Pandas Library provides multiple methods that can be used to manipulate string according to the required output. But first, let’s create a Pandas dataframe.
Python3
import pandas as pd
data = [[ 1 , "ABC KUMAR" , "xYZ" ], [ 2 , "BCD" , "XXY" ],
[ 3 , "CDE KUMAR" , "ZXX" ], [ 3 , "DEF" , "xYZZ" ]]
cfile = pd.DataFrame(data, columns = [ "SN" , "FirstName" , "LastName" ])
cfile |
Output:
“Pandas” library provides a “.str()” method that can be used to create any data of the data frame into a string, After that any string operation defined in python documentation or in this article can be used on that data.
Below is the code that illustrates some examples
Python3
# find firstname starting with 'D' result = cfile.FirstName. str .startswith( 'D' )
print (result)
# find lasttname containing 'XX' result = cfile.LastName. str .contains( 'XX' )
print (result)
# split FirstName on the basis of ' ' result = cfile.FirstName. str .split()
print (result)
# find length of lasttname result = cfile.LastName. str . len ()
print (result)
# Capitalize the first Letter of LastName result = cfile.LastName. str .capitalize()
print (result)
# Capitalize all Letter of LastName result = cfile.LastName. str .upper()
print (result)
# Convert all Letter of LastName to lowercase result = cfile.LastName. str .lower()
print (result)
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Output:
0 False 1 False 2 False 3 True Name: FirstName, dtype: bool 0 False 1 True 2 True 3 False Name: LastName, dtype: bool 0 [ABC, KUMAR] 1 [BCD] 2 [CDE, KUMAR] 3 [DEF] Name: FirstName, dtype: object 0 3 1 3 2 3 3 4 Name: LastName, dtype: int64 0 Xyz 1 Xxy 2 Zxx 3 Xyzz Name: LastName, dtype: object 0 XYZ 1 XXY 2 ZXX 3 XYZZ Name: LastName, dtype: object 0 xyz 1 xxy 2 zxx 3 xyzz Name: LastName, dtype: object