文章目錄

• 數據分析第三講 matplotlib常用統計圖
• • • 1.繪制散點圖
    • 2.繪制條形圖
    • 3.繪制直方圖
    • 4.繪制餅圖
    • 5. 繪制3D立體圖形
    • • 5.1 3D曲線
      • 5.2 3D散點圖
      • 5.3 3D平面圖
    • 6.子圖的使用
    • 7.matplotlib常見問題總結
    • 8.matplotlib使用的流程總結

數據分析第三講 matplotlib常用統計圖

1.繪制散點圖

使用的方法:scatter(x,y)

假設通過爬蟲你獲取到了長沙2018年10,11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]

# 假設通過爬蟲你獲取到了長沙2018年10,11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_managerfont = font_manager.FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14) y_10 = [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_11 = [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]# 設置圖片大小 plt.figure(figsize=(15, 8), dpi=80)# 十月份 31天 x_10 = range(1, 32) # 十一月份 30天 x_11 = range(51, 81)# 繪制散點圖 plt.scatter(x_10, y_10, label="十月份") plt.scatter(x_11, y_11, label="十一月份")# 設置x軸刻度 _x = list(x_10) + list(x_11) _xticks_label = ["十月{}日".format(i) for i in x_10] _xticks_label += ["十月{}日".format(i - 30) for i in x_11] plt.xticks(_x[::3], _xticks_label[::3], fontproperties=font, rotation=45)# 添加描述信息 plt.xlabel("時間", fontproperties=font) plt.xlabel("溫度", fontproperties=font) # 添加標題 plt.title("2018年長沙市10,11月份氣溫變化", fontproperties=font) # 添加圖例 plt.legend(prop=font)plt.show()

2.繪制條形圖

• 2.1使用的方法:bar(x,y)
  
  假設你獲取到了2019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 = [“流浪地球”,“復仇者聯盟4:終局之戰”,“哪吒之魔童降世”,“瘋狂的外星人”,“飛馳人生”,“蜘蛛俠:英
  雄遠征”,“掃毒2天地對決”,“烈火英雄”,“大黃蜂”,“驚奇隊長”,“比悲傷更悲傷的故事”,“哥斯拉2:怪獸之
  王”,“阿麗塔:戰斗天使”,“銀河補習班”,“獅子王”,“反貪風暴4”,“熊出沒”,“大偵探皮卡丘”,"新喜劇之王
  ",“使徒行者2:諜影行動”,]
  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]
• 示例1

''' 假設你獲取到了2019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 = ["流浪地球","復仇者聯盟4:終局之戰","哪吒之魔童降世","瘋狂的外星人","飛馳人生","蜘蛛俠:英 雄遠征","掃毒2天地對決","烈火英雄","大黃蜂","驚奇隊長","比悲傷更悲傷的故事","哥斯拉2:怪獸之 王","阿麗塔:戰斗天使","銀河補習班","獅子王","反貪風暴4","熊出沒","大偵探皮卡丘","新喜劇之王 ","使徒行者2:諜影行動"] 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] ''' from matplotlib import pyplot as plt from matplotlib import font_managerfont = font_manager.FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14)x = ["流浪地球","復仇者聯盟4:\n終局之戰","哪吒之\n魔童降世","瘋狂的外星人","飛馳人生","蜘蛛俠:\n英雄遠征","掃毒2\n天地對決","烈火英雄","大黃蜂","驚奇隊長","比悲傷更悲\n傷的故事","哥斯拉2:\n怪獸之王","阿麗塔:\n戰斗天使","銀河補習班","獅子王","反貪風暴4","熊出沒","大偵探皮卡丘","新喜劇之王","使徒行者2:\n諜影行動"] y = [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]# print(len(x),len(y)) # 設置圖片大小 plt.figure(figsize=(15,8),dpi=80)# 繪制條形圖 plt.bar(x,y)# 設置x軸刻度 # 0 - 19 plt.xticks(range(len(x)),x,fontproperties=font,rotation=45)plt.show() # 效果如下圖

• 2.2繪制橫向條形圖
  使用的方法:barh(x,y)
  
  把x軸橫過來：查看源碼，** def barh(self, y, width, height=0.8, left=None, *, align=“center”,
  kwargs):
  r"""
  Make a horizontal bar plot.
  
  更改plt.bar(x,y)為plt.barh(x,y)，刪除rotation=45和x數據里面的\n
  更改plt.xticks(range(len(x)),x,fontproperties=font)為plt.yticks(range(len(x)),x,fontproperties=font)
  代碼和效果如下：

from matplotlib import pyplot as plt from matplotlib import font_managerfont = font_manager.FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=10)x = ["流浪地球","復仇者聯盟4:終局之戰","哪吒之魔童降世","瘋狂的外星人","飛馳人生","蜘蛛俠:英雄遠征","掃毒2天地對決","烈火英雄","大黃蜂","驚奇隊長","比悲傷更悲傷的故事","哥斯拉2:怪獸之王","阿麗塔:戰斗天使","銀河補習班","獅子王","反貪風暴4","熊出沒","大偵探皮卡丘","新喜劇之王","使徒行者2:諜影行動"] y = [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]# print(len(x),len(y)) # 設置圖片大小 plt.figure(figsize=(15,8),dpi=80)# 繪制條形圖 plt.barh(x,y) # plt.bar(x,y)# 設置y軸刻度 # 0 - 19 plt.yticks(range(len(x)),x,fontproperties=font) # plt.xticks(range(len(x)),x,fontproperties=font)plt.show()

要實現排名高的前面：
更改plt.barh(x[::-1],y[::-1])，將x和y數據反轉
更改plt.yticks(range(len(x)),x,fontproperties=font) 為
plt.yticks(range(len(x)),x[::-1],fontproperties=font)
代碼、效果如下：

from matplotlib import pyplot as plt from matplotlib import font_managerfont = font_manager.FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=10)x = ["流浪地球","復仇者聯盟4:終局之戰","哪吒之魔童降世","瘋狂的外星人","飛馳人生","蜘蛛俠:英雄遠征","掃毒2天地對決","烈火英雄","大黃蜂","驚奇隊長","比悲傷更悲傷的故事","哥斯拉2:怪獸之王","阿麗塔:戰斗天使","銀河補習班","獅子王","反貪風暴4","熊出沒","大偵探皮卡丘","新喜劇之王","使徒行者2:諜影行動"] y = [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]# print(len(x),len(y)) # 設置圖片大小 plt.figure(figsize=(15,8),dpi=80)# 繪制條形圖 plt.barh(x[::-1],y[::-1]) # plt.bar(x,y)# 設置y軸刻度 # 0 - 19 plt.yticks(range(len(x)),x[::-1],fontproperties=font) # plt.xticks(range(len(x)),x,fontproperties=font)plt.show()

• 2.3 繪制多條條形圖
  如何在一張圖里繪制多個條形圖？
  x1 = [1,3,5,7,9]
  data1 = [5,2,7,8,2]
  x2 = [1,3,5,7,9]
  data2 = [8,6,2,5,6]
• 示例

''' 繪制多組數據 ''' from matplotlib import pyplot as pltx1 = [1,3,5,7,9,11,13,15] data1 = [5,3,4,3,2,3,1,3] x2 = x1 data2 = [4,2,3,2,1,2,0,2] plt.bar(x1,data1) plt.bar(x2,data2) plt.show() # 此時圖形會重合如下圖

解決辦法1：import numpy as np，x2 = np.array(x1) + 1
解決辦法2：使用列表推導式
代碼、效果如下：

''' 繪制多組數據 ''' from matplotlib import pyplot as plt import numpy as npx1 = [1,3,5,7,9,11,13,15] data1 = [5,3,4,3,2,3,1,3] # x2 = np.array(x1) + 1 x2 = [i + 1 for i in x1] data2 = [4,2,3,2,1,2,0,2] plt.bar(x1,data1) plt.bar(x2,data2) plt.show() #

• 條形圖練習
  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 = [‘流浪地球’,‘復仇者聯盟4’,‘哪吒之魔童降世’,‘瘋狂的外星人’]
  b_14 = [2358,399,2358,362]
  b_15 = [12357,156,2045,168]
  b_16 = [15746,312,4497,319]

''' 條形圖練習 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 = ['流浪地球','復仇者聯盟4','哪吒之魔童降世','瘋狂的外星人'] b_14 = [2358,399,2358,362] b_15 = [12357,156,2045,168] b_16 = [15746,312,4497,319] ''' from matplotlib import pyplot as plt from matplotlib import font_managerfont = font_manager.FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=10) a = ['流浪地球','復仇者聯盟4','哪吒之魔童降世','瘋狂的外星人'] b_14 = [2358,399,2358,362] b_15 = [12357,156,2045,168] b_16 = [15746,312,4497,319] # 設置圖形大小 plt.figure(figsize=(15,8), dpi=80) # 設置間隔 bar_width = 0.3 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.bar(x_14,b_14,bar_width,label='9月14日') plt.bar(x_15,b_15,bar_width,label='9月15日') plt.bar(x_16,b_16,bar_width,label='9月16日') # 設置圖例 plt.legend(prop=font) # 設置x軸刻度 plt.xticks(x_15,a,fontproperties=font)plt.show()

3.繪制直方圖

• 類似于柱狀圖
• 通過將數組組合在一起來顯示分布
• 使用的方法:hist(x,bins)
  組數:將數據分組，當數據在100個以內時，按數據多少常分5-12組
  組距:指每個小組的兩個端點的距離
  組數 = 極差/組距 極差=>max(a)-min(a) 組距=>78到81 組距3
• 示例

""" 直方圖示例 250部電影時長 """ # import random from matplotlib import pyplot as plt# a = [random.randint(78,150) for i in range(250)] # 隨機獲取250個time # print(a) a = [132, 114, 149, 127, 114, 83, 101, 98, 147, 78, 95, 129, 98, 93, 102, 131, 83, 123, 100, 93, 83, 88, 138, 119, 145, 121, 136, 87, 94, 86, 85, 112, 80, 143, 107, 116, 123, 124, 139, 104, 102, 104, 113, 121, 107, 80, 141, 86, 103, 119, 142, 131, 93, 101, 92, 81, 137, 138, 144, 79, 121, 79, 116, 143, 89, 82, 133, 87, 96, 140, 139, 104, 121, 84, 107, 93, 141, 119, 90, 132, 85, 139, 142, 126, 106, 138, 141, 116, 105, 143, 91, 116, 106, 134, 137, 144, 96, 112, 133, 150, 95, 96, 137, 79, 94, 116, 118, 105, 101, 87, 96, 82, 97, 90, 79, 91, 93, 93, 98, 114, 138, 80, 139, 108, 97, 143, 142, 97, 117, 92, 135, 118, 97, 137, 146, 144, 146, 107, 85, 145, 128, 78, 132, 142, 83, 111, 88, 106, 142, 128, 150, 116, 97, 113, 100, 130, 79, 149, 84, 104, 96, 94, 103, 105, 123, 113, 96, 95, 103, 126, 134, 123, 149, 140, 114, 116, 99, 97, 119, 93, 91, 120, 104, 98, 78, 124, 139, 106, 119, 141, 91, 136, 97, 107, 102, 95, 118, 93, 134, 142, 90, 84, 90, 149, 105, 112, 135, 93, 100, 149, 148, 136, 126, 88, 148, 113, 81, 86, 117, 145, 109, 120, 89, 103, 82, 112, 78, 125, 105, 132, 108, 105, 102, 95, 148, 111, 79, 88, 87, 97, 88, 83, 110, 145, 101, 138, 102, 92, 83, 83] # 組距 d = 3 # 組數 num = (max(a) - min(a)) // d # print(num) # 24# 圖像大小 plt.figure(figsize=(15,8),dpi=80) # 頻數直方圖 num還可以傳一個列表，比如num = [i for i in range(78,160,4)] plt.hist(a,num) # num = [i for i in range(78,160,4)] # 設置頻數直方圖傳入列表參數 # plt.hist(a,num,density=True) # 設置頻數直方圖修改默認參數# 設置x軸刻度 plt.xticks(range(min(a),max(a)+d,d)) # 78 81 84...150 # y軸表示在某個時長內有多少部電影 # 設置網格 plt.grid()plt.show()

頻率圖代碼、效果如下：

""" 直方圖示例 250部電影時長 """ from matplotlib import pyplot as plt a = [132, 114, 149, 127, 114, 83, 101, 98, 147, 78, 95, 129, 98, 93, 102, 131, 83, 123, 100, 93, 83, 88, 138, 119, 145, 121, 136, 87, 94, 86, 85, 112, 80, 143, 107, 116, 123, 124, 139, 104, 102, 104, 113, 121, 107, 80, 141, 86, 103, 119, 142, 131, 93, 101, 92, 81, 137, 138, 144, 79, 121, 79, 116, 143, 89, 82, 133, 87, 96, 140, 139, 104, 121, 84, 107, 93, 141, 119, 90, 132, 85, 139, 142, 126, 106, 138, 141, 116, 105, 143, 91, 116, 106, 134, 137, 144, 96, 112, 133, 150, 95, 96, 137, 79, 94, 116, 118, 105, 101, 87, 96, 82, 97, 90, 79, 91, 93, 93, 98, 114, 138, 80, 139, 108, 97, 143, 142, 97, 117, 92, 135, 118, 97, 137, 146, 144, 146, 107, 85, 145, 128, 78, 132, 142, 83, 111, 88, 106, 142, 128, 150, 116, 97, 113, 100, 130, 79, 149, 84, 104, 96, 94, 103, 105, 123, 113, 96, 95, 103, 126, 134, 123, 149, 140, 114, 116, 99, 97, 119, 93, 91, 120, 104, 98, 78, 124, 139, 106, 119, 141, 91, 136, 97, 107, 102, 95, 118, 93, 134, 142, 90, 84, 90, 149, 105, 112, 135, 93, 100, 149, 148, 136, 126, 88, 148, 113, 81, 86, 117, 145, 109, 120, 89, 103, 82, 112, 78, 125, 105, 132, 108, 105, 102, 95, 148, 111, 79, 88, 87, 97, 88, 83, 110, 145, 101, 138, 102, 92, 83, 83] # 組距 d = 3 # 組數 num = (max(a) - min(a)) // d # print(num) # 24# 圖像大小 plt.figure(figsize=(15,8),dpi=80) # 頻數直方圖 num還可以傳一個列表，比如num = [i for i in range(78,160,4)]num = [i for i in range(78,160,4)] # 設置頻數直方圖傳入列表參數 plt.hist(a,num,density=True) # 設置頻數直方圖修改默認參數# 設置x軸刻度 plt.xticks(range(min(a),max(a)+d,d)) # 78 81 84...150 # y軸表示在某個時長內有多少部電影 # 設置網格 plt.grid()plt.show()

4.繪制餅圖

plt.pie() 繪制餅圖
練習：統計5個國家每年人口的變化情況

# 練習：統計5個國家每年人口的變化情況 from matplotlib import pyplot as plt import pandas as pdfile_path = './population_data.json' df = pd.read_json(file_path) # print(df.info()) ''' <class 'pandas.core.frame.DataFrame'> RangeIndex: 12407 entries, 0 to 12406 Data columns (total 4 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 Country Name 12407 non-null object 1 Country Code 12407 non-null object 2 Year 12407 non-null int64 3 Value 12407 non-null float64 dtypes: float64(1), int64(1), object(2) memory usage: 387.8+ KB None''' # print(df.head()) '''Country Name Country Code Year Value 0 Arab World ARB 1960 96388069.0 1 Arab World ARB 1961 98882541.4 2 Arab World ARB 1962 101474075.8 3 Arab World ARB 1963 104169209.2 4 Arab World ARB 1964 106978104.6''' # 透視表 population_data = df.pivot_table(index='Country Name',columns='Year',values='Value') # print(population_data.head()) ''' Year 1960 1961 ... 2009 2010 Country Name ... Afghanistan 9671046.0 9859928.0 ... 3.343833e+07 34385000.0 Albania 1610565.0 1661158.0 ... 3.192723e+06 3205000.0 Algeria 10799997.0 11006643.0 ... 3.495017e+07 35468000.0 American Samoa 20041.0 20500.0 ... 6.731200e+04 68420.0 Andorra 13377.0 14337.0 ... 8.367700e+04 84864.0[5 rows x 51 columns]''' plt.figure(figsize=(15,8),dpi=80) # countries = df.iloc[:,0].drop_duplicates() # print(list(countries)) '''['Arab World', 'Caribbean small states', 'East Asia & Pacific (all income levels)', 'East Asia & Pacific ( developing only)', 'Euro area', 'Europe & Central Asia (all income levels)', 'Europe & Central Asia (developing only)', 'European Union', 'Heavily indebted poor countries (HIPC)', 'High income', 'High income: nonOECD', 'High income: OECD', 'Latin America & Caribbean (all income levels)', 'Latin America & Caribbean (developing only)', 'Least developed countries: UN classification', 'Low & middle income', 'Low income', 'Lower middle income', 'Middle East & North Africa (all income levels)', 'Middle East & North Africa (developing only)', 'Middle income', 'North America', 'OECD members', 'Other small states', 'Pacific island small states', 'Small states', 'South Asia', 'Sub-Saharan Africa (all income levels)', 'Sub-Saharan Africa (developing only)', 'Upper middle income', 'World', 'Afghanistan', 'Albania', 'Algeria', 'American Samoa', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas, The', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands', 'Central African Republic', 'Chad', 'Channel Islands', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba', 'Curacao', 'Cyprus', 'Czech Republic', 'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt, Arab Rep.', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Faeroe Islands', 'Fiji', 'Finland', 'France', 'French Polynesia', 'Gabon', 'Gambia, The', 'Georgia', 'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada', 'Guam', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras', 'Hong Kong SAR, China', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran, Islamic Rep.', 'Iraq', 'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Korea, Dem. Rep.', 'Korea, Rep.', 'Kosovo', 'Kuwait', 'Kyrgyz Republic', 'Lao PDR', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macao SAR, China', 'Macedonia, FYR', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania', 'Mauritius', 'Mayotte', 'Mexico', 'Micronesia, Fed. Sts.', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands', 'New Caledonia', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Northern Mariana Islands', 'Norway', 'Oman', 'Pakistan', 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar', 'Romania', 'Russian Federation', 'Rwanda', 'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Sint Maarten (Dutch part)', 'Slovak Republic', 'Slovenia', 'Solomon Islands', 'Somalia', 'South Africa', 'Spain', 'Sri Lanka', 'St. Kitts and Nevis', 'St. Lucia', 'St. Martin (French part)', 'St. Vincent and the Grenadines', 'Sudan', 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syrian Arab Republic', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan', 'Turks and Caicos Islands', 'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela, RB', 'Vietnam', 'Virgin Islands (U.S.)', 'West Bank and Gaza', 'Yemen, Rep.', 'Zambia', 'Zimbabwe'] ''' country_list = ['China','Australia','Germany','Brazil','Japan'] country_data = population_data.loc[country_list] # print(country_data) ''' Year 1960 1961 ... 2009 2010 Country Name ... China 667070000.0 660330000.0 ... 1.331380e+09 1.338300e+09 Australia 10276477.0 10483000.0 ... 2.195170e+07 2.229900e+07 Germany 72814900.0 73377632.0 ... 8.190231e+07 8.177700e+07 Brazil 72758801.0 74975656.0 ... 1.932466e+08 1.949460e+08 Japan 92500572.0 94943000.0 ... 1.275580e+08 1.274510e+08[5 rows x 51 columns] '''# country_data = population_data.loc[country_list].iloc[:,-1] #也可以 country_data = population_data.loc[country_list][2010] print(country_data) ''' Country Name China 1.338300e+09 Australia 2.229900e+07 Germany 8.177700e+07 Brazil 1.949460e+08 Japan 1.274510e+08 Name: 2010, dtype: float64 ''' # labels 標簽 autopct='%1.2f%%' 百分比 shadow=True 顯示陰影 explode部分抽出 plt.pie(country_data.values,labels=country_data.index,autopct='%1.2f%%',shadow=True,explode=[0,0,0,0,0.2]) plt.show()

5. 繪制3D立體圖形

mplot3d:matplotlib里用于繪制3D圖形的一個模塊
主要通過Axes3D完成3D繪圖
3D繪圖步驟
1.創建3D圖像畫布
2.通過ax繪制圖像

5.1 3D曲線

Axes3D.plot(xs,ys,zs,zdir)
xs,ys,zs:點的三維坐標
zdir:豎直軸,默認為z

# 練習：3D曲線 from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np# 準備數據 等差數列 0到15的1000個等差數列 zline = np.linspace(0, 15, 1000) xline = np.sin(zline) yline = np.cos(zline)# 創建3D畫板 fig = plt.figure() ax = Axes3D(fig)# 繪圖 plt.plot(xline, yline, zline) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') plt.savefig('fig.png',bbox_inches='tight') plt.show()

5.2 3D散點圖

Axes3D.scatter(xs,ys,zs,zdir,s,c,marker)
s:點的大小
c:點的顏色
marker:點的標記

# 練習：3D散點圖 from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np# 準備數據 創建0到100的隨機數 x = np.random.rand(100) y = np.random.rand(100) z = np.random.rand(100) x1 = np.random.rand(100) y1 = np.random.rand(100) z1 = np.random.rand(100)# 創建3D畫板 fig = plt.figure() ax = Axes3D(fig)# 繪圖 ax.scatter(x,y,z,s=10,c='r',marker='o') ax.scatter(x1,y1,z1,s=20,c='g',marker='^')# plt.savefig('fig.png',bbox_inches='tight') plt.show()

5.3 3D平面圖

# 練習：3D平面圖 from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np# 準備數據 x = np.arange(-4, 4, 0.25) y = np.arange(-4, 4, 0.25)x, y = np.meshgrid(x, y) # 創建3D畫板 fig = plt.figure() ax = Axes3D(fig)# 繪圖 ax.plot_surface(x, y, (x+y))plt.show()

6.子圖的使用

• plt.subplots(nrows,ncols,sharex,sharey)
  nrows,ncols 分割的行數和列數
  sharex,sharey 是否共享x軸、y軸

• 示例

""" 子圖的使用 """ from matplotlib import pyplot as pltfig,suplot_arr = plt.subplots(2,2,figsize=(8,8))suplot_arr[0,0].scatter(range(10),range(10)) suplot_arr[0,1].bar(range(10),range(10)) suplot_arr[1,0].hist(range(10),range(5),rwidth=0.8) y = [15,12,14,17,19,22,24,23,23,21,17,14] suplot_arr[1,1].plot(range(2,26,2),y)plt.show()

添加參數設置共享x軸：fig,suplot_arr = plt.subplots(2,2,figsize=(8,8),sharex=True) # 共享x軸 效果如下：

添加參數設置共享y軸：fig,suplot_arr = plt.subplots(2,2,figsize=(8,8),sharey=True) # 共享y軸 效果如下：

7.matplotlib常見問題總結

應該選擇那種圖形來呈現數據

matplotlib.plot(x,y)

matplotlib.bar(x,y)

matplotlib.scatter(x,y)

matplotlib.hist(data,bins,normed)

xticks和yticks的設置

label和title,grid的設置

繪圖的大小和保存圖片

8.matplotlib使用的流程總結

1.明確問題
2.選擇圖形的呈現方式
3.準備數據
4.繪圖和圖形完善

總結

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