关于python中plt.hist参数的使用详解

如下所示：

matplotlib.pyplot.hist(
 x, bins=10, range=None, normed=False,  
 weights=None, cumulative=False, bottom=None,  
 histtype=u'bar', align=u'mid', orientation=u'vertical',  
 rwidth=None, log=False, color=None, label=None, stacked=False,  
 hold=None, **kwargs)

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

这个参数是指定每个bin(箱子)分布的数据,对应x轴

bins : integer or array_like, optional

这个参数指定bin(箱子)的个数,也就是总共有几条条状图

normed : boolean, optional

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)

这个参数指定密度,也就是每个条状图的占比例比,默认为1

color : color or array_like of colors or None, optional

这个指定条状图的颜色

我们绘制一个10000个数据的分布条状图,共50份,以统计10000分的分布情况

 """
 Demo of the histogram (hist) function with a few features.

In addition to the basic histogram, this demo shows a few optional features:

* Setting the number of data bins
   * The ``normed`` flag, which normalizes bin heights so that the integral of
    the histogram is 1. The resulting histogram is a probability density.
   * Setting the face color of the bars
   * Setting the opacity (alpha value).

"""
 import numpy as np
 import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt

# example data
 mu = 100 # mean of distribution
 sigma = 15 # standard deviation of distribution
 x = mu + sigma * np.random.randn(10000)

num_bins = 50
 # the histogram of the data
 n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='blue', alpha=0.5)
 # add a 'best fit' line
 y = mlab.normpdf(bins, mu, sigma)
 plt.plot(bins, y, 'r--')
 plt.xlabel('Smarts')
 plt.ylabel('Probability')
 plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')

# Tweak spacing to prevent clipping of ylabel
 plt.subplots_adjust(left=0.15)
 plt.show()

来源：https://blog.csdn.net/qq_42600434/article/details/81626749