call signature::
hist(x, bins=10, range=None, normed=False, cumulative=False,
bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False, **kwargs)
Compute and draw the histogram of *x*. The return value is a
tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,
[*patches0*, *patches1*,...]) if the input contains multiple
data.
Multiple data can be provided via *x* as a list of datasets
of potentially different length ([*x0*, *x1*, ...]), or as
a 2-D ndarray in which each column is a dataset. Note that
the ndarray form is transposed relative to the list form.
Masked arrays are not supported at present.
Keyword arguments:
*bins*:
Either an integer number of bins or a sequence giving the
bins. If *bins* is an integer, *bins* + 1 bin edges
will be returned, consistent with :func:`numpy.histogram`
for numpy version >= 1.3, and with the *new* = True argument
in earlier versions.
Unequally spaced bins are supported if *bins* is a sequence.
*range*:
The lower and upper range of the bins. Lower and upper outliers
are ignored. If not provided, *range* is (x.min(), x.max()).
Range has no effect if *bins* is a sequence.
If *bins* is a sequence or *range* is specified, autoscaling
is based on the specified bin range instead of the
range of x.
*normed*:
If *True*, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
``n/(len(x)*dbin)``. In a probability density, the integral of
the histogram should be 1; you can verify that with a
trapezoidal integration of the probability density function::
pdf, bins, patches = ax.hist(...)
print np.sum(pdf * np.diff(bins))
.. Note:: Until numpy release 1.5, the underlying numpy
histogram function was incorrect with *normed*=*True*
if bin sizes were unequal. MPL inherited that
error. It is now corrected within MPL when using
earlier numpy versions
*weights*
An array of weights, of the same shape as *x*. Each value in
*x* only contributes its associated weight towards the bin
count (instead of 1). If *normed* is True, the weights are
normalized, so that the integral of the density over the range
remains 1.
*cumulative*:
If *True*, then a histogram is computed where each bin
gives the counts in that bin plus all bins for smaller values.
The last bin gives the total number of datapoints. If *normed*
is also *True* then the histogram is normalized such that the
last bin equals 1. If *cumulative* evaluates to less than 0
(e.g. -1), the direction of accumulation is reversed. In this
case, if *normed* is also *True*, then the histogram is normalized
such that the first bin equals 1.
*histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ]
The type of histogram to draw.
- 'bar' is a traditional bar-type histogram. If multiple data
are given the bars are aranged side by side.
- 'barstacked' is a bar-type histogram where multiple
data are stacked on top of each other.
- 'step' generates a lineplot that is by default
unfilled.
- 'stepfilled' generates a lineplot that is by default
filled.
*align*: ['left' | 'mid' | 'right' ]
Controls how the histogram is plotted.
- 'left': bars are centered on the left bin edges.
- 'mid': bars are centered between the bin edges.
- 'right': bars are centered on the right bin edges.
*orientation*: [ 'horizontal' | 'vertical' ]
If 'horizontal', :func:`~matplotlib.pyplot.barh` will be
used for bar-type histograms and the *bottom* kwarg will be
the left edges.
*rwidth*:
The relative width of the bars as a fraction of the bin
width. If *None*, automatically compute the width. Ignored
if *histtype* = 'step' or 'stepfilled'.
*log*:
If *True*, the histogram axis will be set to a log scale.
If *log* is *True* and *x* is a 1D array, empty bins will
be filtered out and only the non-empty (*n*, *bins*,
*patches*) will be returned.
*color*:
Color spec or sequence of color specs, one per
dataset. Default (*None*) uses the standard line
color sequence.
*label*:
String, or sequence of strings to match multiple
datasets. Bar charts yield multiple patches per
dataset, but only the first gets the label, so
that the legend command will work as expected::
ax.hist(10+2*np.random.randn(1000), label='men')
ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5)
ax.legend()
kwargs are used to update the properties of the
:class:`~matplotlib.patches.Patch` instances returned by *hist*:
%(Patch)s
**Example:**
.. plot:: mpl_examples/pylab_examples/histogram_demo.py
Definition at line 18 of file mpl_axes_hist_fix.py.
1.8.5