Let’s see the different methods to join two text columns into a single column.
Method #1: Using cat() function
We can also use different separators during join. e.g. -, _, ” ” etc.
import pandas as pd
df = pd.DataFrame({ 'Last' : [ 'Gaitonde' , 'Singh' , 'Mathur' ],
'First' : [ 'Ganesh' , 'Sartaj' , 'Anjali' ]})
print ( 'Before Join' )
print (df, '\n' )
print ( 'After join' )
df[ 'Name' ] = df[ 'First' ]. str .cat(df[ 'Last' ], sep = " " )
print (df)
|
Output :

Method #2: Using lambda function
This method generalizes to an arbitrary number of string columns by replacing df[[‘First’, ‘Last’]] with any column slice of your dataframe, e.g. df.iloc[:, 0:2].apply(lambda x: ‘ ‘.join(x), axis=1).
import pandas as pd
df = pd.DataFrame({ 'Last' : [ 'Gaitonde' , 'Singh' , 'Mathur' ],
'First' : [ 'Ganesh' , 'Sartaj' , 'Anjali' ]})
print ( 'Before Join' )
print (df, '\n' )
print ( 'After join' )
df[ 'Name' ] = df[[ 'First' , 'Last' ]]. apply ( lambda x: ' ' .join(x), axis = 1 )
print (df)
|
Output :

Method #3: Using + operator
We need to convert data frame elements into string before join. We can also use different separators during join, e.g. -, _, ‘ ‘ etc.
import pandas as pd
df = pd.DataFrame({ 'Last' : [ 'Gaitonde' , 'Singh' , 'Mathur' ],
'First' : [ 'Ganesh' , 'Sartaj' , 'Anjali' ]})
print ( 'Before Join' )
print (df, '\n' )
print ( 'After join' )
df[ 'Name' ] = df[ "First" ].astype( str ) + " " + df[ "Last" ]
print (df)
|
Output :

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Last Updated :
01 Jan, 2019
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