matplotlib.pyplot.hist — Matplotlib 3.1.2 documentation (original) (raw)

Parameters:

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

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

bins : int or sequence or str, optional

If an integer is given, bins + 1 bin edges are calculated and returned, consistent with numpy.histogram.

If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified.

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

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.

Unequally spaced bins are supported if bins is a sequence.

With Numpy 1.11 or newer, you can alternatively provide a string describing a binning strategy, such as 'auto', 'sturges', 'fd', 'doane', 'scott', 'rice' or 'sqrt', seenumpy.histogram.

The default is taken from [rcParams["hist.bins"] = 10](../../tutorials/introductory/customizing.html?highlight=hist.bins#a-sample-matplotlibrc-file).

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 : bool, 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.

This parameter can be used to draw a histogram of data that has already been binned, e.g. using np.histogram (by treating each bin as a single point with a weight equal to its count)

counts, bins = np.histogram(data) plt.hist(bins[:-1], bins, weights=counts)

(or you may alternatively use bar()).

cumulative : bool, 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 arranged 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. IfNone, automatically compute the width.

Ignored if histtype is 'step' or 'stepfilled'.

Default is None

log : bool, 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 : str 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 : bool, optional

If True, multiple data are stacked on top of each other IfFalse multiple data are arranged side by side if histtype is 'bar' or on top of each other if histtype is 'step'

Default is False

normed : bool, optional

Deprecated; use the density keyword argument instead.

Other Parameters:

**kwargs : Patch properties