Classe « DataFrame »
Signature de la méthode quantile
def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation='linear')
Description
quantile.__doc__
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
-------
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
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