Learn Financial Modeling with Python Step-by-Step (2025 Edition)
Introduction to Financial Modeling with Python
Transitioning from Excel to Python can feel daunting, especially for CA students, finance analysts, and MBA aspirants with minimal coding experience. But in 2025, learning financial modeling with Python is not only practical—it’s essential for staying ahead in the finance world. This guide walks you through everything: from installing tools to building your first model, even if you’re a complete beginner.
Why CA Students Should Switch to Python for Modeling
Python offers several advantages over Excel:
Speed: Automate repetitive tasks that would take hours in Excel.
Reusability: Once written, your Python scripts can be reused across projects.
Scalability: Python handles large datasets more efficiently than Excel.
Integration: Python connects seamlessly with APIs, databases, and Excel itself.
If you’re comfortable with Excel, you’ll find Python’s logical structure and libraries like NumPy and Pandas intuitive once you get started.
Set Up Python on Windows (Zero Admin Needed)
Before you dive into modeling, ensure Python is correctly installed:
Open Command Prompt and type
python --version. If it returns a version number, Python is installed.Check
pipby typingpip --version. Pip is Python’s package manager—essential for installing libraries.Try
pip install numpyto ensure you have installation rights.
Choose the Right IDE: Jupyter vs. VS Code Explained
Jupyter Notebook is ideal for interactive, cell-based coding—perfect for beginners and data visualization. It runs in a browser and lets you test code line by line.
Visual Studio Code (VS Code) is a more advanced editor suitable for bigger projects. It supports Python via extensions and has better debugging tools.
Install and Launch Jupyter Notebook
Open Command Prompt and run:
Once installed, type:
Your browser will open a new tab showing the notebook interface. You’re ready to start coding!
Install VS Code (User Setup for Non-Tech Users)
Go to Visual Studio Code and download the User Installer (64-bit).
Why User Installer?
No admin rights needed
Easy updates
Safe for single-user machines
Run the .exe file and check all setup options like “Add to PATH” and “Open with Code” for smoother usage.
Configure Python in VS Code
Create a folder named
VSCode Config.Open VS Code → File → Open Folder → Select your folder.
Create a file
first_code.pywith this code:
print(“Welcome to Learning Python with Help of AI”)
ask_name = input(“What is your name?”)
print(f”Hi {ask_name}, Welcome to the Course..!”)
Press Ctrl+S to save. Or enable Auto Save under the File menu.
To test Jupyter support, create
new_code.ipynb, paste the same code in a cell, and run it.
Install Key Extensions for Python Coding
Search for these extensions in VS Code (Ctrl+Shift+X):
Python (by Microsoft)
Jupyter (for
.ipynbfiles)Pylint (for code quality)
Excel Viewer (to view Excel files directly)
IntelliCode (for smart code suggestions)
After installing, open the command palette (Ctrl+Shift+P), choose Python: Select Interpreter, and pick the correct version.
Build a Simple Financial Model in Python
Create a new .py file and start small. For example, forecast revenue using assumptions:
growth_rate = 0.1
base_revenue = 1000000
years = 5
revenues = [base_revenue * ((1 + growth_rate) ** year) for year in range(years)]
print(“Projected Revenues:”, revenues)
You can later add EBITDA, depreciation, and net income calculations step-by-step.
Conclusion
Python isn’t just a programming language — it’s a career booster for finance professionals.
You don’t need a CS degree to start with Python. Just install the right tools, follow these steps, and begin building real financial models.
Make 2025 the year you go beyond Excel. With Python, your financial modeling skills will be faster, smarter, and future-ready.
Start small. Stay curious. Keep building. You don’t need to be a coder to get started — just a problem-solver with a financial mindset.