串列如何進入python資料框的單元格？

我有一些資料幀（df、tmp_df 和 final_df），我想將兩列 tmp_df 作為串列型別輸入到 final_df 的兩個不同單元格中。我的代碼和資料框如下所示。但是，回圈部分無法正常作業。stackoverflow 或其他網站中的其他問題，如果程式開頭的串列有可用的字典，請回答此問題。但在這里，tmp_df 資料幀在 for 回圈和每次迭代中都會發生變化，計算出合適的價格。此外，最相關的資料被建立，它們必須定位為 final_df 的相關單元格。

    import pandas as pd

    df = pd.read_csv('myfile.csv')
    tmp_df = pd.DataFrame()
    final_df = pd.DataFrame()
    tmp_df = df[df['Type'] == True]
    cnt = 0
    for c in tmp_df['Category']:
        #################
        # Apply some calculations and call different methods to do some changes on Price column of tmp_df.
        #################
        final_df.at[cnt,'Data'] = list(set(tmp_sub['Data']))
        final_df ['Category'], final_df['Acceptable'], final_df['Rank'],final_df['Price'] = \
        tmp_df['Rank'], list(tmp_sub['Price'])
        cnt  =1



df:

| Data    | Category | Acceptable | Rank | Price |
| ------- | -------- | ---------- | ---- | ----- |
| 30275   | A        | Yes        |  1   | 52787 |
| 35881   | C        | No         |  2   | 14804 |
| 28129   | C        | Yes        |  3   | 180543|
| 30274   | D        | No         |  2   | 8066  |
| 30351   | D        | Yes        |  3   | 273478|
| 35886   | A        | Yes        |  2   | 10808 |
| 39900   | C        | Yes        |  1   | 21893 |
| 35887   | A        | No         |  2   | 2244  |
| 35883   | A        | Yes        |  1   | 10066 |
| 35856   | D        | Yes        |  3   | 19011 |
| 35986   | C        | No         |  2   | 6895  |
| 30350   | D        | No         |  3   | 5243  |
| 28129   | C        | Yes        |  1   | 112859|
| 31571   | C        | Yes        |  1   | 20701 |

tmp_df:

| Data    | Category | Acceptable | Rank | Price |
| ------- | -------- | ---------- | ---- | ----- |
| 30275   | A        | Yes        |  1   | 52787 |
| 38129   | C        | Yes        |  3   | 180543|
| 30351   | D        | Yes        |  3   | 273478|
| 35886   | A        | Yes        |  2   | 10808 |
| 39900   | C        | Yes        |  1   | 21893 |
| 35883   | A        | Yes        |  1   | 10066 |
| 35856   | D        | Yes        |  3   | 19011 |
| 28129   | C        | Yes        |  1   | 112859|
| 31571   | C        | Yes        |  1   | 20701 |

The prices in the final dataframe (final_df) are changed because of the calculations over the tmp_df. Now, what should I do if I want the following result?

final_df:

| Data                      | Category | Acceptable | Rank | Price |
| -------                   | -------- | ---------- | ---- | ----- |
| [30275,35886,35883]       | A        | Yes        | [1,2]| 195543|
| [28129,39900,38129,31571] | C        | Yes        | [1,3]| 210089|
| [30351,35856]             | D        | Yes        |   3  | 113859|

uj5u.com熱心網友回復：

您可以聚合list和Price另一個聚合函式，例如sum，mean...：

#generate custom function for Price
def func(x):
    
    return x.sum()

d = {'Data':list,'Rank':lambda x: list(set(x)), 'Price':func}
final_df = (tmp_df.groupby(['Category','Acceptable'],as_index=False)
                  .agg(d)
                  .reindex(tmp_df.columns, axis=1))

d = {'Data':list,'Rank':lambda x: list(set(x)), 'Price':'max'}
final_df = (tmp_df.groupby(['Category','Acceptable'],as_index=False)
                  .agg(d)
                  .reindex(tmp_df.columns, axis=1))

print (final_df)
                           Data Category Acceptable    Rank   Price
0         [30275, 35886, 35883]        A        Yes  [1, 2]   52787
1  [38129, 39900, 28129, 31571]        C        Yes  [1, 3]  180543
2                [30351, 35856]        D        Yes     [3]  273478

使用自定義函式的解決方案：

def func1(x):
    return x.sum()


def f(x):

    a = list(x['Data'])
    b = list(set(x['Rank']))
    c = func1(x['Price'])
    
    return pd.Series({'Data':a,'Rank':b,'Price':c})


final_df = (tmp_df.groupby(['Category','Acceptable'])
                  .apply(f)
                  .reset_index()
                  .reindex(tmp_df.columns, axis=1))

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