Data Analysis with Pandas and Python

This project contains comprehensive resources and code examples that cover various aspects of data analysis using Pandas, the popular Python library.

Repository Overview

This repository is structured into several folders, each focusing on a different topic in Pandas. These topics range from basic DataFrame manipulation to advanced techniques like multi-indexing, merging, and visualizations.

Folder Structure

1. Python Crash Course : A quick Python refresher before diving into Pandas.
2. Series : Operations on Pandas Series, a one-dimensional array for handling data.
3. DataFrames I - Introduction : Basics of working with DataFrames – selecting, indexing, and modifying data.
4. DataFrames II - Filtering Data : Intermediate DataFrame operations like filtering and sorting.
5. DataFrames III - Data Extraction : Advanced DataFrame tasks, including applying functions and reshaping data.
6. Working with Text Data : Processing and manipulating text data in Pandas.
7. MultiIndex : Working with hierarchical (multi-level) indices in Pandas.
8. The GroupBy Object : Grouping data and applying aggregate functions using groupby.
9. Merging, Joining, and Concatenating DataFrames : Combining multiple datasets with merge, join, and concat.
10. Working with Dates and Times in Datasets : Handling date and time data, including parsing and time-based indexing.
11. Input and Output in Pandas : Reading data from files (CSV, Excel) and saving outputs.
12. Visualizations : Creating plots and charts to visualize data.
13. Pandas Options and Settings : Configuring Pandas display settings like precision, max rows/columns, and more to control the output format.

Prerequisites

To follow along with the examples in this repository, you will need the following Python libraries installed:

• Python 3.x
• Pandas
• NumPy
• Matplotlib