str.cat()

Pandas

Concatenate strings with different options .
Returns str, Series or Index

Options

Without using any separator sep = None ( default )
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex']}
df = pd.DataFrame(data=my_dict)
print(df.NAME.str.cat())
Output ( without any separator all are joined )
RaviRajuAlex
Let us try with separator
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex']}
df = pd.DataFrame(data=my_dict)
print(df.NAME.str.cat(sep=' '))
Output
Ravi Raju Alex
We can use , as separator.
print(df.NAME.str.cat(sep=','))
Output
Ravi,Raju,Alex
Using domain name and userid
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],
         'DOMAIN':['example.com','example.net','example.org'] }
df = pd.DataFrame(data=my_dict)
print(df.NAME.str.cat(df['DOMAIN'],sep='@'))
Output
0    Ravi@example.com
1    Raju@example.net
2    Alex@example.org

Handling missing data

By using option na_rep='#' we will say how to handle missing data.
import pandas as pd 
import numpy as np
my_dict={'NAME':['Ravi','Raju','Alex',np.nan],
   'DOMAIN':['example.com','example.net','example.org','example.co']}
df = pd.DataFrame(data=my_dict)
print(df.NAME.str.cat(df['DOMAIN'],sep='@',na_rep='#'))
Output
0    Ravi@example.com
1    Raju@example.net
2    Alex@example.org
3        #@example.co
Pandas contains() Converting char case slice() split()


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