You can rename columns in Pandas with any of the following methods. I highly recommend you This book to learn Python. You will see 2 methods to rename columns.
Step 1: Install Pandas Library
Install the Pandas library using this code, if it is not installed.
pip install pandas
Method 1
In the first method, I will use the df.columns method to rename the columns of Pandas DataFrame.
# Import the Pandas library as pd import pandas as pd # Initialize a NumPy Array a = np.array([[95, 90, 75], [19, 17, 13], [75, 52, 89]]) # Create DataFrame df = pd.DataFrame(a, columns=['A', 'B', 'C']) # Display the Original DataFrame print(df) # Rename Columns df.columns = ['new_A', 'new_B', 'new_C'] # Display the DataFrame print(df)
Output:
A B C 0 95 90 75 1 19 17 13 2 75 52 89 new_A new_B new_C 0 95 90 75 1 19 17 13 2 75 52 89
Method 2
In this method, I will use the df.rename() method to change the name of columns.
# Import the Pandas library as pd
import pandas as pd
# Initialize a NumPy Array
a = np.array([[95, 90, 75], [19, 17, 13], [75, 52, 89]])
# Create DataFrame
df = pd.DataFrame(a, columns=['A', 'B', 'C'])
# Display the Original DataFrame
print(df)
# Rename Two Columns
df.rename(columns= {'A': 'new_A', 'B': 'new_B'} , inplace=True)
# Display the DataFrame
print(df)Output:
A B C 0 95 90 75 1 19 17 13 2 75 52 89 new_A new_B C 0 95 90 75 1 19 17 13 2 75 52 89
Example: Rename CSV Data in Pandas
# Import the Pandas library as pd
import pandas as pd
# Read the CSV file as Pandas DataFrame
df = pd.read_csv("house_price.csv")
# Display the Original DataFrame
print(df)Area Rooms House_Age Price 0 5000 5 5 75000 1 4000 4 5 65000 2 3000 3 1 60000 3 2000 3 1 58000 4 1500 2 1 50000 5 7000 5 5 90000 6 6000 5 3 85000 7 6500 4 7 80000 8 8000 5 15 95000
Now, I want to rename two columns.
# Rename Two Columns
df.rename(columns= {'Rooms': 'No. of Rooms', 'House_Age': 'Duration'} , inplace=True)
# Display the DataFrame
print(df)Area No. of Rooms Duration Price 0 5000 5 5 75000 1 4000 4 5 65000 2 3000 3 1 60000 3 2000 3 1 58000 4 1500 2 1 50000 5 7000 5 5 90000 6 6000 5 3 85000 7 6500 4 7 80000 8 8000 5 15 95000
