I have a two different pandas Dataframe
df_1 with columns id(int), name(string), description(string)
and df_2 with columns id(int), name(string), description(string)
The names from df_1 and df_2 are only similar but not the same and I would like to connect both data frames with id of df_1.
I have created a new column for both dataframes called splitted_name with a list of words from name column.
Now I would like to check if at least one element from df_1.splitted_name is in df_2.splitted_name. How can I get this done in Pandas?
sample data:
df_1
name name_split
1 Alone in the jungle ['alone','in','the','jungle']
2 Born by the sea ['born','by','the','sea']
df_2
1 Goodbye my love ['goodbye','my','love']
2 Alone in the jungle remastered ['alone','in','the','jungle','remastered']
You should first join them to one Data frame and then try this. I have made my own example with these datasets:
df1 = pd.DataFrame(data=[['John Black'], ['Sara Smith'], ['Jane Jane']], columns=['name'])
df2 = pd.DataFrame(data=[['John Smith'], ['Sara Midname Smith'], ['Emma Sunshine']], columns=['name'])
df1['splitted_name'] = df1.name.str.split(' ')
df2['splitted_name'] = df2.name.str.split(' ')
Create data frame with all possible combinations:
df = []
for i in df1.values:
for j in df2.values:
df.append(i.tolist()+j.tolist())
df = pd.DataFrame(df)
df.columns = ['name1','splitted_name1', 'name2','splitted_name2']
And finally compare splitting names:
result = df.apply(lambda x: (pd.Index(pd.unique(x.splitted_name1)).get_indexer(x.splitted_name2) >= 0).any(), 1)
Output:
0 True
1 False
2 False
3 True
4 True
5 False
6 False
7 False
8 False
Name: result, dtype: bool
Also you can use it as a new column in the Data frame:
df['result'] = result
And then filter rows you need:
df = df[df.result]
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