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Welcome toPandas Masterclass — your complete hands-on guide to mastering data manipulation and analysis using the powerfulPandas library in Python.

This repository features9 comprehensive Jupyter Notebook modules designed to take you from understanding basic data structures to executing advanced data wrangling projects. Each notebook is clean, well-commented, and includes descriptive markdown explanations for clarity and practical understanding.

Every project folder includes attached datasets (anime.csv,countries.csv) forrealistic, hands-on learning.

This masterclass is structured for all kinds of learners:

• For Beginners (🧑‍💻): A guided, step-by-step journey starting from the fundamentals (Series, DataFrame).
• For Revision (🔁): Perfect for refreshing concepts before real-world applications or interviews.
• For Interview Prep (🎯): Focuses on must-know topics like GroupBy, Merging, Pivot Tables, and Capstone projects.
• For Building Projects (🚀): Includes two full projects using authentic datasets.

Follow the modules in order to build your Pandas expertise — from basics to complete analysis.

Learn about creation, indexing, slicing, and vectorized operations.
Focus: The 1D structure of Pandas.

Work with 2D tabular data — selecting, filtering, and modifying using.loc and.iloc.
Focus: The 2D foundation of Pandas.

Detect and handle missing values using.isna(),.dropna(), and.fillna().
Focus: Data cleaning and NaN handling.

Combine multiple datasets usingpd.merge(),pd.concat(), anddf.join().
Focus: Dataset integration and relational joins.

Apply the Split-Apply-Combine methodology for data summarization.
Focus: Grouping, aggregation, and multi-level analysis.

Create insightful summary tables withpd.pivot_table() andpd.crosstab().
Focus: Advanced reshaping and reporting.

Perform element-wise arithmetic, transformations with.apply() andlambda, and general data profiling.
Focus: Data transformation and inspection.

Real-world project to clean and extract useful insights from anime data.
Focus: Text parsing, string cleaning, and feature engineering.

Analyze global data with filtering, sorting, and complex querying.
Focus: End-to-end analytical workflow and storytelling with data.

You’ll needPython 3.x and the core data analysis libraries.

pip install pandas numpy matplotlib seaborn jupyter python-dateutil

git clone https://github.com/your-username/Pandas-Masterclass.gitcd Pandas-Masterclassjupyter notebook

Then start fromModule 1️⃣ - Series and progress sequentially.

This repository isactively maintained and will continue to evolve.

• 🆕 More real-world capstone projects
• 📈 Deep dives into time series, multi-indexing, and performance tuning
• 🧪 Dedicated interview challenge notebooks

1. Fork the repository
2. Create a branch —git checkout -b feature/new-module
3. Commit your changes —git commit -m 'feat: add new topic module'
4. Push to your branch —git push origin feature/new-module
5. Open a Pull Request 🎉

Pandas-Masterclass/│├── Module1_Series/├── Module2_DataFrame/├── Module3_Missing_Data/├── Module4_Merging_Joining_Concatenation/├── Module5_GroupBy_Aggregation/├── Module6_Pivot_Table/├── Module7_Operations/│├── Module8_Feature_Extraction_Anime_Project/│   ├── Anime_Feature_Extraction.ipynb│   └── data/ (anime.csv)│└── Module9_Data_Capstone_Countries_Project/    ├── Countries_Data_Analysis.ipynb    └── data/ (countries.csv)

"Every great analysis starts with clean data. Master Pandas, master data science."

Keep exploring, experimenting, and analyzing — welcome to the world ofdata mastery! 🌍