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Python Coding in VS Code

You want to write Python code but aren’t sure which editor to use, or you’re looking for something lighter than PyCharm. Think of VS Code as a Swiss Army knife - it handles Python, R, JavaScript, and many other languages in one lightweight editor. This tutorial shows you how to set up Python in VS Code with smart features like code completion, interactive debugging, and even web apps.

Key Concepts

• Python Extension - VS Code extension by Microsoft that provides syntax highlighting, debugging, code execution, and Jupyter notebook support
• Pylance - Language server that enables fast IntelliSense, type checking, and auto-imports for Python
• Virtual Environment - Isolated Python environment for managing project-specific packages without affecting system Python
• Streamlit - Python library for creating interactive web apps with simple Python scripts

What You’ll Need

• Finished VS Code Basics
• Internet connection to download Python and packages
• 15-20 minutes

Step 1: Install Python

You need Python 3.11 or higher (the latest version from python.org is best) so the newest versions of packages like pandas and Streamlit install.

• Windows:
  • Go to python.org/downloads/windows and, under the latest Python 3 release, click Windows installer (64-bit) (not the “install manager”)
  • Run the installer
  • Check the box “Add python.exe to PATH” before clicking Install Now
  • Note the installation location (typically C:\Users\YourName\AppData\Local\Programs\Python\Python312 — the number matches your Python version)
• macOS:
  • Download from python.org and run the installer
  • After the python.org installer finishes, open Finder > Applications > Python 3.x and double-click Install Certificates.command (otherwise loading data from the internet in Step 7 fails with CERTIFICATE_VERIFY_FAILED)
  • Or use Homebrew: open Terminal and type brew install python
  • Homebrew path: /opt/homebrew/bin/python3 (Apple Silicon) or /usr/local/bin/python3 (Intel)
• Linux:
  • Python is usually pre-installed. Check version with python3 --version
  • If needed, install via package manager: sudo apt install python3 python3-pip python3-venv (Ubuntu/Debian)

Verify installation by opening a terminal and typing python3 --version or python --version.

Step 2: Install Python Extensions in VS Code

• Open VS Code
• Click the Extensions icon in the left sidebar (or click View > Extensions)
• Search for ms-python.python and click Install on the Python extension by Microsoft
• Pylance should install automatically with the Python extension. If not, search for ms-python.vscode-pylance and install it

The Python extension includes support for debugging, IntelliSense, code formatting, and Jupyter notebooks.

Step 3: Create Your Python Project

• Create a new folder on your computer (e.g., my-python-project)
• In VS Code, click File > Open Folder and select your new folder
• Click File > New File… in the top menu. A list appears at the top of the window — choose Python File
• Save the file as analysis.py by clicking File > Save

Step 4: Select Python Interpreter

VS Code needs to know which Python installation to use.

• Click View in the menu bar, then Command Palette
• Type Python: Select Interpreter and select it
• Choose the Python version you installed (e.g., Python 3.12.x or similar)
• The selected interpreter appears in the bottom-right corner of VS Code

If you don’t see your Python installation, click Enter interpreter path and navigate to where Python is installed.

Step 5: Create a Virtual Environment

Virtual environments keep your project dependencies isolated.

• Click View in the menu bar, then Command Palette
• Type Python: Create Environment and select it
• Choose Venv (built-in virtual environment)
• Select your Python interpreter from the list
• Wait for VS Code to create the environment (you’ll see a .venv folder appear)
• VS Code will automatically activate this environment for your project

You’ll see (.venv) in your terminal prompt when the environment is active.

Step 6: Install Required Packages

• Open a new terminal in VS Code by clicking View > Terminal
• The terminal should show (.venv) indicating your virtual environment is active
• Install packages by typing this command:

pip install pandas matplotlib streamlit

• Wait for installation to complete (may take 1-2 minutes)

If (.venv) doesn’t appear, the environment isn’t active. Click View > Command Palette, run Python: Select Interpreter, then choose the interpreter with ('.venv': venv) next to it.

Step 7: Write Your First Python Script

Type this code into analysis.py:

import pandas as pd
import matplotlib.pyplot as plt

# Load the iris dataset
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv"
iris = pd.read_csv(url)

# View the first few rows
print(iris.head())

# Generate summary statistics
print("\nSummary Statistics:")
print(iris.describe())

# Create a histogram
plt.figure(figsize=(8, 6))
plt.hist(iris['sepal_length'], bins=20, color='steelblue', edgecolor='white')
plt.xlabel('Sepal Length (cm)')
plt.ylabel('Frequency')
plt.title('Distribution of Sepal Length')
plt.show()

• Save the file by clicking File > Save

Step 8: Run Python Code Interactively

• With analysis.py open, click the ▶ Run Python File button in the top-right corner
• Or right-click in the editor and select Run Python > Run Python File in Terminal
• Watch the output appear in the terminal panel
• A histogram window will pop up showing your plot
• You can also select specific lines and press Shift+Enter to run just those lines in an interactive Python session

Step 9: Create a Simple Streamlit App

• Create a new file: click File > New File…, then choose Python File from the list at the top of the window
• Save it as app.py
• Type this code:

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

st.title("Interactive Histogram")

# Sidebar slider
bins = st.sidebar.slider(
    "Number of bins:",
    min_value=5,
    max_value=50,
    value=30
)

# Load data
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv"
iris = pd.read_csv(url)

# Create histogram
fig, ax = plt.subplots(figsize=(8, 6))
ax.hist(iris['sepal_length'], bins=bins, color='steelblue', edgecolor='white')
ax.set_xlabel('Sepal Length (cm)')
ax.set_ylabel('Frequency')
ax.set_title('Distribution of Sepal Length')

# Display in Streamlit
st.pyplot(fig)

• Save the file
• Open the terminal and run:

streamlit run app.py

• The first time you run Streamlit, it asks for an Email: in the terminal. Just press Enter to skip it
• The app opens in your browser (usually at http://localhost:8501)
• Move the slider in the sidebar and watch the histogram update instantly
• Press Ctrl+C in the terminal to stop the app

Step 10: Use Code Completion and IntelliSense

• In analysis.py, start typing iris. on a new line
• A dropdown appears with all available methods and attributes
• Type iris.gr and watch it suggest groupby()
• Hover your mouse over pd.read_csv in your existing code
• A popup shows function signature, parameters, and documentation
• Try typing import and watch VS Code suggest package names
• When you type a function call, IntelliSense shows parameter hints
• When you’re done, delete the unfinished lines you typed in this step (such as iris.gr and import ) and save the file, so the script runs cleanly in the next step

Step 11: Try Debugging

• In analysis.py, click to the left of line number 9 (the print(iris.head()) line) to set a breakpoint (red dot appears)
• Click Run in the menu bar, then Start Debugging
• Select Python File when prompted
• Code execution pauses at the breakpoint
• Use the debug toolbar to step through code, inspect variables, and see the call stack
• Click Run > Continue to resume execution

Next Steps

• Explore pandas for data manipulation and analysis
• Learn Jupyter notebooks in VS Code for interactive data science
• Try Flask or FastAPI for building web APIs
• Install Black for automatic code formatting
• Use pytest for writing and running tests

Troubleshooting

• “Python is not recognized” in terminal: Python isn’t in your system PATH. On Windows, reinstall Python and check “Add python.exe to PATH”. On Mac/Linux, use python3 instead of python. Restart VS Code after fixing.

• No interpreter found: Click the interpreter selector in the bottom-right corner of VS Code, or click View > Command Palette and run Python: Select Interpreter. If your Python installation doesn’t appear, select Enter interpreter path and browse to your Python executable.

• Virtual environment not activating: VS Code should auto-activate it when you open a terminal. If not, manually activate: Windows: .venv\Scripts\activate, Mac/Linux: source .venv/bin/activate. Verify by seeing (.venv) in your terminal prompt.

• IntelliSense not working: Make sure Pylance is installed and enabled. Check the bottom-right corner shows your selected interpreter. Wait 10-20 seconds after opening a file for Pylance to initialize. Restart VS Code if issues persist.

• pip install fails: Ensure your virtual environment is activated (look for (.venv) in terminal). On Linux, you might need to install python3-venv first. On Windows, check antivirus isn’t blocking pip.

• SSL: CERTIFICATE_VERIFY_FAILED on Mac: Open Finder > Applications > Python 3.x and double-click Install Certificates.command, then run the script again.

• Streamlit app won’t run: Ensure streamlit is installed in your virtual environment (pip show streamlit). Check the terminal for error messages. Make sure no other app is using port 8501.

Workflow Summary

VS Code provides a modern, lightweight environment for Python development with these advantages:

• Unified environment: Code Python, R, JavaScript, and more in one editor
• Powerful IntelliSense: Smart completions, type checking, and auto-imports via Pylance
• Integrated debugging: Set breakpoints, inspect variables, and step through code
• Jupyter support: Run notebooks directly in VS Code without separate applications
• Version control: Built-in Git integration for tracking changes
• Extensions ecosystem: Thousands of extensions for testing, formatting, linting, and more

Created by Steven Ge on December 7, 2025.