Data Analysis With Python

A practical, beginner-friendly, and hands-on course project for learning Data Analysis with Python using Pandas, NumPy, SQL Server connectivity, and modern Python project setup with uv.

This repository contains Jupyter notebooks covering the most important Python and Pandas concepts required for real-world data analysis.

📺 YouTube Playlist

All video explanations are available in this playlist:

Watch Full Course on YouTube

📚 Topics Covered

This project includes practical Jupyter notebooks on the following topics.

1. Pandas Basics

Introduction to Series in Pandas
Create DataFrame using Python built-in data structures
Inspect a dataset using Pandas
Data types in Pandas
Convert data types in Pandas
Index in Pandas DataFrame

2. Reading Data

Reading CSV files in Pandas
Reading Excel files in Pandas
Connect and read SQL Server data using Pandas

3. Selecting, Filtering, and Sorting Data

How to efficiently subset a Pandas DataFrame
Filter a Pandas DataFrame using a single condition
Filter a Pandas DataFrame using multiple conditions
Filter a Pandas DataFrame using the loc method
Efficient DataFrame filtering
Sort a Pandas DataFrame
Axis in Pandas DataFrame

4. Creating and Transforming Variables

Create new variables in Pandas DataFrame
Use the assign() method effectively in Pandas
Conditional variable creation using np.where() and np.select()
Map function in Pandas
Apply function in Pandas
ApplyMap function in Pandas

5. Working With Text, Dates, and Numeric Operations

String methods in Pandas
Arithmetic functions in Pandas
Date functions in Pandas
Aggregate functions in Pandas

6. Grouping and Summarizing Data

Groupby function in Pandas
Pivot table in Pandas
Crosstab in Pandas

7. Reshaping and Combining Data

Transform data from wide to long format using Pandas melt()
Combine multiple DataFrames using Pandas concat()
Joining DataFrames using Pandas merge()

8. Data Cleaning and Feature Engineering

Binning in Pandas
Fix duplicate rows in Pandas

🧰 Tools and Technologies Used

This project uses the following tools and technologies:

Python
Pandas
NumPy
Jupyter Notebook
SQL Server
pyodbc
SQLAlchemy
uv Python package manager
VS Code / DataSpell / JupyterLab

🚀 Project Setup Using uv

This project uses uv as the Python package manager.

uv is a modern and fast Python package and environment manager. It helps you create virtual environments, install dependencies, and run Python commands in a clean and reproducible way.

Using uv makes the project easier to clone, set up, and run on another machine.

✅ What To Do After Cloning This Project

Follow these steps after cloning the repository.

1. Clone the Repository

git clone <your-repository-url>

Move into the project folder:

cd <your-project-folder>

2. Install uv

If uv is not already installed, install it first.

For Windows PowerShell:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Check whether uv is installed:

uv --version

3. Restore the Project Environment

If the project contains a pyproject.toml file and uv.lock file, run:

uv sync

This command will:

Create a .venv virtual environment if it does not already exist
Install all required project dependencies
Use the locked dependency versions from uv.lock
Make the project environment consistent across machines

4. Activate the Virtual Environment

On Windows PowerShell:

.venv\Scripts\Activate.ps1

On macOS/Linux:

source .venv/bin/activate

After activation, you should see something like this in the terminal:

(.venv)

5. Add Jupyter Support

If Jupyter and ipykernel are not already installed, run:

uv add --dev jupyter ipykernel

Then register the virtual environment as a Jupyter kernel:

uv run python -m ipykernel install --user --name=data-analysis-python --display-name "Python (.venv)"

When you open a notebook, select this kernel:

Python (.venv)

📦 Common uv Commands

Install all dependencies

uv sync

Add a package

uv add pandas

Add a development package

uv add --dev jupyter

Run Python

uv run python

Run a Python script

uv run python script.py

Check installed packages

uv pip list

Add Excel file support

uv add openpyxl xlrd

Add SQL Server support

uv add pyodbc sqlalchemy

🧠 Who This Course Is For

This course is useful for:

Beginners learning Python for data analysis
Data analysts moving from Excel or SQL to Python
Business analysts who want to learn Pandas
Students learning practical data analysis
Professionals preparing for analytics interviews
Anyone building a strong foundation in Python-based analytics

🎯 Learning Outcomes

After completing this course, you will be able to:

Create and manipulate Pandas Series and DataFrames
Read data from CSV, Excel, and SQL Server
Inspect and understand datasets
Understand and convert data types
Work with DataFrame indexes
Filter, subset, and sort data
Create new variables using multiple techniques
Use np.where() and np.select() for conditional logic
Use map(), apply(), and applymap()
Work with string methods in Pandas
Work with date functions in Pandas
Perform arithmetic and aggregate operations
Summarize data using groupby, pivot tables, and crosstab
Reshape data from wide to long format
Combine datasets using concat
Join datasets using merge
Create bins and categories
Identify and fix duplicate rows
Use uv to manage Python environments and dependencies

▶️ Recommended Learning Path

Follow the notebooks in sequence.

Start with Pandas Series and DataFrame basics
Learn how to read data from CSV, Excel, and SQL Server
Practice inspecting datasets
Understand and convert data types
Learn indexing, filtering, sorting, and subsetting
Create new variables and apply transformations
Work with string, date, arithmetic, and aggregate functions
Summarize data using groupby, pivot table, and crosstab
Reshape data using melt
Combine and join multiple datasets
Learn data cleaning topics like binning and duplicate handling

📝 Working With Notebooks

To open Jupyter Notebook:

uv run jupyter notebook

To open JupyterLab:

uv run jupyter lab

You can also open the project in VS Code or DataSpell and select the Python (.venv) kernel.

🙌 About This Project

This project is part of the Analytics School learning series.

The goal of this project is to make Python, Pandas, SQL, and data analysis concepts easy to understand through practical examples and hands-on notebooks.

⭐ Support

If you find this project helpful:

Watch the YouTube playlist
Practice each notebook
Star the repository
Share it with other learners

📄 License

This project is for educational purposes. You may use and modify the notebooks for learning and practice.

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
.idea		.idea
Assets		Assets
Datasets		Datasets
Notebooks		Notebooks
.gitignore		.gitignore
.python-version		.python-version
LICENSE		LICENSE
README.md		README.md
main.py		main.py
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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