Data Exploration and Outliers Handling with Pandas - Week 3 - IAT 461
This hands-on guide explores fundamental data wrangling, cleaning, and analysis techniques in Python using popular libraries like Pandas, NumPy, Matplotlib, and Seaborn.
The tutorial is structured into several key steps:
1. Initial Data Profiling: Loading a student health dataset and inspecting null counts, datatypes, and initial summary statistics.
2. Identifying Inconsistencies: Spotting unit system discrepancies (mixed metric and imperial measurement rows) through data distribution checks, summary means, and group-by calculations.
3. Exploratory Data Visualization: Standardizing environment libraries, installing missing dependencies (`matplotlib`, `seaborn`), and plotting box plots to visually identify height and weight outliers.
4. Correlation Analysis: Evaluating relationships between height, weight, daily steps, and BMI through statistical correlation matrices and graphical heatmaps.
5. Data Standardization: Creating logic masks with Pandas and NumPy to convert imperial columns (inches, pounds) to standard metric values (centimeters, kilograms).
6. Outlier Detection and Handling: Writing a standard-deviation-based filter function to identify extreme anomalies and demonstratively walking through handling methods (e.g., dropping rows vs. mean/median imputation).
By the end of the video, you will understand how to inspect, clean, standardize, and impute data to produce a high-quality dataset ready for predictive modeling.
00:00 Importing Dataset and Troubleshooting
01:50 Exploring Data Columns and Missing Values
03:00 Analyzing Data Inconsistencies and Grouping by Unit System
06:53 Visualizing Outliers with Box Plots
09:02 Correlation Analysis and Heatmap Visualization
13:20 Data Cleaning: Standardizing Metric Systems
18:37 Outlier Detection using Standard Deviation
23:00 Handling Outliers (Removal vs. Mean Imputation)
38:59 Future Scope: Missing Value Prediction and Conclusion