#https://matplotlib.org/gallery/index.html #https://echarts.apache.org/zh/index.html #https://plotly.com #http://seaborn.pydata.org import pandas as pd from matplotlib import pyplot as plt import numpy import random a = pd.Series([15,13,14.5,17,20,25,26,26,27,22,18,15],index =[i for i in range(2,26,2)]) # 设置图片大小 此句要放在绘制图片之前 plt.figure(figsize=(15,8),dpi = 80) plt.plot(a.index,a,'o-',color='r',linewidth=2) plt.xticks(numpy.arange(2, 26, 2)) # 来确定x轴 的刻度 #plt.yticks(range(min(a),max(a)+1)) plt.xlabel('时间') plt.ylabel('温度') # 保存 plt.savefig("./sig_size.png) 为当前路径下 相对路径，位置要放在绘制图片之后，show之前 # plt.savefig("./sig_size.svg) svg 是以矢量图的方式进行保存 plt.show() #%% ---------------------------------------------------- x = range(120) random.seed(10) y= [random.randint(20,35) for i in range(120)] plt.plot(x,y) _x_ticks = ['10点{}分'.format(i) for i in x if i < 60] _x_ticks +=['11点{}分'.format(i) for i in x if i >= 60] plt.xticks(x[::10],_x_ticks[::10],rotation=45) # 取步长，且数字和字符串一一对应，数据的长度一样 #为了让字符串不会覆盖，旋转90度显示 # 添加描述信息 plt.xlabel("时间") plt.ylabel("温度 单位（℃）") plt.title('10点到12点每分钟的气温变化情况') # 设置网格 plt.grid(alpha=1,linestyle=':') # alpha 是指透明度 从0-1 plt.show() #%%------------------------ #双 折线图 plot多次即可 q = [1,0,1,1,2,4,3,2,3,4,4,5,6,5,4,3,3,1,1,1] w = [1,0,3,1,2,2,3,3,2,1,2,1,1,1,1,1,1,1,1,1] age = range(11,31) plt.plot(age,q,label='自己',color='orange') plt.plot(age,w, label='同桌',color='cyan',linestyle=':',linewidth=2) # 添加图例 plt.legend(loc = 'upper left') #%%———————————————————————————————— #散点图 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) plt.figure(figsize=(20,8),dpi = 80) plt.scatter(x_3,y_3,label='三月份') plt.scatter(x_10,y_10,label='十月份' ) # 调整x 轴的刻度 _x = list(x_3)+list(x_10) _xtick_lables = ['3月{}日'.format(i) for i in x_3 ] _xtick_lables += ['10月{}日'.format(i-50) for i in x_10 ] plt.xticks(_x[::3],_xtick_lables[::3],rotation=45) plt.xlabel('时间') plt.ylabel('温度') plt.title('标题') plt.legend(loc = 2) plt.show() #%%------------------------------ # 绘制条形图 a = ["战狼2","速度与激情8","功夫瑜伽","西游伏妖篇","变形金刚5\n:最后的骑士","摔跤吧 !爸爸","加勒比海盗5\n:死无对证","金刚:骷髅岛","极限特工:终极回归","生化危机6\n: 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] plt.figure(figsize=(20,8),dpi = 80) plt.bar(range(len(a)),b,width=0.5) # width 是指宽度 plt.barh(a,b,height=0.5) # width 是指宽度 plt.xticks(range(len(a)),a,rotation=90) plt.show() #%% 横向条形图 plt.figure(figsize=(20,10),dpi = 80) plt.barh(a,b,height=0.7,color='orange') # width 是指宽度 #%% 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_17 = [12357,156,2045,168] b_18 = [2358,399,2358,362] c = list(range(len(a))) c17 = [i+0.2 for i in c] c18 = [i+0.4 for i in c] plt.bar(c,b_16,label='16日',color='red',width=0.2) plt.bar(c17,b_17,label='17日',color='orange',width=0.2) plt.bar(c18,b_18,label='18日',color='green',width=0.2) plt.xticks(c17,a) plt.legend() plt.show() #%%------------------- # 绘制直方图（没有进行统计之前的数据） # 统计之后的数据无法绘制直方图 只能绘制条形图 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] d = 3 # 组距 最好设置成能够被整除的数字 plt.figure(figsize=(20,8),dpi = 80) num_bins = ((max(a)-min(a))//d) #//为了得到整数 ，去掉余数 plt.hist(a,num_bins,density=1) # normed =1 为频数分布 使用den plt.grid() #plt.xticks(range(min(a),max(a)+d,d)) x = range(min(a)//d*d,max(a)+d,d) plt.xticks(x) plt.show() #%%------------------- # 统计之后的数据无法绘制直方图 只能绘制条形图 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] plt.figure(figsize=(20,10),dpi = 80) plt.bar(range(len(quantity)),quantity,width=1) # 默认宽度为0.8 # 设置x轴的刻度 _x = [i-0.5 for i in range(13)] _xtick_labels = interval+[150] plt.xticks(_x,_xtick_labels) plt.grid(alpha=0.4) # alpha 调网格的粗细 plt.show() #%%——----------------——————————————————- #
