如何根据多列中的特定值将行值转换为列？

huangapple
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英文:

How to transform row values into column based on specific values from multiple columns?

问题

这是一个更大数据的示例。

我有一个类似这样的数据框：

  1. df = pd.DataFrame({"Year":[2023, 2023, 2023, 2024, 2024, 2024],
  2.                    "Value":[0, 2, 3, 1, 5, 2],
  3.                    "Field":["A", "A", "B", "A", "B", "B"],
  4.                    "ID":["X", "X", "X", "X", "Z", "Y"],
  5.                    "Type":["class1","class2","class1","class1","class1","class2"]})

我想要基于df["Type"]的值创建新的列，并根据列Field和ID中的相同数据获取列df["Value"]中的值。因此，我的输出应该类似于这样：

  1.    Year Field ID  class1  class2
  2. 0  2023     A  X     0.0     2.0
  3. 1  2023     B  X     3.0     NaN
  4. 2  2024     A  X     1.0     NaN
  5. 3  2024     B  Z     5.0     NaN
  6. 4  2024     B  Y     NaN     2.0

有人能帮助我吗？

英文:

This is an example of bigger data.

I have a dataframe like this:

  1. df = pd.DataFrame({"Year":[2023, 2023, 2023, 2024, 2024, 2024],
  2.                    "Value":[0, 2, 3, 1, 5, 2],
  3.                    "Field":["A", "A", "B", "A", "B", "B"],
  4.                    "ID":["X", "X", "X", "X", "Z", "Y"],
  5.                    "Type":["class1","class2","class1","class1","class1","class2"]})
  7. df
  8. Out[8]: 
  9.    Year  Value Field ID    Type
  10. 0  2023      0     A  X  class1
  11. 1  2023      2     A  X  class2
  12. 2  2023      3     B  X  class1
  13. 3  2024      1     A  X  class1
  14. 4  2024      5     B  Z  class1
  15. 5  2024      2     B  Y  class2

I would like to create new columns based on df["Type"] values and take the values from the column df["Value"] based on the same data in columns Field and ID. So my output should be something like this:

  1.    Year Field ID  class1  class2
  2. 0  2023     A  X     0.0     2.0
  3. 1  2023     B  X     3.0     NaN
  4. 2  2024     A  X     1.0     NaN
  5. 3  2024     B  Z     5.0     NaN
  6. 4  2024     B  Y     NaN     2.0

Anyone could help me?

答案1

得分: 1

  1. 使用 [`df.pivot`][1] 函数：
  3. ```python
  4. res = df.pivot(columns='Type', values='Value', index=['Year', 'Field', 'ID']).reset_index()
  1.    Type  Year Field ID  class1  class2
  2. 0  2023     A  X     0.0     2.0
  3. 1  2023     B  X     3.0     NaN
  4. 2  2024     A  X     1.0     NaN
  5. 3  2024     B  Y     NaN     2.0
  6. 4  2024     B  Z     5.0     NaN
  2. <details>
  3. <summary>英文:</summary>
  5. Apply [`df.pivot`][1]:
  7.     res = df.pivot(columns=&#39;Type&#39;, values=&#39;Value&#39;, index=[&#39;Year&#39;, &#39;Field&#39;, &#39;ID&#39;]).reset_index()
  10. ----------
  12.     Type  Year Field ID  class1  class2
  13.     0     2023     A  X     0.0     2.0
  14.     1     2023     B  X     3.0     NaN
  15.     2     2024     A  X     1.0     NaN
  16.     3     2024     B  Y     NaN     2.0
  17.     4     2024     B  Z     5.0     NaN
  20.   [1]: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot.html
  22. </details>

huangapple
  • 本文由 发表于 2023年3月10日 01:38:13
  • 转载请务必保留本文链接：https://go.coder-hub.com/75688185.html
  • pandas
  • pivot
  • python
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