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matplotlib.pyplot.hist

matplotlib.pyplot.hist(x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, normed=None, hold=None, data=None, **kwargs)

Plot a histogram.

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.

Parameters:

x : (n,) array or sequence of (n,) arrays

Input values, this takes either a single array or a sequency of arrays which are not required to be of the same length

bins : integer or array_like or ‘auto’, optional

If an integer is given, bins + 1 bin edges are returned, consistently with numpy.histogram() for numpy version >= 1.3.

Unequally spaced bins are supported if bins is a sequence.

If Numpy 1.11 is installed, may also be 'auto'.

Default is taken from the rcParam hist.bins.

range : tuple or None, optional

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.

Default is None

density : boolean, optional

If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., the area (or integral) under the histogram will sum to 1. This is achieved by dividing the count by the number of observations times the bin width and not dividing by the total number of observations. If stacked is also True, the sum of the histograms is normalized to 1.

Default is None for both normed and density. If either is set, then that value will be used. If neither are set, then the args will be treated as False.

If both density and normed are set an error is raised.

weights : (n, ) array_like or None, optional

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 or density is True, the weights are normalized, so that the integral of the density over the range remains 1.

Default is None

cumulative : boolean, optional

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 or density 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 and/or density is also True, then the histogram is normalized such that the first bin equals 1.

Default is False

bottom : array_like, scalar, or None

Location of the bottom baseline of each bin. If a scalar, the base line for each bin is shifted by the same amount. If an array, each bin is shifted independently and the length of bottom must match the number of bins. If None, defaults to 0.

Default is None

histtype : {‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}, optional

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.

Default is ‘bar’

align : {‘left’, ‘mid’, ‘right’}, optional

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.

Default is ‘mid’

orientation : {‘horizontal’, ‘vertical’}, optional

If ‘horizontal’, barh will be used for bar-type histograms and the bottom kwarg will be the left edges.

rwidth : scalar or None, optional

The relative width of the bars as a fraction of the bin width. If None, automatically compute the width.

Ignored if histtype is ‘step’ or ‘stepfilled’.

Default is None

log : boolean, optional

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.

Default is False

color : color or array_like of colors or None, optional

Color spec or sequence of color specs, one per dataset. Default (None) uses the standard line color sequence.

Default is None

label : string or None, optional

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.

default is None

stacked : boolean, optional

If True, multiple data are stacked on top of each other If False multiple data are aranged side by side if histtype is ‘bar’ or on top of each other if histtype is ‘step’

Default is False

Returns:

n : array or list of arrays

The values of the histogram bins. See normed or density and weights for a description of the possible semantics. If input x is an array, then this is an array of length nbins. If input is a sequence arrays [data1, data2,..], then this is a list of arrays with the values of the histograms for each of the arrays in the same order.

bins : array

The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in.

patches : list or list of lists

Silent list of individual patches used to create the histogram or list of such list if multiple input datasets.

Other Parameters:
 

**kwargs : Patch properties

See also

hist2d
2D histograms

Notes

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.

Note

In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments are replaced by data[<arg>]:

  • All arguments with the following names: ‘weights’, ‘x’.

Examples using matplotlib.pyplot.hist