Python Pandas One-Hot Encoding for CSV Data: Transforming Categorical Features

This guide demonstrates how to use Python's Pandas library to one-hot encode specific columns in a CSV file and save the transformed data to a new file. This technique is crucial for preparing data for machine learning models that often require numerical features.

Steps

1. Import Pandas: Begin by importing the Pandas library.

   import pandas as pd
   

2. Load CSV: Read your original CSV file into a Pandas DataFrame.

   df = pd.read_csv('original.csv')
   

3. One-Hot Encoding: Apply one-hot encoding to the desired columns ('proto', 'service', 'state', 'attack_cat' in this example).

   df = pd.get_dummies(df, columns=['proto', 'service', 'state', 'attack_cat'])
   

4. Drop Columns: Remove unnecessary columns like 'id' and 'label'.

   df = df.drop(['id', 'label'], axis=1)
   

5. Save to CSV: Save the modified DataFrame to a new CSV file.

   df.to_csv('new.csv', index=False)
   

Code Example

import pandas as pd

# Load the original CSV file
# Replace 'original.csv' with your actual file path
df = pd.read_csv('original.csv')

# One-hot encode specific columns
df = pd.get_dummies(df, columns=['proto', 'service', 'state', 'attack_cat'])

# Drop unwanted columns
df = df.drop(['id', 'label'], axis=1)

# Save to a new CSV file
# Replace 'new.csv' with your desired output file name
df.to_csv('new.csv', index=False)

Important:

• Replace 'original.csv' and 'new.csv' with the actual paths to your files.
• Ensure that the specified columns ('proto', 'service', 'state', 'attack_cat', 'id', 'label') are present in your CSV file.

This code snippet will create a new CSV file ('new.csv') with one-hot encoded features. The original columns will be replaced with their corresponding binary representations, making your data suitable for machine learning models.