2个数据框之间的字符串匹配

Question

Learning Python here, and any help on this is much appreciated. My problem scenario is, there are 2 dataframes A and B contains a column(Name and Flag) list of Names.

ExDF = pd.DataFrame({'Name' : ['Smith','John, Alex','Peter Lin','Carl Marx','Abhraham Moray','Calvin Klein'], 'Flag':['False','False','False','False','False','False']})

SnDF = pd.DataFrame({'Name' : ['Adam K ','John Smith','Peter Lin','Carl Josh','Abhraham Moray','Tim Klein'], 'Flag':['False','False','False','False','False','False']})

The initial value of Flag is False.

Point 1: I need to flip the names in both dataframe ie. Adam Smith to Smith Adam and save the flip names in another new column in the both dataframes. - This part is done.

Point 2: Then both the Original name and flip names of A dataframe should get check in B dataframe original names and flip names. If it found the the flag column in both the dataframe should get update by True.

I wrote the code but it checks one on one row to both dataframe like A[0] to B[0] , A[1] to B[1] , but i need to check A[0] record to all the records of B dataframe.

Pls help me on this!!

The code which tried is below:

import numpy as np

import pandas as pd

from sklearn.feature_extraction.text import CountVectorizer

ExDF_swap = ExDF["Swap"] = ExDF["Name"].apply(lambda x: " ".join(reversed(x.split()))) 
SnDF_swap = SnDF["Swap"] = SnDF["Name"].apply(lambda x: " ".join(reversed(x.split()))) 
ExDF_swap =  pd.DataFrame(ExDF_swap)
SnDF_swap =  pd.DataFrame(SnDF_swap)

vect = CountVectorizer()
X = vect.fit_transform(ExDF_swap.Name)
Y = vect.transform(SnDF_swap.Name)

res = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
pd.DataFrame(X.toarray(), columns=vect.get_feature_names())
pd.DataFrame(Y.toarray(), columns=vect.get_feature_names())

ExDF["Flag"] = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))
SnDF["Flag"] = np.ravel(np.any((X.dot(Y.T) > 1).todense(), axis=1))

Answer 1

You could try isin() - of pandas:

import pandas as pd

ExDF = pd.DataFrame({'Name' : ['Smith','John, Alex','Peter Lin','Carl Marx','Abhraham Moray','Calvin Klein'], 'Flag':['False','False','False','False','False','False']})
SnDF = pd.DataFrame({'Name' : ['Adam K ','John Smith','Peter Lin','Carl Josh','Abhraham Moray','Tim Klein'], 'Flag':['False','False','False','False','False','False']})

print(ExDF)
print(SnDF)

ExDF["Swap"] = ExDF["Name"].apply(lambda x: " ".join(reversed(x.split())))
SnDF["Swap"] = SnDF["Name"].apply(lambda x: " ".join(reversed(x.split())))

print(ExDF)
print(SnDF)

ExDF['Flag'] = ExDF.Name.isin(SnDF.Name)
SnDF['Flag'] = SnDF.Name.isin(ExDF.Name)

print(ExDF)
print(SnDF)