Repeat rows in DataFrame with respect to column 重复DataFrame中的行，以列为基准。

huangapple
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英文:

Repeat rows in DataFrame with respect to column

问题

我有一个 Pandas DataFrame，看起来像这样：

  1. df = pd.DataFrame({'col1': [1, 2, 3],
  2.                    'col2': [4, 5, 6],
  3.                    'col3': [7, 8, 9]})
  5. df
  6.    col1  col2  col3
  7. 0     1     4     7
  8. 1     2     5     8
  9. 2     3     6     9

我想创建一个 Pandas DataFrame，如下所示：

  1. df_new
  2.    col1  col2  col3
  3. 0     1     4     7
  4. 1     1     5     8
  5. 2     1     6     9
  6. 3     2     4     7
  7. 4     2     5     8
  8. 5     2     6     9
  9. 6     3     4     7
  10. 7     3     5     8
  11. 8     3     6     9

是否有内置的或组合的 Pandas 方法可以实现这个目标？

即使在 df 中有重复值，我希望输出的格式仍然相同。换句话说：

  1. df
  2.    col1  col2  col3
  3. 0     1     4     7
  4. 1     2     5     8
  5. 2     2     6     8
  7. df_new
  8.    col1  col2  col3
  9. 0     1     4     7
  10. 1     1     5     8
  11. 2     1     6     8
  12. 3     2     4     7
  13. 4     2     5     8
  14. 5     2     6     8
  15. 6     2     4     7
  16. 7     2     5     8
  17. 8     2     6     8
英文:

I have a Pandas DataFrame that looks like this:

  1. df = pd.DataFrame({'col1': [1, 2, 3],
  2.                    'col2': [4, 5, 6],
  3.                    'col3': [7, 8, 9]})
  5. df
  6.     col1    col2    col3
  7. 0      1       4       7
  8. 1      2       5       8
  9. 2      3       6       9

I would like to create a Pandas DataFrame like this:

  1. df_new
  2.     col1    col2    col3
  3. 0      1       4       7
  4. 1      1       5       8
  5. 2      1       6       9
  6. 3      2       4       7
  7. 4      2       5       8
  8. 5      2       6       9
  9. 6      3       4       7
  10. 7      3       5       8
  11. 8      3       6       9

Is there built-in or combination of built-in Pandas methods that can achieve this?

Even if there are duplicates in df, I would like the output to be the same format. In other words:

  1. df
  2.     col1    col2    col3
  3. 0      1       4       7
  4. 1      2       5       8
  5. 2      2       6       8
  7. df_new
  8.     col1    col2    col3
  9. 0      1       4       7
  10. 1      1       5       8
  11. 2      1       6       8
  12. 3      2       4       7
  13. 4      2       5       8
  14. 5      2       6       8
  15. 6      2       4       7
  16. 7      2       5       8
  17. 8      2       6       8

答案1

得分: 8

  1. import pandas as pd
  2. import numpy as np
  4. n=3
  6. df = pd.DataFrame({'col1': [1, 2, 3],
  7.                    'col2': [4, 5, 6],
  8.                    'col3': [7, 8, 9]})
  10. # Edited and added this new method.
  11. df2 = pd.DataFrame({df.columns[0]:np.repeat(df['col1'].values, n)})
  12. df2[df.columns[1:]] = df.iloc[:,1:].apply(lambda x: np.tile(x, n))
  14. """ Old method.
  15. for col in df.columns[1:]:
  16.    df2[col] = np.tile(df[col].values, n)
  17. """
  18. print(df2)
英文:

I would love to see a more pythonic or a 'pandas-exclusive' answer, but this one also works good!

  1. import pandas as pd
  2. import numpy as np
  4. n=3
  6. df = pd.DataFrame({'col1': [1, 2, 3],
  7.                    'col2': [4, 5, 6],
  8.                    'col3': [7, 8, 9]})
  10. # Edited and added this new method.
  11. df2 = pd.DataFrame({df.columns[0]:np.repeat(df['col1'].values, n)})
  12. df2[df.columns[1:]] = df.iloc[:,1:].apply(lambda x: np.tile(x, n))
  14. """ Old method.
  15. for col in df.columns[1:]:
  16.    df2[col] = np.tile(df[col].values, n)
  18. """
  19. print(df2)

答案2

得分: 7

以下是翻译好的部分：

"我也会选择使用交叉合并，正如@Henry在评论中建议的那样：

  1. out = df[['col1']].merge(df[['col2', 'col3']], how='cross').reset_index(drop=True)

输出：

  1.    col1  col2  col3
  2. 0     1     4     7
  3. 1     1     5     8
  4. 2     1     6     9
  5. 3     2     4     7
  6. 4     2     5     8
  7. 5     2     6     9
  8. 6     3     4     7
  9. 7     3     5     8
  10. 8     3     6     9

不同方法的比较：

Repeat rows in DataFrame with respect to column 重复DataFrame中的行，以列为基准。

请注意，@sammywemmy的方法在存在重复行时表现不同，导致无法进行可比较的时间测试。"

英文:

I would also have gone for a cross merge as suggested by @Henry in comments:

  1. out = df[['col1']].merge(df[['col2', 'col3']], how='cross').reset_index(drop=True)

Output:

  1.    col1  col2  col3
  2. 0     1     4     7
  3. 1     1     5     8
  4. 2     1     6     9
  5. 3     2     4     7
  6. 4     2     5     8
  7. 5     2     6     9
  8. 6     3     4     7
  9. 7     3     5     8
  10. 8     3     6     9

Comparison of the different approaches:

Repeat rows in DataFrame with respect to column 重复DataFrame中的行，以列为基准。

Note that @sammywemmy's approach behaves differently when rows are duplicated, which leads to a non comparable timing.

答案3

得分: 6

以下是您要的翻译：

您可以通过将数据框的副本连接在一起，将其中的 col1 替换为 col1 中的每个值：

  1. out = df.drop('col1', axis=1)
  2. out = pd.concat([out.assign(col1=c1) for c1 in df['col1']]).reset_index(drop=True)

输出结果：

  1.    col2  col3  col1
  2. 0     4     7     1
  3. 1     5     8     1
  4. 2     6     9     1
  5. 3     4     7     2
  6. 4     5     8     2
  7. 5     6     9     2
  8. 6     4     7     3
  9. 7     5     8     3
  10. 8     6     9     3

如果您愿意，您可以使用以下方法将列重新排序为原始顺序：

  1. out = out[['col1', 'col2', 'col3']]
英文:

You could concatenate copies of the dataframe, with col1 replaced in each copy by each of the values in col1:

  1. out = df.drop('col1', axis=1)
  2. out = pd.concat([out.assign(col1=c1) for c1 in df['col1']]).reset_index(drop=True)

Output:

  1.    col2  col3  col1
  2. 0     4     7     1
  3. 1     5     8     1
  4. 2     6     9     1
  5. 3     4     7     2
  6. 4     5     8     2
  7. 5     6     9     2
  8. 6     4     7     3
  9. 7     5     8     3
  10. 8     6     9     3

If you prefer, you can then re-order the columns back to the original using

  1. out = out[['col1', 'col2', 'col3']]

答案4

得分: 6

以下是翻译好的内容：

你可以使用 np.repeat 和 np.tile 来获得期望的输出：

  1. import numpy as np
  3. N = 3
  4. cols_to_repeat = ['col1']  # 1, 1, 1, 2, 2, 2
  5. cols_to_tile = ['col2', 'col3']  # 1, 2, 1, 2, 1, 2
  7. data = np.concatenate([np.tile(df[cols_to_tile].values.T, N).T,
  8.                        np.repeat(df[cols_to_repeat].values, N, axis=0)], axis=1)
  9. out = pd.DataFrame(data, columns=cols_to_tile + cols_to_repeat)[df.columns]

输出：

  1. >>> out
  2.    col1  col2  col3
  3. 0     1     4     7
  4. 1     1     5     8
  5. 2     1     6     9
  6. 3     2     4     7
  7. 4     2     5     8
  8. 5     2     6     9
  9. 6     3     4     7
  10. 7     3     5     8
  11. 8     3     6     9

你可以创建一个通用的函数：

  1. def repeat(df: pd.DataFrame, to_repeat: list[str], to_tile: list[str]=None) -> pd.DataFrame:
  2.     to_tile = to_tile if to_tile else df.columns.difference(to_repeat).tolist()
  4.     assert df.columns.difference(to_repeat + to_tile).empty, "所有列应该被重复或平铺"
  6.     data = np.concatenate([np.tile(df[to_tile].values.T, N).T,
  7.                            np.repeat(df[to_repeat].values, N, axis=0)], axis=1)
  9.     return pd.DataFrame(data, columns=to_tile + to_repeat)[df.columns]
  11. repeat(df, ['col1'])

用法：

  1. >>> repeat(df, ['col1'])
  2.    col1  col2  col3
  3. 0     1     4     7
  4. 1     1     5     8
  5. 2     1     6     9
  6. 3     2     4     7
  7. 4     2     5     8
  8. 5     2     6     9
  9. 6     3     4     7
  10. 7     3     5     8
  11. 8     3     6     9
英文:

You can use np.repeat and np.tile to get the expected output:

  1. import numpy as np
  3. N = 3
  4. cols_to_repeat = ['col1']  # 1, 1, 1, 2, 2, 2
  5. cols_to_tile = ['col2', 'col3']  # 1, 2, 1, 2, 1, 2
  7. data = np.concatenate([np.tile(df[cols_to_tile].values.T, N).T,
  8.                        np.repeat(df[cols_to_repeat].values, N, axis=0)], axis=1)
  9. out = pd.DataFrame(data, columns=cols_to_tile + cols_to_repeat)[df.columns]

Output:

  1. >>> out
  2.    col1  col2  col3
  3. 0     1     4     7
  4. 1     1     5     8
  5. 2     1     6     9
  6. 3     2     4     7
  7. 4     2     5     8
  8. 5     2     6     9
  9. 6     3     4     7
  10. 7     3     5     8
  11. 8     3     6     9

You can create a generic function:

  1. def repeat(df: pd.DataFrame, to_repeat: list[str], to_tile: list[str]=None) -> pd.DataFrame:
  2.     to_tile = to_tile if to_tile else df.columns.difference(to_repeat).tolist()
  4.     assert df.columns.difference(to_repeat + to_tile).empty, "all columns should be repeated or tiled"
  6.     data = np.concatenate([np.tile(df[to_tile].values.T, N).T,
  7.                            np.repeat(df[to_repeat].values, N, axis=0)], axis=1)
  9.     return pd.DataFrame(data, columns=to_tile + to_repeat)[df.columns]
  11. repeat(df, ['col1'])

Usage:

  1. >>> repeat(df, ['col1'])
  2.    col1  col2  col3
  3. 0     1     4     7
  4. 1     1     5     8
  5. 2     1     6     9
  6. 3     2     4     7
  7. 4     2     5     8
  8. 5     2     6     9
  9. 6     3     4     7
  10. 7     3     5     8
  11. 8     3     6     9

答案5

得分: 6

另一个可能的解决方案是基于 itertools.product 的：

  1. from itertools import product
  3. pd.DataFrame([[x, y[0], y[1]] for x, y in 
  4.               product(df['col1'], zip(df['col2'], df['col3']))], 
  5.              columns=df.columns)

输出：

  1.    col1  col2  col3
  2. 0     1     4     7
  3. 1     1     5     8
  4. 2     1     6     9
  5. 3     2     4     7
  6. 4     2     5     8
  7. 5     2     6     9
  8. 6     3     4     7
  9. 7     3     5     8
  10. 8     3     6     9
英文:

Another possible solution, which is based on itertools.product:

  1. from itertools import product
  3. pd.DataFrame([[x, y[0], y[1]] for x, y in 
  4.               product(df['col1'], zip(df['col2'], df['col3']))], 
  5.              columns=df.columns)

Output:

  1.    col1  col2  col3
  2. 0     1     4     7
  3. 1     1     5     8
  4. 2     1     6     9
  5. 3     2     4     7
  6. 4     2     5     8
  7. 5     2     6     9
  8. 6     3     4     7
  9. 7     3     5     8
  10. 8     3     6     9

答案6

得分: 6

一种选择是使用pyjanitor中的complete函数：

  1. # pip install pyjanitor
  2. import janitor 
  3. import pandas as pd
  5. df.complete('col1', ('col2', 'col3'))
  6.    col1  col2  col3
  7. 0     1     4     7
  8. 1     1     5     8
  9. 2     1     6     9
  10. 3     2     4     7
  11. 4     2     5     8
  12. 5     2     6     9
  13. 6     3     4     7
  14. 7     3     5     8
  15. 8     3     6     9

complete主要用于暴露缺失的行 - 上面的输出只是一个不错的附带效果。更合适但相对冗长的选项是使用expand_grid：

  1. # pip install pyjanitor
  2. import janitor as jn
  3. import pandas as pd
  5. others = {'df1': df.col1, 'df2': df[['col2', 'col3']]}
  6. jn.expand_grid(others=others).droplevel(axis=1, level=0)
  7.    col1  col2  col3
  8. 0     1     4     7
  9. 1     1     5     8
  10. 2     1     6     8
  11. 3     2     4     7
  12. 4     2     5     8
  13. 5     2     6     8
  14. 6     2     4     7
  15. 7     2     5     8
  16. 8     2     6     8
英文:

One option is with complete from pyjanitor:

  1. # pip install pyjanitor
  2. import janitor 
  3. import pandas as pd
  5. df.complete('col1', ('col2','col3'))
  6.    col1  col2  col3
  7. 0     1     4     7
  8. 1     1     5     8
  9. 2     1     6     9
  10. 3     2     4     7
  11. 4     2     5     8
  12. 5     2     6     9
  13. 6     3     4     7
  14. 7     3     5     8
  15. 8     3     6     9

complete primarily is for exposing missing rows - the output above just happens to be a nice side effect. A more appropriate, albeit quite verbose option is expand_grid:

  1. # pip install pyjanitor
  2. import janitor as jn
  3. import pandas as pd
  5. others = {'df1':df.col1, 'df2':df[['col2','col3']]}
  6. jn.expand_grid(others=others).droplevel(axis=1,level=0)
  7.    col1  col2  col3
  8. 0     1     4     7
  9. 1     1     5     8
  10. 2     1     6     8
  11. 3     2     4     7
  12. 4     2     5     8
  13. 5     2     6     8
  14. 6     2     4     7
  15. 7     2     5     8
  16. 8     2     6     8

答案7

得分: 0

这是使用带有键的concat方法的一种方式：

  1. pd.concat([df]*len(df), keys=df.pop('col1')).reset_index(level=0)

输出：

  1.        col1  col2  col3
  2.     0     1     4     7
  3.     1     1     5     8
  4.     2     1     6     9
  5.     0     2     4     7
  6.     1     2     5     8
  7.     2     2     6     9
  8.     0     3     4     7
  9.     1     3     5     8
  10.     2     3     6     9
英文:

Here is a way using the concat with keys:

  1. pd.concat([df]*len(df),keys = df.pop('col1')).reset_index(level=0)

Output:

  1.    col1  col2  col3
  2. 0     1     4     7
  3. 1     1     5     8
  4. 2     1     6     9
  5. 0     2     4     7
  6. 1     2     5     8
  7. 2     2     6     9
  8. 0     3     4     7
  9. 1     3     5     8
  10. 2     3     6     9

huangapple
  • 本文由 发表于 2023年5月26日 12:01:39
  • 转载请务必保留本文链接：https://go.coder-hub.com/76337589.html
  • dataframe
  • pandas
  • python
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