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()  

python中plt.hist参数详解

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