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Classe « DataFrame »

Méthode pandas.DataFrame.quantile

Signature de la méthode quantile

def quantile(self, q: 'float | AnyArrayLike | Sequence[float]' = 0.5, axis: 'Axis' = 0, numeric_only: 'bool' = False, interpolation: 'QuantileInterpolation' = 'linear', method: "Literal['single', 'table']" = 'single') -> 'Series | DataFrame' 

Description

help(DataFrame.quantile)

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 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.

    .. versionchanged:: 2.0.0
        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'
    Whether to compute quantiles per-column ('single') or over all columns
    ('table'). When 'table', the only allowed interpolation methods are
    'nearest', 'lower', and 'higher'.

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.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 `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

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