Pandas Analyzing DataFrames
Pandas Analyzing DataFrames
Analyzing DataFrames in Pandas means understanding, exploring, and extracting insights from data. Pandas provides many built-in functions to quickly examine structure, quality, and patterns in a dataset.
1. Inspecting the DataFrame
View Data
Understand Structure
2. Statistical Summary
3. Analyzing Individual Columns
4. Filtering & Conditional Analysis
5. Handling Missing Data
6. Sorting & Ranking
7. Grouping Data (GroupBy)
8. Correlation & Relationships
9. Detecting Outliers (Basic)
10. Real-World Example
Key Functions for Analysis
| Purpose | Function |
|---|---|
| View data | head(), tail() |
| Summary | describe() |
| Missing values | isnull() |
| Sorting | sort_values() |
| Grouping | groupby() |
| Counting | value_counts() |
Conclusion
Pandas makes DataFrame analysis fast, flexible, and powerful. By combining inspection, statistics, filtering, and grouping, you can uncover meaningful insights from data.
