如何根據條件更新單元格值？

Question

大家，我是 Python 的絕對初學者，目前獨自學習。 我遇到了一個問題，我希望我能找到有才華的人來教我如何解決這個問題。

1. 我有兩個不同的 excel 來比較...

    Data1.xlsx Data2.xlsx | Name | City | Reg Date | Gender | Check In | Check In Date | | Name | Reg Date | |Alex | Hong Kong | 2021-06-30 | Male | Y | 2021-06-30 | |Annie | 2021-07-01 | |Annie | Hong Kong | 2021-07-01 | Female | | | |Billy | 2021-07-02 | |Bob | Taipei | 2021-06-28 | Male | Y | 2021-06-28 | |Cathrine | 2021-07-03 | |Lucy | Tokyo | 2021-06-28 | Female | Y | 2021-06-28 | |David | 2021-07-04 | |David | London | 2021-07-04 | Male | | | |Eric | 2021-07-04 | |Kate | New York | 2021-07-03 | Female | | | |Cathrine | London | 2021-07-03 | Female | | | |Rose | Hong Kong | 2021-07-04 | Female | | |

2. 使用Name和Reg Date作為鍵合並

   import openpyxl as xl import pandas as pd import numpy as np dt1 = pd.read_excel('Data1.xlsx') dt2 = pd.read_excel('Data2.xlsx') df_merge = pd.merge(dt1, dt2[['Name', 'Reg Date']], on=['Name', 'Reg Date'], how='left', indicator=True) Name City Reg Date Gender Check In Check In Date _merge 0 Alex Hong Kong 2021-06-30 Male Y 2021-06-30 left_only 1 Annie Hong Kong 2021-07-01 Female NaN NaN both 2 Bob Taipei 2021-06-28 Male Y 2021-06-28 left_only 3 Lucy Tokyo 2021-06-28 Female Y 2021-06-28 left_only 4 David London 2021-07-04 Male NaN NaN both 5 Kate New York 2021-07-03 Female NaN NaN left_only 6 Cathrine London 2021-07-03 Female NaN NaN both 7 Rose Hong Kong 2021-07-04 Female NaN NaN left_only

3. 如何檢查both是否相等以填寫Check In is Y並將Reg Date復制到Check In Date ？

    for a in df_merge.iloc[:, [7]].values: if a == 'both':

Answer 1

使用 boolean 索引和fillna ：

cols = ['Check In', 'Check In Date']
mask = df_merged['_merge'].eq('both')

df_merged.loc[mask, cols] = \
    df_merged.loc[mask, cols].fillna({'Check In': 'Y',
                                      'Check In Date': df_merged['Reg Date']})

Output：

>>> df_merged
       Name       City    Reg Date  Gender Check In Check In Date     _merge
0      Alex  Hong Kong  2021-06-30    Male        Y    2021-06-30  left_only
1     Annie  Hong Kong  2021-07-01  Female        Y    2021-07-01       both
2       Bob     Taipei  2021-06-28    Male        Y    2021-06-28  left_only
3      Lucy      Tokyo  2021-06-28  Female        Y    2021-06-28  left_only
4     David     London  2021-07-04    Male        Y    2021-07-04       both
5      Kate   New York  2021-07-03  Female      NaN           NaN  left_only
6  Cathrine     London  2021-07-03  Female        Y    2021-07-03       both
7      Rose  Hong Kong  2021-07-04  Female      NaN           NaN  left_only