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Module « numpy.matlib »

Fonction histogram - module numpy.matlib

Signature de la fonction histogram

def histogram(a, bins=10, range=None, density=None, weights=None) 

Description

help(numpy.matlib.histogram)

Compute the histogram of a dataset.

Parameters
----------
a : array_like
    Input data. The histogram is computed over the flattened array.
bins : int or sequence of scalars or str, optional
    If `bins` is an int, it defines the number of equal-width
    bins in the given range (10, by default). If `bins` is a
    sequence, it defines a monotonically increasing array of bin edges,
    including the rightmost edge, allowing for non-uniform bin widths.

    If `bins` is a string, it defines the method used to calculate the
    optimal bin width, as defined by `histogram_bin_edges`.

range : (float, float), optional
    The lower and upper range of the bins.  If not provided, range
    is simply ``(a.min(), a.max())``.  Values outside the range are
    ignored. The first element of the range must be less than or
    equal to the second. `range` affects the automatic bin
    computation as well. While bin width is computed to be optimal
    based on the actual data within `range`, the bin count will fill
    the entire range including portions containing no data.
weights : array_like, optional
    An array of weights, of the same shape as `a`.  Each value in
    `a` only contributes its associated weight towards the bin count
    (instead of 1). If `density` is True, the weights are
    normalized, so that the integral of the density over the range
    remains 1.
    Please note that the ``dtype`` of `weights` will also become the
    ``dtype`` of the returned accumulator (`hist`), so it must be
    large enough to hold accumulated values as well.
density : bool, optional
    If ``False``, the result will contain the number of samples in
    each bin. If ``True``, the result is the value of the
    probability *density* function at the bin, normalized such that
    the *integral* over the range is 1. Note that the sum of the
    histogram values will not be equal to 1 unless bins of unity
    width are chosen; it is not a probability *mass* function.

Returns
-------
hist : array
    The values of the histogram. See `density` and `weights` for a
    description of the possible semantics.  If `weights` are given,
    ``hist.dtype`` will be taken from `weights`.
bin_edges : array of dtype float
    Return the bin edges ``(length(hist)+1)``.


See Also
--------
histogramdd, bincount, searchsorted, digitize, histogram_bin_edges

Notes
-----
All but the last (righthand-most) bin is half-open.  In other words,
if `bins` is::

  [1, 2, 3, 4]

then the first bin is ``[1, 2)`` (including 1, but excluding 2) and
the second ``[2, 3)``.  The last bin, however, is ``[3, 4]``, which
*includes* 4.


Examples
--------
>>> import numpy as np
>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
(array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))

>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, density=True)
>>> hist
array([0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])
>>> hist.sum()
2.4999999999999996
>>> np.sum(hist * np.diff(bin_edges))
1.0

Automated Bin Selection Methods example, using 2 peak random data
with 2000 points.

.. plot::
    :include-source:

    import matplotlib.pyplot as plt
    import numpy as np

    rng = np.random.RandomState(10)  # deterministic random data
    a = np.hstack((rng.normal(size=1000),
                   rng.normal(loc=5, scale=2, size=1000)))
    plt.hist(a, bins='auto')  # arguments are passed to np.histogram
    plt.title("Histogram with 'auto' bins")
    plt.show()

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