使用字典创建的Pandas DataFrame与使用列表创建的DataFrame相比。

huangapple
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英文:

Pandas DataFrame Created from Dictionary vs Created from List

问题

以下是代码的翻译部分：

  1. # 使用列表创建的DataFrame，使其表现得像使用字典创建的DataFrame一样是否有一行或两行代码？
  3. # 从字典创建的DataFrame，这是有效的：
  4. import pandas as pd
  5. data = {'Salary': [30000, 40000, 50000, 85000, 75000],
  6.         'Exp': [1, 3, 5, 10, 25],
  7.         'Gender': ['M', 'F', 'M', 'F', 'M']}
  8. df = pd.DataFrame(data)
  9. print(df), print()
  11. new_df1 = df[df['Salary'] >= 50000]
  12. print(new_df1), print()
  14. new_df2 = df.sort_values(['Exp'], axis=0, ascending=[False])
  15. print(new_df2)
  17. # 这不适用于使用df函数、排序和条件：
  18. data = [['Salary', 'Exp', 'Gender'], [30000, 1, 'M'],
  19.         [40000, 3, 'F'], [50000, 5, 'M'], [85000, 10, 'F'], [75000, 25, 'M']]
  21. df = pd.DataFrame(data)
  22. print(df), print()
  24. new_df1 = df[df['Salary'] >= 50000]  # 不起作用
  25. print(new_df1), print()
  27. new_df2 = df.sort_values(['Exp'], axis=0, ascending=[False])  # 同样不起作用
  28. print(new_df2)

请注意，这是代码的翻译，不包括任何其他内容。

英文:

Is there a line or two of code that would make the DataFrame created from lists behave like the one created from a dictionary?

  1. #DataFrame created from dictionary, this works:
  2. import pandas as pd
  3. data= {'Salary': [30000, 40000, 50000, 85000, 75000],            
  4.         'Exp': [1, 3, 5, 10, 25],          
  5.         'Gender': ['M','F', 'M', 'F', 'M']} 
  6. df = pd.DataFrame(data)
  7. print(df), print()
  9. new_df1 = df[df['Salary'] >= 50000]
  10. print(new_df1), print()
  12. new_df2 = df.sort_values(['Exp'], axis = 0, ascending=[False])
  13. print(new_df2)
  16. #This doesn't work with the df.functions, sort and conditionals    
  17. data = [['Salary', 'Exp', 'Gender'],[30000, 1, 'M'],
  18.         [40000, 3, 'F'], [50000, 5, 'M'], [85000, 10, 'F'], [75000, 25, 'M']]
  20. df = pd.DataFrame(data)
  21. print(df), print()
  23. new_df1 = df[df['Salary'] >= 50000]  #doesn't work
  24. print(new_df1), print()
  26. new_df2 = df.sort_values(['Exp'], axis = 0, ascending=[False])  #ditto
  27. print(new_df2)

答案1

得分: 1

在你的第二段代码中，你没有将第一个子列表用作列名，而是用作数据。请将第一个子列表作为DataFrame构造函数的columns参数传递：

  1. df = pd.DataFrame(data[1:], columns=data[0])

输出：

  1.    Salary  Exp Gender
  2. 0   30000    1      M
  3. 1   40000    3      F
  4. 2   50000    5      M
  5. 3   85000   10      F
  6. 4   75000   25      M
为什么你的代码失败了

你的代码错误地将第一个子列表映射为数据：

  1. pd.DataFrame(data)
  3.         0    1       2   # 错误的列名
  4. 0  Salary  Exp  Gender   # 这不应该是数据行
  5. 1   30000    1       M
  6. 2   40000    3       F
  7. 3   50000    5       M
  8. 4   85000   10       F
  9. 5   75000   25       M
完整代码：
  1. df = pd.DataFrame(data[1:], columns=data[0])
  2. print(df), print()
  4. new_df1 = df[df['Salary'] >= 50000]  # 无法工作
  5. print(new_df1), print()
  7. new_df2 = df.sort_values(['Exp'], axis=0, ascending=[False])  # 同样无法工作
  8. print(new_df2)

输出：

  1.    Salary  Exp Gender
  2. 0   30000    1      M
  3. 1   40000    3      F
  4. 2   50000    5      M
  5. 3   85000   10      F
  6. 4   75000   25      M
  8.    Salary  Exp Gender
  9. 2   50000    5      M
  10. 3   85000   10      F
  11. 4   75000   25      M
  13.    Salary  Exp Gender
  14. 4   75000   25      M
  15. 3   85000   10      F
  16. 2   50000    5      M
  17. 1   40000    3      F
  18. 0   30000    1      M
英文:

In your second code, you're not using the first sublist as column names but rather data.

Pass instead the first sublist as the columns parameter of your DataFrame constructor:

  1. df = pd.DataFrame(data[1:], columns=data[0])

Output:

  1.    Salary  Exp Gender
  2. 0   30000    1      M
  3. 1   40000    3      F
  4. 2   50000    5      M
  5. 3   85000   10      F
  6. 4   75000   25      M
why your code failed

You code was incorrectly mapping the first sublist as data:

  1. pd.DataFrame(data)
  3.         0    1       2   # incorrect header
  4. 0  Salary  Exp  Gender   # this shouldn't be a data row
  5. 1   30000    1       M
  6. 2   40000    3       F
  7. 3   50000    5       M
  8. 4   85000   10       F
  9. 5   75000   25       M
full code:
  1. df = pd.DataFrame(data[1:], columns=data[0])
  2. print(df), print()
  4. new_df1 = df[df['Salary'] >= 50000]  #doesn't work
  5. print(new_df1), print()
  7. new_df2 = df.sort_values(['Exp'], axis = 0, ascending=[False])  #ditto
  8. print(new_df2)

Output:

  1.    Salary  Exp Gender
  2. 0   30000    1      M
  3. 1   40000    3      F
  4. 2   50000    5      M
  5. 3   85000   10      F
  6. 4   75000   25      M
  8.    Salary  Exp Gender
  9. 2   50000    5      M
  10. 3   85000   10      F
  11. 4   75000   25      M
  13.    Salary  Exp Gender
  14. 4   75000   25      M
  15. 3   85000   10      F
  16. 2   50000    5      M
  17. 1   40000    3      F
  18. 0   30000    1      M

答案2

得分: 1

这里需要通过所有值创建DataFrame，不包括第一行，并传递参数columns：

  1. # 这不适用于df函数、排序和条件
  2. data = [['Salary', 'Exp', 'Gender'], [30000, 1, 'M'],
  3.         [40000, 3, 'F'], [50000, 5, 'M'], [85000, 10, 'F'], [75000, 25, 'M']]
  5. df = pd.DataFrame(data[1:], columns=data[0])
  6. print(df), print()
  7.    Salary  Exp Gender
  8. 0   30000    1      M
  9. 1   40000    3      F
  10. 2   50000    5      M
  11. 3   85000   10      F
  12. 4   75000   25      M
  14. new_df1 = df[df['Salary'] >= 50000]  # 运行良好
  15. print(new_df1), print()
  16.    Salary  Exp Gender
  17. 2   50000    5      M
  18. 3   85000   10      F
  19. 4   75000   25      M
  21. new_df2 = df.sort_values(['Exp'], axis=0, ascending=[False])  # 同样适用
  22. print(new_df2)
  24.    Salary  Exp Gender
  25. 4   75000   25      M
  26. 3   85000   10      F
  27. 2   50000    5      M
  28. 1   40000    3      F
  29. 0   30000    1      M
英文:

Here is necessary create DataFrame by all values without first and pass parameter columns:

  1. #This doesn't work with the df.functions, sort and conditionals    
  2. data = [['Salary', 'Exp', 'Gender'],[30000, 1, 'M'],
  3.         [40000, 3, 'F'], [50000, 5, 'M'], [85000, 10, 'F'], [75000, 25, 'M']]
  5. df = pd.DataFrame(data[1:], columns=data[0])
  6. print(df), print()
  7.    Salary  Exp Gender
  8. 0   30000    1      M
  9. 1   40000    3      F
  10. 2   50000    5      M
  11. 3   85000   10      F
  12. 4   75000   25      M
  14. new_df1 = df[df['Salary'] >= 50000]  #working well
  15. print(new_df1), print()
  16.    Salary  Exp Gender
  17. 2   50000    5      M
  18. 3   85000   10      F
  19. 4   75000   25      M
  21. new_df2 = df.sort_values(['Exp'], axis = 0, ascending=[False])  #ditto
  22. print(new_df2)
  24.    Salary  Exp Gender
  25. 4   75000   25      M
  26. 3   85000   10      F
  27. 2   50000    5      M
  28. 1   40000    3      F
  29. 0   30000    1      M

huangapple
  • 本文由 发表于 2023年3月15日 20:07:32
  • 转载请务必保留本文链接：https://go.coder-hub.com/75744477.html
  • dataframe
  • dictionary
  • list
  • pandas
  • python
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