What is data cleaning in Python?

1. 1Import pandas library to handle datasets
2. 2Use dropna() function to remove rows with missing values
3. 3Apply fillna() function to replace missing values with mean or median
4. 4Utilize data normalization techniques to scale values
5. 5Validate data using info() and describe() functions

Data scientists and analysts benefit from data cleaning to ensure reliable insights and models

💡 Data cleaning is crucial for reliable data analysis and modeling

🚮 Clean your data with Python!