python - 有条件地附加多行数据框

Question

我正在迭代一个数据帧，并尝试在第一个数据帧中的一行是特定值时从另一个数据帧中追加新值。

考虑以下两个数据框:

print(full_df)
                                             AccessName                                          PolicyArn
0                                   arn:aws:glue:sample  arn:aws:iam::971340810992:policy/service-role/...
1                                  arn:aws:glue:sample2  arn:aws:iam::971340810992:policy/service-role/...
2                                                   ---  arn:aws:iam::971340810992:policy/service-role/...
3                                  arn:aws:s3:::sample3  arn:aws:iam::971340810992:policy/service-role/...


print(side_df)
                                            AccessName
0                                          sample-test
1                                         query_sample
2                                     us-east-1-sample

如果full_df中的AccessName为某个值，则在full_df后追加side_df，让所有行的第二行值始终为 arn。

arn = 'fixed_value'
for index, row in full_df.iterrows():
    if row['AccessName'] == '---':
        #Here I don't know how I'd define the code to append the side_df values:
        #full_df['AccessName'] = side_df['AccessName']
        #full_df['PolicyArn'] = arn

是否最好在此时创建另一个 if 语句并迭代 side_df 值并逐行追加？

这个for函数嵌套在实际代码中，arn是动态生成的。

期望的输出:

                                             AccessName                                          PolicyArn
0                                   arn:aws:glue:sample  arn:aws:iam::971340810992:policy/service-role/...
1                                  arn:aws:glue:sample2  arn:aws:iam::971340810992:policy/service-role/...
2                                                   ---  arn:aws:iam::971340810992:policy/service-role/...
3                                           sample-test  fixed_value
4                                          query_sample  fixed_value
5                                      us-east-1-sample  fixed_value
6                                  arn:aws:s3:::sample3  arn:aws:iam::971340810992:policy/service-role/...

编写此代码的最佳方式是什么？

Answer 1

首先让我们构建您的side_df:

side_df = pd.DataFrame([['sample-test'], ['query_sample'], ['us-east-1-sample']]
                       , columns=['AccessName'])
fixed_series = pd.Series(['fixed_value'] * len(side_df), name='PolicyArn').to_frame()
side_df_extended = pd.concat([side_df, fixed_series], axis=1)
print(side_df_extended)

       AccessName    PolicyArn
0       sample-test  fixed_value
1      query_sample  fixed_value
2  us-east-1-sample  fixed_value

假设full_df如下:

    AccessName              PolicyArn
0   arn:aws:glue:sample     arn:aws:iam::971340810992:policy/service-role/
1   arn:aws:glue:sample2    arn:aws:iam::971340810992:policy/service-role/
2   arn:aws:s3:::sample3    arn:aws:iam::971340810992:policy/service-role/
3   arn:aws:glue:sample4    arn:aws:iam::971340810992:policy/service-role/
4   arn:aws:s3:::sample5    arn:aws:iam::971340810992:policy/service-role/

现在让我们获取具有您的条件的行的索引，例如:

indices = full_df['AccessName'] == 'arn:aws:glue:sample2'
rows = full_df[indices].index.tolist()
rows

[1]

现在，您想在条件发生后附加 side_df:

final_df = pd.concat([full_df.iloc[:(rows[0] + 1)], side_df_extended, full_df.iloc[(rows[0] + 1):]], ignore_index=True)
final_df

    AccessName              PolicyArn
0   arn:aws:glue:sample     arn:aws:iam::971340810992:policy/service-role/
1   arn:aws:glue:sample2    arn:aws:iam::971340810992:policy/service-role/
2   sample-test             fixed_value
3   query_sample            fixed_value
4   us-east-1-sample        fixed_value
5   arn:aws:s3:::sample3    arn:aws:iam::971340810992:policy/service-role/
6   arn:aws:glue:sample4    arn:aws:iam::971340810992:policy/service-role/
7   arn:aws:s3:::sample5    arn:aws:iam::971340810992:policy/service-role/