Create subplot of multiple features (columns), by overlapping dataframes with a baseline dataframe, for every group/id in Python

huangapple
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英文:

Create subplot of multiple features (columns), by overlapping dataframes with a baseline dataframe, for every group/id in Python

问题

I have translated the code as requested. Here are the translated code sections for df_1, df_2, and df_3 overlapping with df_base:

For df_1 overlapped with df_base:

  1. plt_fig_verify = plt.figure(figsize=(10,8))
  3. ##########################################################################
  5. ## Plots for id1, overlap for df_1 and df_base
  7. ## Salary plot
  8. plt.subplot(3,3,1)
  9. plt.plot(df_base.groupby(by="id").get_group(1)['cycle_base'], df_base.groupby(by="id").get_group(1)['Salary_base'], 'b',  linewidth = '1', label ='Salary: df_base')
  10. plt.plot(df_1.groupby(by="id").get_group(1)['cycle'], df_1.groupby(by="id").get_group(1)['Salary_1'], 'r',  linewidth = '1', label ='Salary: df_1')
  11. plt.xlabel('cycle')
  12. plt.ylabel('wrt to id1')
  13. plt.legend()
  15. ## Children plot
  16. plt.subplot(3,3,2)
  17. plt.plot(df_base.groupby(by="id").get_group(1)['cycle_base'], df_base.groupby(by="id").get_group(1)['Children_base'], 'b',  linewidth = '1', label ='Children: df_base')
  18. plt.plot(df_1.groupby(by="id").get_group(1)['cycle'], df_1.groupby(by="id").get_group(1)['Children_1'], 'r',  linewidth = '1', label ='Children: df_1')
  19. plt.xlabel('cycle')
  20. plt.legend()
  22. ## Expenditure plot
  23. plt.subplot(3,3,3)
  24. plt.plot(df_base.groupby(by="id").get_group(1)['cycle_base'], df_base.groupby(by="id").get_group(1)['Expenditure_base'], 'b',  linewidth = '1', label ='Expenditure: df_base')
  25. plt.plot(df_1.groupby(by="id").get_group(1)['cycle'], df_1.groupby(by="id").get_group(1)['Expenditure_1'], 'r',  linewidth = '1', label ='Expenditure: df_1')
  26. plt.xlabel('cycle')
  27. plt.legend()
  29. ##########################################################################
  31. ## Plots for id2, overlap for df_1 and df_base
  33. ## Salary plot
  34. plt.subplot(3,3,4)
  35. plt.plot(df_base.groupby(by="id").get_group(2)['cycle_base'], df_base.groupby(by="id").get_group(2)['Salary_base'], 'b',  linewidth = '1', label ='Salary: df_base')
  36. plt.plot(df_1.groupby(by="id").get_group(2)['cycle'], df_1.groupby(by="id").get_group(2)['Salary_1'], 'r',  linewidth = '1', label ='Salary: df_1')
  37. plt.xlabel('cycle')
  38. plt.ylabel('wrt to id2')
  39. plt.legend()
  41. ## Children plot
  42. plt.subplot(3,3,5)
  43. plt.plot(df_base.groupby(by="id").get_group(2)['cycle_base'], df_base.groupby(by="id").get_group(2)['Children_base'], 'b',  linewidth = '1', label ='Children: df_base')
  44. plt.plot(df_1.groupby(by="id").get_group(2)['cycle'], df_1.groupby(by="id").get_group(2)['Children_1'], 'r',  linewidth = '1', label ='Children: df_1')
  45. plt.xlabel('cycle')
  46. plt.legend()
  48. ## Expenditure plot
  49. plt.subplot(3,3,6)
  50. plt.plot(df_base.groupby(by="id").get_group(2)['cycle_base'], df_base.groupby(by="id").get_group(2)['Expenditure_base'], 'b',  linewidth = '1', label ='Expenditure: df_base')
  51. plt.plot(df_1.groupby(by="id").get_group(2)['cycle'], df_1.groupby(by="id").get_group(2)['Expenditure_1'], 'r',  linewidth = '1', label ='Expenditure: df_1')
  52. plt.xlabel('cycle')
  53. plt.legend()
  55. ##########################################################################
  57. ## Plots for id3, overlap for df_1 and df_base
  59. ## Salary plot
  60. plt.subplot(3,3,7)
  61. plt.plot(df_base.groupby(by="id").get_group(3)['cycle_base'], df_base.groupby(by="id").get_group(3)['Salary_base'], 'b',  linewidth = '1', label ='Salary: df_base')
  62. plt.plot(df_1.groupby(by="id").get_group(3)['cycle'], df_1.groupby(by="id").get_group(3)['Salary_1'], 'r',  linewidth = '1', label ='Salary: df_1')
  63. plt.xlabel('cycle')
  64. plt.ylabel('wrt to id3')
  65. plt.legend()
  67. ## Children plot
  68. plt.subplot(3,3,8)
  69. plt.plot(df_base.groupby(by="id").get_group(3)['cycle_base'], df_base.groupby(by="id").get_group(3)['Children_base'], 'b',  linewidth = '1', label ='Children: df_base')
  70. plt.plot(df_
  72. <details>
  73. <summary>英文:</summary>
  75. I have a baseline dataframe (df_base) and 3 other dataframes (df_1/df_2/df_3) as such:
  77.     #Load the required libraries
  78.     import pandas as pd
  79.     import matplotlib.pyplot as plt
  80.     
  81.     ########################################################################################
  82.     
  83.     #Create dataframe_Baseline
  84.     data_set_base = {&#39;id&#39;: [1, 1, 1,1, 1, 1, 1, 1, 1,1,1,
  85.                    2, 2, 2, 2,2,2,
  86.                    3, 3, 3, 3, 3, 3, 3,3,],
  87.             &#39;cycle_base&#39;: [0.0, 0.2,0.4, 0.6, 0.8, 1,1.2,1.4,1.6,1.8,2.0,
  88.                       0.0, 0.2,0.4, 0.6,0.8,1,
  89.                       0.0, 0.2,0.4, 0.6, 0.8,1.0,1.2,1.4,],
  90.             &#39;Salary_base&#39;: [1, 4, 3, 8,2,9,10,5,1,6,1,
  91.                        1, 0, 1, 5,3,8,
  92.                        12, 3,29,10,11,1,0,3,],
  93.             &#39;Children_base&#39;: [2, 0, 1, 0, 2, 0, 0,1, 0,0,1,
  94.                          2, 1, 0, 0,1,1, 
  95.                          0, 1,2, 1, 1,0, 0,1,],
  96.             &#39;Expenditure_base&#39;: [20, 10, 128, 76, 30, 40, 85, 60, 27,45,84,
  97.                      25, 100, 120,82,100,110,
  98.                      28,  15,90, 80, 66, 120, 35, 67,],
  99.             }
  100.     
  101.     #Convert to dataframe_Baseline
  102.     df_base = pd.DataFrame(data_set_base)
  103.     print(&quot;\n df_base = \n&quot;,df_base)
  104.     
  105.     ########################################################################################
  106.     
  107.     
  108.     #Create dataframe_1
  109.     data_set_1 = {&#39;id&#39;: [1, 1, 1,1, 1, 1, 1, 1, 1,
  110.                    2, 2, 2, 2,
  111.                    3, 3, 3, 3, 3, 3, 3,3,],
  112.             &#39;cycle&#39;: [0.0, 0.2,0.4, 0.6, 0.8, 1,1.2,1.4,1.6,
  113.                       0.0, 0.2,0.4, 0.6,
  114.                       0.0, 0.2,0.4, 0.6, 0.8,1.0,1.2,1.4,],
  115.             &#39;Salary_1&#39;: [6, 7, 7, 7,8,9,10,11,12,
  116.                        30, 10, 20, 4,
  117.                        2, 1,19,0,11,2,28,5,],
  118.             &#39;Children_1&#39;: [1, 2, 1, 0, 1, 1, 0,2, 1,
  119.                          0, 1, 1, 2,  
  120.                          1, 2,0, 1, 2,0, 1,0,],
  121.             &#39;Expenditure_1&#39;: [141, 123, 128, 66, 66, 120, 141, 52, 52,
  122.                      141, 96, 120,120, 
  123.                      141,  15,123, 128, 66, 120, 141, 141,],
  124.             }
  125.     
  126.     #Convert to dataframe_1
  127.     df_1 = pd.DataFrame(data_set_1)
  128.     print(&quot;\n df_1 = \n&quot;,df_1)
  129.     
  130.     
  131.     ########################################################################################
  132.     
  133.     
  134.     #Create dataframe_2
  135.     data_set_2 = {&#39;id&#39;: [1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1,
  136.                    2, 2, 2, 2, 2, 2, 2,
  137.                    3, 3, 3, 3, 3, 3, 3,3,],
  138.             &#39;cycle&#39;: [0.0, 0.2,0.4, 0.6, 0.8, 1,1.2,1.4,1.6,1.8,2.0,2.2,
  139.                       0.0, 0.2,0.4, 0.6,0.8,1.0,1.2,
  140.                       0.0, 0.2,0.4, 0.6, 0.8,1.0,1.2,1.4,],
  141.             &#39;Salary_2&#39;: [7, 8, 8, 8,8,9,14,21,12,19,14,20,
  142.                        1, 6, 3, 8,4,9,8,
  143.                        6, 4,9,10,4,12,13,6,],
  144.             &#39;Children_2&#39;: [1, 0, 1, 1, 0, 1, 2,2, 0, 1, 0, 2,
  145.                          1, 0, 1, 2, 1, 1, 1, 
  146.                          1, 2,1, 0, 2,0, 1,0,],
  147.             &#39;Expenditure_2&#39;: [79, 82, 128, 66, 42, 120, 141, 52,96, 120, 141, 52,
  148.                      30, 96, 86,120, 91, 52,96,
  149.                      28,  15,51, 128, 76, 120, 87, 141,],
  150.             }
  151.     
  152.     #Convert to dataframe_2
  153.     df_2 = pd.DataFrame(data_set_2)
  154.     print(&quot;\n df_2 = \n&quot;,df_2)
  155.     
  156.     
  157.     ########################################################################################
  158.     
  159.     
  160.     #Create dataframe_3
  161.     data_set_3 = {&#39;id&#39;: [1, 1, 1,1, 1, 1, 
  162.                    2, 2, 2, 
  163.                    3, 3, 3, 3, 3, 3, 3,],
  164.             &#39;cycle&#39;: [0.0, 0.2,0.4, 0.6, 0.8, 1,
  165.                       0.0, 0.2,0.4, 
  166.                       0.0, 0.2,0.4, 0.6, 0.8,1.0,1.2,],
  167.             &#39;Salary_3&#39;: [2, 5, 2, 7,2,1,
  168.                        3, 7, 4, 
  169.                        2, 8,6,10,11,4,13,],
  170.             &#39;Children_3&#39;: [1, 0, 1, 0, 1, 0, 
  171.                          0, 1, 0, 
  172.                          1, 0,0, 1, 1,0,2,],
  173.             &#39;Expenditure_3&#39;: [100, 50, 10, 66, 66, 80, 
  174.                      10, 5, 80,
  175.                      70,  15,40, 85, 66, 93, 60, ],
  176.             }
  177.     
  178.     #Convert to dataframe_1
  179.     df_3 = pd.DataFrame(data_set_3)
  180.     print(&quot;\n df_3 = \n&quot;,df_3)
  187. The features are: &#39;Salary&#39;, &#39;Children&#39;, and &#39;Expenditure&#39;.
  189. Every dataframe (df_base/df_1/df_2/df_3) has ids from 1 to 4.
  191. Now for dataframes (df_1/df_2/df_3), for every id, I need to plot the features(Salary/Children/Expenditure) vs cycle, such that they overlap with df_base, in one single plot.
  193. For df_1 overlapping with df_base these are the codes:
  195.     plt_fig_verify = plt.figure(figsize=(10,8))
  196.     
  197.     ##########################################################################
  198.     
  199.     ## Plots for id1, overlap for df_1 and df_base
  200.     
  201.     ## Salary plot
  202.     plt.subplot(3,3,1)
  203.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  204.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(1)[&#39;Salary_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_1&#39;)
  205.     plt.xlabel(&#39;cycle&#39;)
  206.     plt.ylabel(&#39;wrt to id1&#39;)
  207.     plt.legend()
  208.     
  209.     ## Children plot
  210.     plt.subplot(3,3,2)
  211.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  212.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(1)[&#39;Children_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_1&#39;)
  213.     plt.xlabel(&#39;cycle&#39;)
  214.     plt.legend()
  215.     
  216.     ## Expenditure plot
  217.     plt.subplot(3,3,3)
  218.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  219.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(1)[&#39;Expenditure_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_1&#39;)
  220.     plt.xlabel(&#39;cycle&#39;)
  221.     plt.legend()
  222.     
  223.     ##########################################################################
  224.     
  225.     ## Plots for id2, overlap for df_1 and df_base
  226.     
  227.     ## Salary plot
  228.     plt.subplot(3,3,4)
  229.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  230.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(2)[&#39;Salary_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_1&#39;)
  231.     plt.xlabel(&#39;cycle&#39;)
  232.     plt.ylabel(&#39;wrt to id2&#39;)
  233.     plt.legend()
  234.     
  235.     ## Children plot
  236.     plt.subplot(3,3,5)
  237.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  238.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(2)[&#39;Children_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_1&#39;)
  239.     plt.xlabel(&#39;cycle&#39;)
  240.     plt.legend()
  241.     
  242.     ## Expenditure plot
  243.     plt.subplot(3,3,6)
  244.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  245.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(2)[&#39;Expenditure_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_1&#39;)
  246.     plt.xlabel(&#39;cycle&#39;)
  247.     plt.legend()
  248.     
  249.     ##########################################################################
  250.     
  251.     ## Plots for id3, overlap for df_1 and df_base
  252.     
  253.     ## Salary plot
  254.     plt.subplot(3,3,7)
  255.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  256.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(3)[&#39;Salary_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_1&#39;)
  257.     plt.xlabel(&#39;cycle&#39;)
  258.     plt.ylabel(&#39;wrt to id3&#39;)
  259.     plt.legend()
  260.     
  261.     ## Children plot
  262.     plt.subplot(3,3,8)
  263.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  264.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(3)[&#39;Children_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_1&#39;)
  265.     plt.xlabel(&#39;cycle&#39;)
  266.     plt.legend()
  267.     
  268.     ## Expenditure plot
  269.     plt.subplot(3,3,9)
  270.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  271.     plt.plot(df_1.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_1.groupby(by=&quot;id&quot;).get_group(3)[&#39;Expenditure_1&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_1&#39;)
  272.     plt.xlabel(&#39;cycle&#39;)
  273.     plt.legend()
  274.     
  275.     plt.show()
  278. The plot looks as such:
  280. [![enter image description here][1]][1]
  282. Likewise for df_2 overlapped with df_base: 
  284.     plt_fig_verify = plt.figure(figsize=(10,8))
  285.     
  286.     ##########################################################################
  287.     
  288.     ## Plots for id1, overlap for df_2 and df_base
  289.     
  290.     ## Salary plot
  291.     plt.subplot(3,3,1)
  292.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  293.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(1)[&#39;Salary_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_2&#39;)
  294.     plt.xlabel(&#39;cycle&#39;)
  295.     plt.ylabel(&#39;wrt to id1&#39;)
  296.     plt.legend()
  297.     
  298.     ## Children plot
  299.     plt.subplot(3,3,2)
  300.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  301.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(1)[&#39;Children_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_2&#39;)
  302.     plt.xlabel(&#39;cycle&#39;)
  303.     plt.legend()
  304.     
  305.     ## Expenditure plot
  306.     plt.subplot(3,3,3)
  307.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  308.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(1)[&#39;Expenditure_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_2&#39;)
  309.     plt.xlabel(&#39;cycle&#39;)
  310.     plt.legend()
  311.     
  312.     ##########################################################################
  313.     
  314.     ## Plots for id2, overlap for df_2 and df_base
  315.     
  316.     ## Salary plot
  317.     plt.subplot(3,3,4)
  318.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  319.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(2)[&#39;Salary_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_2&#39;)
  320.     plt.xlabel(&#39;cycle&#39;)
  321.     plt.ylabel(&#39;wrt to id2&#39;)
  322.     plt.legend()
  323.     
  324.     ## Children plot
  325.     plt.subplot(3,3,5)
  326.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  327.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(2)[&#39;Children_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_2&#39;)
  328.     plt.xlabel(&#39;cycle&#39;)
  329.     plt.legend()
  330.     
  331.     ## Expenditure plot
  332.     plt.subplot(3,3,6)
  333.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  334.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(2)[&#39;Expenditure_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_2&#39;)
  335.     plt.xlabel(&#39;cycle&#39;)
  336.     plt.legend()
  337.     
  338.     ##########################################################################
  339.     
  340.     ## Plots for id3, overlap for df_2 and df_base
  341.     
  342.     ## Salary plot
  343.     plt.subplot(3,3,7)
  344.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  345.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(3)[&#39;Salary_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_2&#39;)
  346.     plt.xlabel(&#39;cycle&#39;)
  347.     plt.ylabel(&#39;wrt to id3&#39;)
  348.     plt.legend()
  349.     
  350.     ## Children plot
  351.     plt.subplot(3,3,8)
  352.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  353.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(3)[&#39;Children_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_2&#39;)
  354.     plt.xlabel(&#39;cycle&#39;)
  355.     plt.legend()
  356.     
  357.     ## Expenditure plot
  358.     plt.subplot(3,3,9)
  359.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  360.     plt.plot(df_2.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_2.groupby(by=&quot;id&quot;).get_group(3)[&#39;Expenditure_2&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_2&#39;)
  361.     plt.xlabel(&#39;cycle&#39;)
  362.     plt.legend()
  363.     
  364.     plt.show()
  368. [![enter image description here][2]][2]
  370. And df_3 overlapped with df_base:
  372.     plt_fig_verify = plt.figure(figsize=(10,8))
  373.     
  374.     ##########################################################################
  375.     
  376.     ## Plots for id1, overlap for df_3 and df_base
  377.     
  378.     ## Salary plot
  379.     plt.subplot(3,3,1)
  380.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  381.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(1)[&#39;Salary_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_3&#39;)
  382.     plt.xlabel(&#39;cycle&#39;)
  383.     plt.ylabel(&#39;wrt to id1&#39;)
  384.     plt.legend()
  385.     
  386.     ## Children plot
  387.     plt.subplot(3,3,2)
  388.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  389.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(1)[&#39;Children_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_3&#39;)
  390.     plt.xlabel(&#39;cycle&#39;)
  391.     plt.legend()
  392.     
  393.     ## Expenditure plot
  394.     plt.subplot(3,3,3)
  395.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(1)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  396.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(1)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(1)[&#39;Expenditure_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_3&#39;)
  397.     plt.xlabel(&#39;cycle&#39;)
  398.     plt.legend()
  399.     
  400.     ##########################################################################
  401.     
  402.     ## Plots for id2, overlap for df_3 and df_base
  403.     
  404.     ## Salary plot
  405.     plt.subplot(3,3,4)
  406.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  407.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(2)[&#39;Salary_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_3&#39;)
  408.     plt.xlabel(&#39;cycle&#39;)
  409.     plt.ylabel(&#39;wrt to id2&#39;)
  410.     plt.legend()
  411.     
  412.     ## Children plot
  413.     plt.subplot(3,3,5)
  414.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  415.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(2)[&#39;Children_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_3&#39;)
  416.     plt.xlabel(&#39;cycle&#39;)
  417.     plt.legend()
  418.     
  419.     ## Expenditure plot
  420.     plt.subplot(3,3,6)
  421.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(2)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  422.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(2)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(2)[&#39;Expenditure_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_3&#39;)
  423.     plt.xlabel(&#39;cycle&#39;)
  424.     plt.legend()
  425.     
  426.     ##########################################################################
  427.     
  428.     ## Plots for id3, overlap for df_3 and df_base
  429.     
  430.     ## Salary plot
  431.     plt.subplot(3,3,7)
  432.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Salary_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_base&#39;)
  433.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(3)[&#39;Salary_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Salary: df_3&#39;)
  434.     plt.xlabel(&#39;cycle&#39;)
  435.     plt.ylabel(&#39;wrt to id3&#39;)
  436.     plt.legend()
  437.     
  438.     ## Children plot
  439.     plt.subplot(3,3,8)
  440.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Children_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_base&#39;)
  441.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(3)[&#39;Children_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Children: df_3&#39;)
  442.     plt.xlabel(&#39;cycle&#39;)
  443.     plt.legend()
  444.     
  445.     ## Expenditure plot
  446.     plt.subplot(3,3,9)
  447.     plt.plot(df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(3)[&#39;Expenditure_base&#39;], &#39;b&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_base&#39;)
  448.     plt.plot(df_3.groupby(by=&quot;id&quot;).get_group(3)[&#39;cycle&#39;], df_3.groupby(by=&quot;id&quot;).get_group(3)[&#39;Expenditure_3&#39;], &#39;r&#39;,  linewidth = &#39;1&#39;, label =&#39;Expenditure: df_3&#39;)
  449.     plt.xlabel(&#39;cycle&#39;)
  450.     plt.legend()
  451.     
  452.     plt.show()
  454. [![enter image description here][3]][3]
  457. Here I need to write the codes for the subplot function NINE times for df_1 vs df_base, NINE times for df_2 vs df_base, and NINE times for df_3 vs df_base. 
  459. SO a **total of 27 subplot function needs to be written.**
  462. However, is there any way out, by which we can have some iterative function and write the subplot function only once and get all the subplots in Python?
  464. Can somebody please help me out with this ?
  516.   [1]: https://i.stack.imgur.com/XF2Yy.png
  517.   [2]: https://i.stack.imgur.com/dyH0k.png
  518.   [3]: https://i.stack.imgur.com/THllv.png
  520. </details>
  523. # 答案1
  524. **得分**: 1
  526. 这是绘制`subplots`的我的版本，需要预先定义两个变量：
  528. ```python
  529. df_list = [df_1, df_2, df_3]
  530. key_list = ['Salary_base', 'Children_base', 'Expenditure_base']
  532. def subplot_func(df_base, df, key_list, axs, id_):
  533.     axs = axs.ravel()
  534.     for i in range(len(axs)):
  535.         key = key_list[i % 3]
  536.         group_id  = int(i/3+1)
  537.         
  538.         axs[i].plot(df_base.groupby(by="id").get_group(group_id)['cycle_base'], df_base.groupby(by="id").get_group(group_id)[key], 'b', linewidth='1', label=key.replace('_base', '') + ': df_base')
  539.         axs[i].plot(df.groupby(by="id").get_group(group_id)['cycle'], df.groupby(by="id").get_group(group_id)[key.replace('base', str(id_+1))], 'r', linewidth='1', label=key.replace('_base', '') + ': df_' + str(id_+1))
  540.         axs[i].set_xlabel('cycle')
  541.         if i % 3 == 0: 
  542.             axs[i].set_ylabel('wrt to id' + str(group_id))
  543.         axs[i].legend()
  545. for id_ in range(3):
  546.     fig, axs = plt.subplots(3,3, figsize=(10,8))
  547.     subplot_func(df_base, df_list[id_], key_list, axs, id_)
英文:

Here is my version for drawing the subplots, need to pre-define two variables :

  1. df_list = [df_1, df_2, df_3]
  2. key_list = [&#39;Salary_base&#39;, &#39;Children_base&#39;, &#39;Expenditure_base&#39;]
  3. def subplot_func(df_base, df, key_list, axs, id_):
  4. axs = axs.ravel()
  5. for i in range(len(axs)):
  6. key = key_list[i % 3]
  7. group_id  = int(i/3+1)
  8. axs[i].plot(df_base.groupby(by=&quot;id&quot;).get_group(group_id)[&#39;cycle_base&#39;], df_base.groupby(by=&quot;id&quot;).get_group(group_id)[key], &#39;b&#39;,  linewidth = &#39;1&#39;, label =key.replace(&#39;_base&#39;, &#39;&#39;) + &#39;: df_base&#39;)
  9. axs[i].plot(df.groupby(by=&quot;id&quot;).get_group(group_id)[&#39;cycle&#39;], df.groupby(by=&quot;id&quot;).get_group(group_id)[key.replace(&#39;base&#39;, str(id_+1))], &#39;r&#39;,  linewidth = &#39;1&#39;, label =key.replace(&#39;_base&#39;, &#39;&#39;)+&#39;: df_&#39; + str(id_+1))
  10. axs[i].set_xlabel(&#39;cycle&#39;)
  11. if i % 3 ==0: 
  12. axs[i].set_ylabel(&#39;wrt to id&#39; + str(group_id))
  13. axs[i].legend()
  14. for id_ in range(3):
  15. fig, axs = plt.subplots(3,3, figsize=(10,8))
  16. subplot_func(df_base, df_list[id_], key_list, axs, id_)

huangapple
  • 本文由 发表于 2023年6月22日 14:24:22
  • 转载请务必保留本文链接：https://go.coder-hub.com/76529078.html
  • dataframe
  • group-by
  • matplotlib
  • python-3.x
  • subplot
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