在 pandas 中查找 3 列的可能唯一組合

Question

我試圖在 pandas 中找到 3 個變量列的所有可能組合。 示例 df 如下所示：

          Variable_Name Variable1 Variable2 Variable3
0                  X      6.0%      8.0%     10.0%
1                  Y      3.0%      4.0%      5.0%
2                  Z      1.0%      3.0%      5.0%

這些組合只能從該列獲取值，而不能將值移動到其他列，例如，使用 4.0% 作為“X”是不正確的。

嘗試使用itertools.combinations 、 itertools.product 、 itertools.permutation但這些結果給出了所有可能的組合。

我希望結果看起來像這樣，給出 27 種可能的組合：

     Y      X     Z
0   3.0%   6.0%  1.0%
1   3.0%   6.0%  3.0%
2   3.0%   6.0%  5.0%
3   3.0%   8.0%  1.0%
4   3.0%   8.0%  3.0%
5   3.0%   8.0%  5.0%
6   3.0%  10.0%  1.0%
7   3.0%  10.0%  3.0%
8   3.0%  10.0%  5.0%
9   4.0%   8.0%  3.0%
10  4.0%   8.0%  1.0%
11  4.0%   8.0%  5.0%
12  4.0%   6.0%  1.0%
13  4.0%   6.0%  3.0%
14  4.0%   6.0%  5.0%
15  4.0%  10.0%  1.0%
16  4.0%  10.0%  3.0%
17  4.0%  10.0%  5.0%
18  5.0%  10.0%  5.0%
19  5.0%  10.0%  1.0%
20  5.0%  10.0%  3.0%
21  5.0%   8.0%  1.0%
22  5.0%   8.0%  3.0%
23  5.0%   8.0%  5.0%
24  5.0%   6.0%  1.0%
25  5.0%   6.0%  3.0%
26  5.0%   6.0%  5.0%

任何幫助將不勝感激。

Answer 1

讓我們嘗試連續交叉合並每個變量的值：

from functools import reduce

import pandas as pd

df = pd.DataFrame({'Variable_Name': {0: 'X', 1: 'Y', 2: 'Z'},
                   'Variable1': {0: '6.0%', 1: '3.0%', 2: '1.0%'},
                   'Variable2': {0: '8.0%', 1: '4.0%', 2: '3.0%'},
                   'Variable3': {0: '10.0%', 1: '5.0%', 2: '5.0%'}})

# Save Var Names for later
var_names = df['Variable_Name']

# Get Variables Options in Own Rows
new_df = df.set_index('Variable_Name').stack() \
    .droplevel(1, 0) \
    .reset_index()

# Get Collection of DataFrames each with its own variable
dfs = tuple(new_df[new_df['Variable_Name'].eq(v)]
            .drop(columns=['Variable_Name']) for v in var_names)
# Successive Cross Merges
new_df = reduce(lambda left, right: pd.merge(left, right, how='cross'), dfs)
# Fix Column Names
new_df.columns = var_names
# Fix Axis Names
new_df = new_df.rename_axis(None, axis=1)

# For Display
print(new_df.to_string())

Output：

        X     Y     Z
0    6.0%  3.0%  1.0%
1    6.0%  3.0%  3.0%
2    6.0%  3.0%  5.0%
3    6.0%  4.0%  1.0%
4    6.0%  4.0%  3.0%
5    6.0%  4.0%  5.0%
6    6.0%  5.0%  1.0%
7    6.0%  5.0%  3.0%
8    6.0%  5.0%  5.0%
9    8.0%  3.0%  1.0%
10   8.0%  3.0%  3.0%
11   8.0%  3.0%  5.0%
12   8.0%  4.0%  1.0%
13   8.0%  4.0%  3.0%
14   8.0%  4.0%  5.0%
15   8.0%  5.0%  1.0%
16   8.0%  5.0%  3.0%
17   8.0%  5.0%  5.0%
18  10.0%  3.0%  1.0%
19  10.0%  3.0%  3.0%
20  10.0%  3.0%  5.0%
21  10.0%  4.0%  1.0%
22  10.0%  4.0%  3.0%
23  10.0%  4.0%  5.0%
24  10.0%  5.0%  1.0%
25  10.0%  5.0%  3.0%
26  10.0%  5.0%  5.0%

Answer 2

您可以使用CROSS JOIN 。 在 pandas 中，您可以使用pd.merge()或pd.DataFrame.join()參數how='cross' 。 但在交叉加入之前，您需要將每個變量放在 dataframe 中，采用長（非透視）格式（您的表格是寬格式（透視））。

df_X = df.loc[df['Variable_Name'] == 'X', ['Variable1', 'Variable2', 'Variable3']].T
df_Y = df.loc[df['Variable_Name'] == 'Y', ['Variable1', 'Variable2', 'Variable3']].T
df_Z = df.loc[df['Variable_Name'] == 'Z', ['Variable1', 'Variable2', 'Variable3']].T

cross_join_df = df_X.join(df_Y, how='cross').join(df_Z, how='cross')
cross_join_df.columns = ['X','Y','Z']

如果你需要在循環中使用代碼，它會是這樣的。

variables = df['Variable_Name'].unique()
columns_to_cross = ['Variable1', 'Variable2', 'Variable3']
cross_join_df = df.loc[df['Variable_Name'] == variables[0], columns_to_cross].T
for var in variables[1:]:
    to_join_df = df.loc[df['Variable_Name'] == var, columns_to_cross].T
    cross_join_df = pd.merge(cross_join_df, to_join_df, how='cross')
cross_join_df.columns = variables