Pandas Data Analysis
Installation
Summary
Data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib.
- Covers DataFrame and Series creation, indexing, filtering, and type conversions for structured data handling
- Includes data cleaning techniques: missing value handling, deduplication, string operations, and date/time parsing
- Provides GroupBy aggregation, pivot tables, multi-level indexing, and window functions for exploratory analysis
- Integrates Matplotlib and Seaborn for statistical plotting, trend visualization, and correlation analysis
- Three hands-on projects cover customer analytics, time series analysis, and automated data quality reporting
SKILL.md
Pandas Data Analysis
Overview
Master data analysis with Pandas, the powerful Python library for data manipulation and analysis. Learn to clean, transform, analyze, and visualize data effectively.
Learning Objectives
- Load and manipulate data from various sources (CSV, Excel, SQL, APIs)
- Clean and transform messy datasets
- Perform exploratory data analysis (EDA)
- Aggregate and group data for insights
- Create compelling visualizations
- Optimize performance for large datasets
Core Topics
1. Pandas DataFrames & Series
- Creating DataFrames from various sources
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