一、特点
数据必须是原始数据不能经过处理，数据连续型，显示一组或多组分布数据
histogram 直方图
normed 定额
二、核心
hist（x, bins=None, normed=None） # x是需要统计的数据，类型：数组 # bins是组数, 组数 = (max(数组)- min(数组))//组距 # normed 默认为：频数分布直方图， 值为True为： 频率分布直方图
三、示例
1、频数直方图
from matplotlib import pyplot as plt from matplotlib import font_manager a = [131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111, 78, 132, 124, 113, 150, 110, 117, 86, 95, 144, 105, 126, 130, 126, 130, 126, 116, 123, 106, 112, 138, 123, 86, 101, 99, 136, 123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127, 105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114, 105, 115, 132, 145, 119, 121, 112, 139, 138, 109, 132, 134, 156, 106, 117, 127, 144, 139, 139, 119, 140, 83, 110, 102, 123, 107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133, 112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135, 115, 146, 137, 116, 103, 144, 83, 123, 111, 110, 111, 100, 154, 136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141, 120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126, 114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137, 92, 121, 112, 146, 97, 137, 105, 98, 117, 112, 81, 97, 139, 113, 134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110, 105, 129, 137, 112, 120, 113, 133, 112, 83, 94, 146, 133, 101, 131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111, 111, 133, 150, 120] my_font = font_manager.FontProperties(fname="C:WindowsFontsmsjh.ttc") # 设置图行大小 plt.figure(figsize=(20, 8), dpi=80) # 绘图 movie_width = 3 num_bins = (max(a) - min(a)) // movie_width plt.hist(a, num_bins) # 定制x轴刻度和label _x = list(range(min(a), max(a) + 1)) plt.xticks(_x[::movie_width]) # 添加网格 plt.grid() # 添加说明 plt.xlabel("电影时长 单位(分)", fontproperties=my_font) plt.ylabel("数量", fontproperties=my_font) plt.title("电影时长频数分布直方图", fontproperties=my_font) # 展示图片 plt.show()
2、频率直方图
频数直方图->频率直方图， 只需要在绘图的时候添加 normed=True 即可
plt.hist(a, num_bins, normed=True)
注意：
MatplotlibDeprecationWarning: The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead. plt.hist(a, num_bins, normed=True)
四、条形图->直方图
目的：解决处理后的数据不能使用直方图的问题
方案：
1.绘图时，width=1或height=1 2.设置x轴或y轴的刻度，注意设置刻度和绘图之间没有直接的关系
例子
from matplotlib import pyplot as plt from matplotlib import font_manager # my_font = font_manager.FontProperties(fname="C:WindowsFontsmsjh.ttc") interval = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 60, 90] width = [5, 5, 5, 5, 5, 5, 5, 5, 5, 15, 30, 60] quantity = [836, 2737, 3723, 3926, 3596, 1438, 3273, 642, 824, 613, 215, 47] # 显示中文 # 设置图行大小 plt.figure(figsize=(20, 8), dpi=80) # 绘图 plt.bar(range(len(interval)), quantity, width=1) # 设置x轴刻度和label _x = range(len(interval) + 1) _x_ticks = [i - 0.5 for i in _x] _x_label = interval + [150] plt.xticks(_x_ticks, _x_label) # 添加说明 plt.xlabel("间隔", fontproperties=my_font) plt.ylabel("数量", fontproperties=my_font) plt.title("人口普查", fontproperties=my_font) # 添加网格 plt.grid() # 展示图片 plt.show()