Cleaning text data using str.replace() in Pandas

Pandas: Data Cleaning and Preprocessing Exercise-8 with Solution

Write a Pandas program that handles text data with str.replace().

This exercise shows how to clean text data by replacing specific substrings in a column using str.replace().

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with messy text
df = pd.DataFrame({
    'Product': ['$50-Discount', '$100-Off', '$200-Rebate']
})

# Clean the text by removing special characters like '$' and '-'
df['Product_Cleaned'] = df['Product'].str.replace('[$-]', '', regex=True)

# Output the result
print(df)

Output:

        Product Product_Cleaned
0  $50-Discount      50Discount
1      $100-Off          100Off
2   $200-Rebate       200Rebate

Explanation:

• Created a DataFrame with text data that contains special characters.
• Used str.replace() with a regular expression to remove characters like $ and - from the 'Product' column.
• Added a new column 'Product_Cleaned' with the cleaned text.

Python-Pandas Code Editor:

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