检查一个字符串是否包含来自不同数据帧的另一个字符串-Python

Question

I have two Dataframes with different columns and size.

The first one has some columns and one of them is a string field (column 1). The second dataframe has 2 columns, one is a string field (column 4) with 2 words separated by comma and another is a integer field (column 5).

I need to verify that column 1 in Dataframe 1 has the words in column 4 Dataframe 2 and fill in Dataframe 1 with the corresponding information in dataframe 2.

Example:

df1
    column 1                                column 2          column 3
0   bla bla sample1 bla bla sample2         a                 f
1   bla bla sample1 bla bla sample5         b                 g
2   bla bla sample3 bla bla sample4         c                 h
3   bla bla sample8 bla bla sample7         d                 i
4   bla bla sample1 bla bla sample2         e                 j

 df2
    column 4                       column 5
0   ('sample1', 'sample2'),        50
1   ('sample3', 'sample4'),        35 
2   ('sample1', 'sample5')         18

I need the output:

Output:
df1
    column 1                               column 2  column 3  column 4                     column 5
0   bla bla sample1 bla bla sample2        a         f         ('sample1', 'sample2')     50
1   bla bla sample1 bla bla sample5        b         g         ('sample1', 'sample5')     18
2   bla bla sample3 bla bla sample4        c         h         ('sample3', 'sample4')     35
3   bla bla sample8 bla bla sample7        d         i         NaN        
4   bla bla sample1 bla bla sample2        e         j         ('sample1', 'sample2')     50

Any ideas?

Thanks!

Answer 1

I don't guarantee this will be particularly fast, but it gets the job done. We'll use set logic to check for matches. We have to jump through some hoops so that we can store a list of tuples of the matches. I don't think this is a particularly good idea.

import numpy as np
import pandas as pd

df1['setc'] = df1['column 1'].str.split().apply(set)
# Initialize so addition works
df1['column 4'] = [[] for i in range(len(df1))]
df1['column 5'] = 0

for idx, row in df2.iterrows():
    m = (df1.setc.values & set(row['column 4'])) == set(row['column 4'])
    df1.loc[m, 'column 4'] += pd.Series([[row['column 4']] for x in range(len(m))])[m]
    df1.loc[m, 'column 5'] += row['column 5']

df1 = df1.drop(columns='setc')
# NaN where nothing matched
df1.loc[df1['column 4'].str.len().eq(0), ['column 4', 'column 5']] = np.NaN

Output:

                          column 1 column 2 column 3                                  column 4  column 5
0  bla bla sample1 sample5 sample2        a        f  [(sample1, sample2), (sample1, sample5)]      68.0
1  bla bla sample1 bla bla sample5        b        g                      [(sample1, sample5)]      18.0
2  bla bla sample3 bla bla sample4        c        h                      [(sample3, sample4)]      35.0
3  bla bla sample8 bla bla sample7        d        i                                       NaN       NaN
4  bla bla sample1 bla bla sample2        e        j                      [(sample1, sample2)]      50.0