`scipy.stats._stats_py:mode`

source: /scipy/stats/_stats_py.py :482

Signature

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

Summary

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

Extended Summary

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.

Array API Standard Support

mode has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ✅                   
JAX                   ⛔                     ⛔                   
Dask                  ⛔                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

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)

✓

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

stats.mode(a, axis=None, keepdims=True)
stats.mode(a, axis=None, keepdims=False)