直方图hist¶

plt.hist函数:

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

参数含义:

bin：箱子的宽度
normed 标准化
histtype 风格，bar，barstacked，step，stepfilled
orientation 水平还是垂直{‘horizontal’, ‘vertical’}
align : {‘left’, ‘mid’, ‘right’}, optional(对齐方式)
stacked：是否堆叠

条形图和直方图之间的区别：直方图用于显示分布，而条形图用于比较不同的实体:

import matplotlib.pyplot as plt
population_age = [22,55,62,45,21,22,34,42,42,4,2,102,95,85,55,110,120,70,65,55,111,115,80,75,65,54,44,43,42,48]
bins = [0,10,20,30,40,50,60,70,80,90,100]
plt.hist(population_age, bins, histtype='bar', color='b', rwidth=0.8)
plt.xlabel('age groups')
plt.ylabel('Number of people')
plt.title('Histogram')
plt.show()

实例:

# 直方图
s = pd.Series(np.random.randn(1000))
s.hist(bins = 20,
       histtype = 'bar',
       align = 'mid',
       orientation = 'vertical',
       alpha=0.5,
       normed =True)
# 密度图
s.plot(kind='kde',style='k--')

# 堆叠直方图:

plt.figure(num=1)
df = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
                    'c': np.random.randn(1000) - 1, 'd': np.random.randn(1000)-2},
                   columns=['a', 'b', 'c','d'])
df.plot.hist(stacked=True,
             bins=20,
             colormap='Greens_r',
             alpha=0.5,
             grid=True)
# 使用DataFrame.plot.hist()和Series.plot.hist()方法绘制

df.hist(bins=50)
# 生成多个直方图