Pandas Analyzing DataFrames
Pandas Analyzing DataFrames – Complete Guide
Pandas Analyzing DataFrames means:
Understanding the data
Finding patterns
Summarizing information
Detecting problems (missing values, outliers)
1. Setup (Required)
2. Create Sample DataFrame (Running Code)
3. Basic Data Inspection (Beginner)
View first rows
View last rows
Shape (rows, columns)
Column names
4. Data Information
Data types & non-null count
Statistical summary
- Mean, min, max, std, quartiles
5. Analyzing Individual Columns
Unique values
Value counts (very important)
Mean / Max / Min
6. Analyzing Multiple Columns
7. Filtering Data (Core Analysis Skill)
Simple condition
Multiple conditions
8. GroupBy Analysis (Most Important)
Average salary by department
Multiple aggregations
9. Detecting Missing Values
- Essential before any serious analysis
10. Handling Duplicates
11. Sorting Data
Sort by one column
Sort descending
12. Index-Based Analysis
Set column as index
Reset index
13. Apply Custom Logic (Intermediate)
14. Simple Visualization for Analysis
- Combine analysis + visualization
15. Real-World Analysis Example
Question: Which department pays highest on average?
16. Common Analysis Mistakes
- Ignoring missing values
- Not checking data types
- Forgetting to remove duplicates
- Jumping to conclusions without visualization
17. Interview Questions (Must Know)
Q1: How do you quickly understand a DataFrame?head(), info(), describe()
Q2: Best way to summarize data by category?groupby()
Q3: How to count category frequency?value_counts()
Q4: How to find correlations?df.corr()
