Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Python – Reshape the data in a Pandas DataFrame
We can easily reshape the data by categorizing a specific column. Here, we will categorize the “Result”column i.e. Pass and Fail values in numbers form.
Import the required library −
import pandas as pd
Create a DataFrame with 2 columns −
dataFrame = pd.DataFrame(
{
"Student": ['Jack', 'Robin', 'Ted', 'Scarlett', 'Kat'],"Result": ['Pass', 'Fail', 'Fail', 'Pass', 'Pass']
}
)Reshape the data using the map() function and just set ‘Pass’ to 1 and ‘Fail’ to 0 −
dataFrame['Result'] = dataFrame['Result'].map({'Pass': 1,'Fail': 0, })
Example
Following is the code −
import pandas as pd
# Create DataFrame
dataFrame = pd.DataFrame(
{
"Student": ['Jack', 'Robin', 'Ted', 'Scarlett', 'Kat'],"Result": ['Pass', 'Fail', 'Fail', 'Pass', 'Pass']
}
)
print"DataFrame ...\n",dataFrame
# reshaping into numbers
dataFrame['Result'] = dataFrame['Result'].map({'Pass': 1,'Fail': 0, })
print"\nReshaped DataFrame ...\n",dataFrameOutput
This will produce the following output
DataFrame ... Result Student 0 Pass Jack 1 Fail Robin 2 Fail Ted 3 Pass Scarlett 4 Pass Kat Reshaped DataFrame ... Result Student 0 1 Jack 1 0 Robin 2 0 Ted 3 1 Scarlett 4 1 Kat
Updated on: 20-Sep-2021
564 Views
Advertisements