0  1    1
1  2   10
2  3  100
3  4  100
>>> df.quantile(0.1)
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