plt.hist()和numpy.histogram（）的学习

matplotlib.pyplot.hist( )

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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)

• Compute and draw the histogram of x. The return value is a tuple (n, bins, patches) or ([n0, n1, …],
• bins,[integer or sequence or ’auto’, 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:
  [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.
  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 to1.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
  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 thebin 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 isTrue 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 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.

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]

numpy.histogram（）

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