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Remove Special Characters From Dataframe Python

Last Updated : Mar 11, 2024

Remove Special Characters From Dataframe Python

In this tutorial we will show you the solution of remove special characters from dataframe python, when working with data, there may be a need to modify the data in some way to organize that data.

You may have to delete some data or enter some additional information to organize the data. There may be a need to maintain a certain state of data.

For example, the data can’t contain any special characters in it or the data can’t contain any white spaces so let’s get into it.

Step By Step Guide On Remove Special Characters From Dataframe Python :-

The Pandas DataFrame is a structure that contains two-dimensional data and its corresponding labels.

DataFrames are widely used in data science, machine learning, scientific computing, and many other data-intensive fields.

You can use the replace() function to remove any special characters in a dataframe in a Python program.

import pandas as pd
data = pd.DataFrame(
        'EmpID1@': [ 'EMP001', 'EMP002', 'EMP003', 'EMP004', 'EMP005' ],
        'EmpName#': [ 'Mukul', 'Rohan', 'Mayank', 'Raj', 'Aakash' ],
        'EmpLocation$': [ 'Saharanpur', 'Meerut', 'Agra', 'Saharanpur', 'Meerut' ],
        'EmpPay^': [ 25000, 30000, 35000, 40000, 45000 ]
data.columns = data.columns.str.replace('[^a-zA-Z]', '')
data.EmpID = data.EmpID.str.replace('[^a-zA-Z0-9]', '')
  1. In the first line there is an import statement that imports the pandas module as pd.
  2. The pandas module will help you to create a dataframe from two-dimensional data.
  3. In the next line, there is a variable that will become a dataframe with the use of the DataFrame() constructor.
  4. In the next line, there is two-dimensional data that has column names and row values.
  5. The names of the lists are represented by the column names, and the values within the lists are the data for that particular column. All of the data is in string format with special characters.
  6. There are four columns and five rows in this data frame named data. The column names are “EmpID1@”, “EmpName#”, “EmpLocation$”, and “EmpPay^”.
  7. Each of these four columns contains five rows of data in them. But there is a problem. All the data has special characters in it.
  8. After the data frame, there is a variable called “data.columns” which points to all the column values in a data frame.
  9. Now, on the other side, there is a function called replace() that is applied to the “data.columns” variable. There is a regular expression passed in as the first argument and a blank space passed in as the second argument.
  10. The regular expression “[^a-zA-Z]” indicates that only alphabetic characters are allowed in this field. So this line will remove all special characters from the names of the columns in the data frame.
  11. In the next line, there is another variable called “data.EmpID” that represents that the changes that are about to be made are for values in the list or column named “EmpID” and not the column name itself.
  12. Now in this line, the regular expression is slightly different from the above regular expression. It says “[^a-zA-Z0-9]”, which means that alphabetic characters are allowed and numeric vales are allowed as well.
  13. In the last line, there is a print statement that will print the whole data frame as output without special characters in it.

Conclusion :-

So finally, in conclusion, we can say that with the help of this article, you can now remove all special characters from a data frame in a Python program.

You can use the method mentioned above to remove the special characters. This is the simplest way you can remove special characters from a dataframe.

I hope this tutorial on remove special characters from dataframe python helps you and the steps and method mentioned above are easy to follow and implement.

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Ashish is a dynamic and motivated individual with a passion of programming and an experienced programmer having 3+ years of experience in various languages like Java, Python, HTML, CSS, JavaScript, jQuery and various frameworks like Bootstrap

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