- Pandas - Install Python and Pandas
- Basic Data Structures in Pandas
- Loading and Saving Data using Pandas
- Exploring Data using pandas
- Correlation Analysis using pandas
- Handling Categorical Data and Unique Values using pandas
- Data Visualization using pandas
- Handling Missing Data in Python
- Strategies for Handling Missing Data
- Handling Missing Data - Example - Part 1
- Handling Missing Data - Example - Part 2
- Handling Missing Data - Example - Part 3 (Non-numeric Values)
- Handling Missing Data - Example - Part 4
- Data Transformation and Feature Engineering
- Converting Data Types in Python pandas
- Encoding Categorical Data in Python pandas
- Handling Date and Time Data in Python pandas
- Renaming Columns in Python pandas
- Filtering Rows in a DataFrame in Python
- Merging and Joining Datasets in Python pandas
- Sorting and Indexing Data for Efficient Analysis in Python
Renaming Columns in Python pandas
Changing column names in a pandas DataFrame is a common task, often done to make data more readable, comply with naming conventions, or prepare for data processing steps. Column names can be changed using the rename method or by directly assigning a new list of column names to the columns attribute of the DataFrame. The rename method is more flexible as it allows you to rename only specific columns without needing to specify all column names.
Here's an example showing how to rename columns in a DataFrame:
# Rename columns using the 'rename' method
loan_data_cleaned = loan_data_cleaned.rename(columns={
'CustomerName': 'ClientName',
'LoanAmount': 'Amount',
'LoanStartDate': 'StartDate',
'LoanEndDate': 'EndDate'
})
# Alternatively, to rename all columns directly:
# loan_data_cleaned.columns = ['NewCol1', 'NewCol2', 'NewCol3', ..., 'NewColN']
# Verify the changes
print(loan_data_cleaned.columns)
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