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python中plt.hist參數詳解

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

這個參數是指定每個bin(箱子)分布的資料,對應x軸

bins : integer or array_like, optional

這個參數指定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)

這個參數指定密度,也就是每個條狀圖的占比例比,預設為1

color : color or array_like of colors or None, optional

這個指定條狀圖的顔色

我們繪制一個10000個資料的分布條狀圖,共50份,以統計10000分的分布情況

"""  
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()  
           
python中plt.hist參數詳解