查找表中的查找字符串值以填充第二个数据帧 [英] lookup string values in lookup table to populate second dataframe

问题描述

我有两个数据框main_df:

  | header_1
0 | value_1
1 | value_2
2 | value_3
3 | value_1

和查找数据帧lookup_df:

  | header_1 | header_2
0 | value_1 | lookup_value_1
1 | value_2 | lookup_value_2
2 | value_3 | lookup_value_3
3 | value_4 | lookup_value_4

main_df中的值不是唯一的. lookup_df中的值是唯一的.

The values in main_df are not unique. The values in `lookup_df' are unique.

我只是想用lookup_df中的相应lookup_value填充main df中的新列.

I simply want to populate a new column in main df with the corresponding lookup_value from lookup_df.

尝试了各种方法，包括.merge，.join，.map和.lookup.

Have tried various approaches including .merge, .join, .map and .lookup.

main_df = pd.merge(main_df, lookup_df, how='inner', on=['header_1'])

我正在寻找的结果是:

  | header_1 | header_2
0 | value_1 | lookup_value_1
1 | value_2 | lookup_value_2
2 | value_3 | lookup_value_3
3 | value_1 | lookup_value_1

推荐答案

您可以使用或者更快一点是转换Series to_dict :

Or a bit faster is convert Series to_dict:

main_df['header_2'] = main_df['header_1'].map(lookup_df.set_index('header_1')['header_2']
                                                       .to_dict())
print (main_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3
3  value_1  lookup_value_1

时间:

#[400000 rows x 1 columns]
main_df = pd.concat([main_df]*100000).reset_index(drop=True)

In [139]: %timeit pd.merge(main_df, lookup_df, how='left', on=['header_1'])
10 loops, best of 3: 73.1 ms per loop

In [140]: %timeit main_df['header_1'].map(lookup_df.set_index('header_1')['header_2'])
10 loops, best of 3: 35.7 ms per loop

In [141]: %timeit main_df['header_1'].map(lookup_df.set_index('header_1')['header_2'].to_dict())
10 loops, best of 3: 35.1 ms per loop

您需要lookup_df中列header_1的唯一值，一种可能的解决方案是

You need unique values of column header_1 in lookup_df, one possible solution is drop_duplicates:

print (lookup_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3
3  value_1  lookup_value_4

#keep first value, default parameter keep='first'
lookup_df = lookup_df.drop_duplicates(['header_1'])
print (lookup_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3

#keep last value
lookup_df1 = lookup_df.drop_duplicates(['header_1'], keep='last')
print (lookup_df1)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3

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