Open In App

How to Manipulate Strings in Pandas?

Last Updated : 05 Sep, 2020
Improve
Improve
Like Article
Like
Save
Share
Report

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)


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


Like Article
Suggest improvement
Previous
Next
Share your thoughts in the comments

Similar Reads