如何将一个数据框中的列表列与另一数据框中的字符串列连接在一起？

Question

I have two dataframes. The first one (let's call it A) has a column (let's call it 'col1') whose elements are lists of strings. The other one (let's call it B) has a column (let's call it 'col2') whose elements are strings. I want to do a join between these two dataframes where B.col2 is in the list in A.col1. This is one-to-many join.

Also, I need the solution to be scalable since I wanna join two dataframes with hundreds of thousands of rows.

I have tried concatenating the values in A.col1 and creating a new column (let's call it 'col3') and joining with this condition: A.col3.contains(B.col2). However, my understanding is that this condition triggers a cartesian product between the two dataframes which I cannot afford considering the size of the dataframes.

def joinIds(IdList):
  return "__".join(IdList)
joinIds_udf = udf(joinIds)

pnr_corr = pnr_corr.withColumn('joinedIds', joinIds_udf(pnr_corr.pnrCorrelations.correlationPnrSchedule.scheduleIds)

pnr_corr_skd = pnr_corr.join(skd, pnr_corr.joinedIds.contains(skd.id), how='inner')

This is a sample of the join that I have in mind:

dataframe A:
listColumn
["a","b","c"]
["a","b"]
["d","e"]

dataframe B:
valueColumn
a
b
d

output:
listColumn      valueColumn
["a","b","c"]   a
["a","b","c"]   b
["a","b"]       a
["a","b"]       b
["d","e"]       d

Answer 1

I don't know if there is an efficient way to do it, but this gives the correct output:

import pandas as pd
from itertools import chain

df1 = pd.Series([["a","b","c"],["a","b"],["d","e"]])
df2 = pd.Series(["a","b","d"])

result = [ [ [el2,list1] for el2 in df2.values if el2 in list1 ] 
                         for list1 in df1.values ]
result_flat = list(chain(*result))

result_df = pd.DataFrame(result_flat)

You get:

In [26]: result_df
Out[26]:
   0          1
0  a  [a, b, c]
1  b  [a, b, c]
2  a     [a, b]
3  b     [a, b]
4  d     [d, e]

Another approach is to use the new explode() method from pandas>=0.25 and merge like this:

import pandas as pd

df1 = pd.DataFrame({'col1': [["a","b","c"],["a","b"],["d","e"]]})
df2 = pd.DataFrame({'col2': ["a","b","d"]})

df1_flat = df1.col1.explode().reset_index()
df_merged = pd.merge(df1_flat,df2,left_on='col1',right_on='col2')

df_merged['col2'] = df1.loc[df_merged['index']].values
df_merged.drop('index',axis=1, inplace=True)

This gives the same result:

  col1       col2
0    a  [a, b, c]
1    a     [a, b]
2    b  [a, b, c]
3    b     [a, b]
4    d     [d, e]

Answer 2

How about:

df['col1'] = [df['col1'].values[i] + [df['col2'].values[i]] for i in range(len(df))]

Where 'col1' is the list of strings and 'col2' is the strings.

You can also drop 'col2' if you don't want it anymore with:

df = df.drop('col2',axis=1)