This repository contains complete Python code and examples covering Pandas, one of the most essential libraries for data analysis and manipulation in Python.
📌 Created by Prince Monga – Web Developer | Data Analyst | Python Enthusiast
Pandas is a powerful Python library that helps in reading, cleaning, analyzing, and visualizing structured data. It provides fast, flexible, and expressive data structures like Series and DataFrames that make data handling efficient and intuitive.
| 🧩 Topic | 🔍 Description |
|---|---|
| 1. Introduction to Pandas | Understand what Pandas is and how it simplifies data analysis. |
| 2. Installing Pandas | Learn how to install and import the Pandas library. |
| 3. Series and DataFrame | Understand Pandas’ core data structures for 1D and 2D data. |
| 4. Reading CSV/Excel Files | Load external data into Pandas using read_csv() and read_excel(). |
| 5. Data Selection (loc, iloc) | Access and slice data using label-based and integer-based indexing. |
| 6. Filtering and Conditions | Apply logical conditions to filter rows and columns. |
| 7. Handling Missing Values | Use functions like isnull(), dropna(), fillna() to clean data. |
| 8. Data Aggregation (GroupBy) | Summarize data using groupby() and aggregation methods. |
| 9. Merging & Joining | Combine multiple DataFrames using merge(), concat(), and join(). |
| 10. Pivot Tables | Create Excel-style pivot tables to reshape and summarize data. |
| 11. Sorting and Ranking | Organize your data using sort_values() and rank(). |
| 12. Time Series Handling | Work with date-time data and perform resampling operations. |
| 13. Exporting Data | Save DataFrames to CSV, Excel, or other formats. |
This project is built from scratch by following:
✅ It is designed to teach students and professionals how to analyze and transform data step-by-step using Pandas.
- Python 3.x
- pandas
pip install pandas