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Python Installation

This page will guide you through installing Python, setting up development environments, running Python scripts, and exploring basic tools for data analysis.

Installing Python

  • Download Python
    Go to Python.org and download Python 3.13.7 (latest stable version).

  • Install Python

    • Run the installer.
    • Important: Check the box "Add Python to PATH" before clicking Install.

  • Verify Installation
    Open a terminal or command prompt and type:

python --version

You should see something like:

Python 3.13.7

Development Environments (IDEs)

There are several ways to write and run Python code. Here are some recommended options:

PyCharm

  1. IDE developed by JetBrains.
  2. Great for projects, especially if you have an .edu email (can get free license).
  3. To run a Python project in PyCharm:
    1. Open PyCharm and create a new project.
    2. Add a new Python file (.py) in the project.
    3. Write your code, e.g.: print("Hello world!")
    4. Right-click the file → Run → see output in the console.

VS Code

  • Lightweight editor, requires Python extension.
  • Steps to run Python in VS Code:
    1. Install VS Code.
    2. Install Python extension from Extensions Marketplace.
    3. Create a .py file.
    4. Run the file in the integrated terminal.

Anaconda

  • Distribution including Python, many scientific libraries, and AI/ML tools.
  • Ideal for data analysis and AI projects.
  • Comes with Jupyter Notebook for interactive code.

Google Colab

  • Online IDE for Python.
  • Great for data analysis without installing anything locally.
  • Visit Google Colab and start coding.

Running Python Scripts

  1. Save your file with .py extension.
  2. Open terminal/command prompt.
  3. Navigate to the folder containing your file.
  4. Run: python your_file.py
  5. You should see the output in the terminal.

Using Jupyter Notebook

Jupyter Notebook is excellent for data analysis.

  • Launch via Anaconda Navigator or command line:
jupyter notebook
  • Create a new Python notebook.
  • You can write Python code in cells and run them individually.
  • Example:
import pandas as pd

# Load Excel data
df = pd.read_excel("data.xlsx")
print(df.head())

Example: Simple Python Script

Sample code:

# hello.py
import numpy

example = numpy.zeros((3, 2))
print(example)

Installation and Running:

pip install numpy
pip3 install numpy
python hello.py

Fixing Jupyter Authentication and XSRF Issues (Short Version)

Common Symptoms:

  • Browser asks for password or token repeatedly.
  • Errors like 403: XSRF cookie does not match POST argument or _xsrf argument missing from POST.

Quick Fix Summary:

  • Use the correct token URL
    When Jupyter starts, copy the full link shown in the terminal (it includes ?token=...).

  • Clear cookies for localhost
    Remove cookies in your browser or open in Incognito mode.

  • Remove stored password
    Edit ~/.jupyter/jupyter_server_config.json and delete lines like:

"IdentityProvider": {
"hashed_password": "argon2:..."
}

Save and restart Jupyter.

  • Disable password/token locally (optional)
    Add this to ~/.jupyter/jupyter_notebook_config.py:
c.NotebookApp.token = ''
c.NotebookApp.password = ''
c.ServerApp.password = ''
c.ServerApp.token = ''
c.PasswordIdentityProvider.hashed_password = ''
c.ServerApp.disable_check_xsrf = True  # local only!
  • Restart Jupyter
jupyter notebook stop 8888
jupyter notebook
  • If still broken
    Reset config:
mv ~/.jupyter ~/.jupyter.backup
jupyter notebook --generate-config

✅ Now Jupyter should open directly at http://localhost:8888 without asking for a token or password.

References