hist

转载：python中plt.hist参数详解

data3[name].hist(bins=50,alpha=0.5,color='b')
plt.title(name)
plt.show()


normed=True画频率直方图

    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()  

转;http://blog.csdn.net/u013571243/article/details/48998619