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

這個引數是指定每個bin(箱子)分佈的資料,對應x軸

bins : integer or array_like,optional

這個引數指定bin(箱子)的個數,也就是總共有幾條條狀圖

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)

這個引數指定密度,也就是每個條狀圖的佔比例比,預設為1

color : color or array_like of colors or None,optional

這個指定條狀圖的顏色

我們繪製一個10000個數據的分佈條狀圖,共50份,以統計10000分的分佈情況

  """ 
  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() 

以上這篇關於python中plt.hist引數的使用詳解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。