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Module « scipy.stats »

Fonction mode - module scipy.stats

Signature de la fonction mode

def mode(a, axis=0, nan_policy='propagate', keepdims=False) 

Description

help(scipy.stats.mode)

    


Return an array of the modal (most common) value in the passed array.

If there is more than one such value, only one is returned.
The bin-count for the modal bins is also returned.

Parameters
----------
a : array_like
    Numeric, n-dimensional array of which to find mode(s).
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

Returns
-------
mode : ndarray
    Array of modal values.
count : ndarray
    Array of counts for each mode.

Notes
-----
The mode  is calculated using `numpy.unique`.
In NumPy versions 1.21 and after, all NaNs - even those with different
binary representations - are treated as equivalent and counted as separate
instances of the same value.

By convention, the mode of an empty array is NaN, and the associated count
is zero.

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

Examples
--------
>>> import numpy as np
>>> a = np.array([[3, 0, 3, 7],
...               [3, 2, 6, 2],
...               [1, 7, 2, 8],
...               [3, 0, 6, 1],
...               [3, 2, 5, 5]])
>>> from scipy import stats
>>> stats.mode(a, keepdims=True)
ModeResult(mode=array([[3, 0, 6, 1]]), count=array([[4, 2, 2, 1]]))

To get mode of whole array, specify ``axis=None``:

>>> stats.mode(a, axis=None, keepdims=True)
ModeResult(mode=[[3]], count=[[5]])
>>> stats.mode(a, axis=None, keepdims=False)
ModeResult(mode=3, count=5)

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