英文:
replace blanks in the column with the values in the same column in DF based on the corresponding values in another column in pandas
问题
初始表格:
| Acc num | Bank name |
| 100250-- | NaN |
| 100250 | NaN |
| 100250 | NaN |
| 100250 | SBI |
| 200100 | NaN |
| 200100 | NaN |
| 200100 | ICICI |
| 200100 | NaN |
期望输出（根据pandas中另一列的相应值填充列中的空白值）:
| Acc num | Bank name |
| 100250-- | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
英文:
Initial table:
| Acc num | Bank name |
| 100250-- | NaN |
| 100250 | NaN |
| 100250 | NaN |
| 100250 | SBI |
| 200100 | NaN |
| 200100 | NaN |
| 200100 | ICICI |
| 200100 | NaN |
Desired output (fill the blanks in the column with the values in the same column in DF based on the corresponding values in another column in pandas):
| Acc num | Bank name |
| 100250-- | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
答案1
得分: 0
你可以在df的Acc num列上使用groupby，然后使用bfill和ffill来填充空白值，将它们填充为上方和下方的数值：
df['Bank name'] = df.groupby('Acc num')['Bank name'].bfill().ffill()
输出结果：
| Acc num | Bank name |
| 100250 | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
英文:
You can use groupby on df on column Acc num, and then use bfill and ffill to fill the blank values with values above and below:
df['Bank name'] = df.groupby('Acc num')['Bank name'].bfill().ffill()
Output:
| Acc num | Bank name |
| 100250 | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 100250 | SBI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
| 200100 | ICICI |
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