09.繪制散點圖

繪制散點圖

假設通過爬蟲你獲取到了北京2016年3,10月份每天白天的最高氣溫(分別位于列表a,b),那么此時如何尋找出氣溫和隨時間(天)變化的某種規律?

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]

from matplotlib import pyplot as plt from matplotlib import font_manager my_font = font_manager.FontProperties(fname="/System/Library/Fonts/PingFang.ttc") 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")_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)plt.xlabel("時間",fontproperties=my_font) plt.ylabel("溫度",fontproperties=my_font) plt.title("標題",fontproperties=my_font) plt.legend(loc="upper left",prop=my_font)plt.show()

散點圖的更多應用場景

不同條件(維度)之間的內在關聯關系

觀察數據的離散聚合程度

10.繪制條形圖

繪制條形圖

假設你獲取到了2017年內地電影票房前20的電影(列表a)和電影票房數據(列表b),那么如何更加直觀的展示該數據?

a = ["戰狼2","速度與激情8","功夫瑜伽","西游伏妖篇","變形金剛5：最后的騎士","摔跤吧！爸爸","加勒比海盜5：死無對證","金剛：骷髏島","極限特工：終極回歸","生化危機6：終章","乘風破浪","神偷奶爸3","智取威虎山","大鬧天竺","金剛狼3：殊死一戰","蜘蛛俠：英雄歸來","悟空傳","銀河護衛隊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] 單位:億

from matplotlib import pyplot as plt from matplotlib import font_manager my_font=font_manager.FontProperties(fname="/System/Library/Fonts/PingFang.ttc") a = ["戰狼2","速度與激情8","功夫瑜伽","西游伏妖篇","變形金剛5：最后的騎士","摔跤吧！爸爸","加勒比海盜5：死無對證","金剛：骷髏島","極限特工：終極回歸","生化危機6：終章","乘風破浪","神偷奶爸3","智取威虎山","大鬧天竺","金剛狼3：殊死一戰","蜘蛛俠：英雄歸來","悟空傳","銀河護衛隊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=(20,15),dpi=80) plt.bar(range(len(a)),b) plt.xticks(range(len(a)),a,fontproperties=my_font,rotation=90) plt.show()

from matplotlib import pyplot as plt from matplotlib import font_manager my_font=font_manager.FontProperties(fname="/System/Library/Fonts/PingFang.ttc") a = ["戰狼2","速度與激情8","功夫瑜伽","西游伏妖篇","變形金剛5：最后的騎士","摔跤吧！爸爸","加勒比海盜5：死無對證","金剛：骷髏島","極限特工：終極回歸","生化危機6：終章","乘風破浪","神偷奶爸3","智取威虎山","大鬧天竺","金剛狼3：殊死一戰","蜘蛛俠：英雄歸來","悟空傳","銀河護衛隊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.barh(range(len(a)),b) plt.grid(alpha=0.5) plt.yticks(range(len(a)),a,fontproperties=my_font) plt.show()

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11.繪制多次條形圖

繪制多次條形圖

假設你知道了列表a中電影分別在2017-09-14(b_14), 2017-09-15(b_15), 2017-09-16(b_16)三天的票房,為了展示列表中電影本身的票房以及同其他電影的數據對比情況,應該如何更加直觀的呈現該數據?

a = ["猩球崛起3：終極之戰","敦刻爾克","蜘蛛俠：英雄歸來","戰狼2"]

b_16 = [15746,312,4497,319]

b_15 = [12357,156,2045,168]

b_14 = [2358,399,2358,362]

from matplotlib import pyplot as plt from matplotlib import font_manager my_font = font_manager.FontProperties(fname="/System/Library/Fonts/PingFang.ttc")a = ["猩球崛起3：終極之戰","敦刻爾克","蜘蛛俠：英雄歸來","戰狼2"] b_16 = [15746,312,4497,319] b_15 = [12357,156,2045,168] b_14 = [2358,399,2358,362]bar_width = 0.2 x_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]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")plt.legend(prop=my_font) plt.xticks(x_15,a,fontproperties=my_font)plt.show()

條形圖的更多應用場景

數量統計

頻率統計(市場飽和度)

12.繪制直方圖

繪制直方圖

假設你獲取了250部電影的時長(列表a中),希望統計出這些電影時長的分布狀態(比如時長為100分鐘到120分鐘電影的數量,出現的頻率)等信息,你應該如何呈現這些數據?

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]

把數據分為多少組進行統計?

組數要適當,太少會有較大的統計誤差,大多規律不明顯

from matplotlib import pyplot as plt from matplotlib import font_manager my_font = font_manager.FontProperties(fname="/System/Library/Fonts/PingFang.ttc")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 num_bins = (max(a)-min(a))//dplt.figure(figsize=(20,8),dpi=80) plt.hist(a,num_bins) # plt.hist(a,num_bins,normed=1) 頻率分布直方圖plt.xticks(range(min(a),max(a)+d,d)) plt.grid(alpha=0.5) plt.show()

在美國2004年人口普查發現有124 million的人在離家相對較遠的地方工作。根據他們從家到上班地點所需要的時間,通過抽樣統計(最后一列)出了下表的數據,這些數據能夠繪制成直方圖么?

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]?

from matplotlib import pyplot as plt from matplotlib import font_manager my_font = font_manager.FontProperties(fname="/System/Library/Fonts/PingFang.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]plt.figure(figsize=(20,8),dpi=80) plt.bar(range(len(quantity)),quantity,width=1)_x = [i-0.5 for i in range(13)] _xtick_labels = interval+[150] plt.xticks(_x,_xtick_labels)plt.grid() plt.show()

直方圖更多應用場景

用戶的年齡分布狀態

一段時間內用戶點擊次數的分布狀態

用戶活躍時間的分布狀態

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13.更多的繪圖工具的了解

更多的繪圖工具的了解

Echarts gallery

plotly:可視化工具中的github,相比于matplotlib更加簡單,圖形更加漂亮,同時兼容matplotlib和pandas

seaborn

《新程序員》：云原生和全面數字化實踐50位技術專家共同創作，文字、視頻、音頻交互閱讀

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