What is a virtual environment?

A virtual environment is an isolated Python setup for a specific project.

It keeps:

• Python version (sometimes)
• Installed libraries
• Tooling (Jupyter, linters, etc.)

separate from other projects.

Why it’s essential in data analytics

Data analytics projects often depend on:

• Specific versions of NumPy/Pandas/Matplotlib
• Jupyter
• Database drivers
• Visualization libraries

If you install everything globally, you will eventually face:

• Version conflicts
• “It worked yesterday” problems
• Broken notebooks after updates

Virtual environments prevent most of those issues.

Two popular choices

Option 1: venvvenv (built-in)

• Comes with Python
• Lightweight
• Uses pippip for packages

Option 2: condaconda environments

• Great for data science libraries
• Handles compiled packages easily
• Works with both conda installconda install and pip installpip install

Using venvvenv (recommended for pure pip projects)

Create a new environment

From your project folder:

python -m venv .venv

python -m venv .venv

Activate the environment

• macOS/Linux:

source .venv/bin/activate

source .venv/bin/activate

• Windows (PowerShell):

.\.venv\Scripts\Activate.ps1

.\.venv\Scripts\Activate.ps1

Install packages

pip install numpy pandas matplotlib seaborn jupyter

pip install numpy pandas matplotlib seaborn jupyter

Freeze requirements

This creates a reproducible spec:

pip freeze > requirements.txt

pip freeze > requirements.txt

Later someone can recreate the same installs:

pip install -r requirements.txt

pip install -r requirements.txt

Using conda environments (recommended for analytics stacks)

Create and activate

conda create -n analytics python=3.12

conda create -n analytics python=3.12

conda activate analytics

conda activate analytics

Install packages

conda install numpy pandas matplotlib seaborn jupyter

conda install numpy pandas matplotlib seaborn jupyter

Mixing conda + pip safely

Sometimes a package is not available in conda.

Recommended approach:

1. Install as much as possible with condaconda
2. Then install remaining packages with pippip

Example:

conda install numpy pandas
pip install yfinance

conda install numpy pandas
pip install yfinance

Environment naming conventions

Good names:

• analyticsanalytics
• titanic-edatitanic-eda
• vizviz

Avoid generic names like testtest or newenvnewenv.

Best practices for data analytics projects

• Create one environment per project
• Pin versions for important packages (especially for long projects)
• Keep a requirements.txtrequirements.txt (pip) or environment.ymlenvironment.yml (conda)
• Store notebooks inside a project folder

Example conda environment file (environment.ymlenvironment.yml)

This is a common way to share environment configuration:

name: analytics
channels:
  - conda-forge
dependencies:
  - python=3.12
  - numpy
  - pandas
  - matplotlib
  - seaborn
  - jupyter
  - pip
  - pip:
      - yfinance

name: analytics
channels:
  - conda-forge
dependencies:
  - python=3.12
  - numpy
  - pandas
  - matplotlib
  - seaborn
  - jupyter
  - pip
  - pip:
      - yfinance

Then create it with:

conda env create -f environment.yml

conda env create -f environment.yml

Next

Continue to: Installing Data Science Libraries (pip & conda) to learn the best ways to install and verify common analytics libraries.