绘制散点图
散点图的更多应用场景 绘制条形图
条形图的更多应用场景 绘制直方图matplotlib使用的流程总结 绘制散点图
假设通过爬虫你获取到了北京2016年3,10月份每天白天的最高气温(分别位于列表a,b),那么此时如何寻找出气温和随时间(天)变化的某种规律?
a = [11,17,16,11,12,11,12,6,6,7,8,9,12,15,14,17,18,21,16,17,20,14,15,15,15,19,21,22,22,22,23]
b = [26,26,28,19,21,17,16,19,18,20,20,19,22,23,17,20,21,20,22,15,11,15,5,13,17,10,11,13,12,13,6]
from matplotlib import pyplot as pltfrom matplotlib import font_managerimport matplotlibmy_font = matplotlib.rc('font',family='MicroSoft YaHei',weight='bold')y_3 = [11,17,16,11,12,11,12,6,6,7,8,9,12,15,14,17,18,21,16,17,20,14,15,15,15,19,21,22,22,22,23]y_10 = [26,26,28,19,21,17,16,19,18,20,20,19,22,23,17,20,21,20,22,15,11,15,5,13,17,10,11,13,12,13,6]x_3 = range(1,32)x_10 = range(51,82)#设置图形大小plt.figure(figsize=(20,8),dpi=80)#使用scatter方法绘制散点图,和之前绘制折线图的唯一区别plt.scatter(x_3,y_3,label="3月份")plt.scatter(x_10,y_10,label="10月份")#调整x轴的刻度_x = list(x_3)+list(x_10)_xtick_labels = ["3月{}日".format(i) for i in x_3]_xtick_labels += ["10月{}日".format(i-50) for i in x_10]plt.xticks(_x[::3],_xtick_labels[::3],fontproperties=my_font,rotation=45)#添加图例plt.legend(loc="upper left",prop=my_font)#添加描述信息plt.xlabel("时间",fontproperties=my_font)plt.ylabel("温度",fontproperties=my_font)plt.title("标题",fontproperties=my_font)#展示plt.show()
运行结果：
不同条件(维度)之间的内在关联关系观察数据的离散聚合程度 绘制条形图
假设你获取到了2017年内地电影票房前20的电影(列表a)和电影票房数据(列表b),那么如何更加直观的展示该数据?
a = [“战狼2”,“速度与激情8”,“功夫瑜伽”,“西游伏妖篇”,“变形金刚5：最后的骑士”,“摔跤吧！爸爸”,“加勒比海盗5：死无对证”,“金刚：骷髅岛”,“极限特工：终极回归”,“生化危机6：终章”,“乘风破浪”,“神偷奶爸3”,“智取威虎山”,“大闹天竺”,“金刚狼3：殊死一战”,“蜘蛛侠：英雄归来”,“悟空传”,“银河护卫队2”,“情圣”,“新木乃伊”,]
b=[56.01,26.94,17.53,16.49,15.45,12.96,11.8,11.61,11.28,11.12,10.49,10.3,8.75,7.55,7.32,6.99,6.88,6.86,6.58,6.23] 单位:亿
bar()方法绘制条形图，用width来控制线条粗细
from matplotlib import pyplot as pltfrom matplotlib import font_managerimport matplotlibmy_font = matplotlib.rc('font',family='MicroSoft YaHei',weight='bold')a = ["战狼2","速度与激情8","功夫瑜伽","西游伏妖篇","变形金刚5：最后的骑士","摔跤吧！爸爸","加勒比海盗5：死无对证","金刚：骷髅岛","极限特工：终极回归","生化危机6：终章","乘风破浪","神偷奶爸3","智取威虎山","大闹天竺","金刚狼3：殊死一战","蜘蛛侠：英雄归来","悟空传","银河护卫队2","情圣","新木乃伊",]b=[56.01,26.94,17.53,16.49,15.45,12.96,11.8,11.61,11.28,11.12,10.49,10.3,8.75,7.55,7.32,6.99,6.88,6.86,6.58,6.23]#设置图形大小plt.figure(figsize=(20,15),dpi=80)#绘制条形图plt.bar(range(len(a)),b,width=0.3)#设置字符串到x轴plt.xticks(range(len(a)),a,fontproperties=my_font,rotation=90)plt.savefig("./movie.png")plt.show()
运行结果：
但是这样看横坐标的呈现效果不是很好看，可以把它的横竖坐标对换一下，
用barh()方法来绘制横向的条形图，用height来控制线条的高低**
#绘制横着的条形图from matplotlib import pyplot as pltfrom matplotlib import font_managerimport matplotlibmy_font = matplotlib.rc('font',family='MicroSoft YaHei',weight='bold')a = ["战狼2","速度与激情8","功夫瑜伽","西游伏妖篇","变形金刚5：最后的骑士","摔跤吧！爸爸","加勒比海盗5：死无对证","金刚：骷髅岛","极限特工：终极回归","生化危机6：终章","乘风破浪","神偷奶爸3","智取威虎山","大闹天竺","金刚狼3：殊死一战","蜘蛛侠：英雄归来","悟空传","银河护卫队2","情圣","新木乃伊",]b=[56.01,26.94,17.53,16.49,15.45,12.96,11.8,11.61,11.28,11.12,10.49,10.3,8.75,7.55,7.32,6.99,6.88,6.86,6.58,6.23]#设置图形大小plt.figure(figsize=(20,8),dpi=80)#绘制条形图plt.barh(range(len(a)),b,height=0.3,color="orange")#设置字符串到x轴plt.yticks(range(len(a)),a,fontproperties=my_font)plt.grid(alpha=0.3)plt.show()
运行结果：
【练习】假设你知道了列表a中电影分别在2017-09-14(b_14), 2017-09-15(b_15), 2017-09-16(b_16)三天的票房,为了展示列表中电影本身的票房以及同其他电影的数据对比情况,应该如何更加直观的呈现该数据?
a = [“猩球崛起3：终极之战”,“敦刻尔克”,“蜘蛛侠：英雄归来”,“战狼2”]
b_16 = [15746,312,4497,319]
b_15 = [12357,156,2045,168]
b_14 = [2358,399,2358,362]
from matplotlib import pyplot as pltimport matplotlibmy_font = matplotlib.rc('font',family='MicroSoft YaHei',weight='bold')a = ["猩球崛起3：终极之战","敦刻尔克","蜘蛛侠：英雄归来","战狼2"]b_16 = [15746,312,4497,319]b_15 = [12357,156,2045,168]b_14 = [2358,399,2358,362]bar_width = 0.2x_14 = list(range(len(a)))x_15 = [i+bar_width for i in x_14]x_16 = [i+bar_width*2 for i in x_14]#设置图形大小plt.figure(figsize=(20,8),dpi=80)plt.bar(range(len(a)),b_14,width=bar_width,label="9月14日")plt.bar(x_15,b_15,width=bar_width,label="9月15日")plt.bar(x_16,b_16,width=bar_width,label="9月16日")#设置图例plt.legend(prop=my_font)#设置x轴的刻度plt.xticks(x_15,a,fontproperties=my_font)plt.show()
运行结果：
数量统计频率统计(市场饱和度) 绘制直方图
用hist()方法来绘制直方图
假设你获取了250部电影的时长(列表a中),希望统计出这些电影时长的分布状态(比如时长为100分钟到120分钟电影的数量,出现的频率)等信息,你应该如何呈现这些数据?
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, 125, 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]
from matplotlib import pyplot as plta=[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, 125, 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]#计算组数d = 3 #组距num_bins = (max(a)-min(a))//dprint(max(a),min(a),max(a)-min(a))print(num_bins)#设置图形的大小plt.figure(figsize=(20,8),dpi=80)plt.hist(a,num_bins,density=True,stacked=True)#设置x轴的刻度plt.xticks(range(min(a),max(a)+d,d))plt.grid()plt.show()
运行结果：
教程里给的是normed方法，但是程序运行之后会报错：AttributeError:‘Rectangle’ object has no property ‘normed’
通过百度得知，是因为这个库更新了，已经没有这个属性了，所以要把代码中 normed这个属性换成density，再加一个属性stacked=True。修改过后再次运行就成功了
###直方图更多应用场景
用户的年龄分布状态一段时间内用户点击次数的分布状态用户活跃时间的分布状态 matplotlib使用的流程总结 明确问题选择图形的呈现方式准备数据绘图和图形完善