Pandas Cleaning Data
Pandas Cleaning Data
Data cleaning in Pandas is the process of fixing or removing incorrect, incomplete, or inconsistent data so it can be properly analyzed. Real-world datasets are often messy, and Pandas provides powerful tools to clean them efficiently.
1. Detecting Missing Data
2. Handling Missing Values
Remove Missing Data
Fill Missing Data
3. Removing Duplicate Data
4. Fixing Data Types
5. Renaming Columns
6. Cleaning Text Data
7. Handling Outliers (Basic)
8. Replacing Values
9. Splitting & Combining Columns
Split Column
Combine Columns
10. Resetting & Setting Index
Real-World Example
Common Data Cleaning Checklist
✔ Remove duplicates
✔ Handle missing values
✔ Correct data types
✔ Standardize text
✔ Remove outliers
Conclusion
Data cleaning is a crucial step in data analysis. With Pandas, you can clean data quickly and systematically, ensuring accurate and reliable results.
