1.1 Setting Up the Python Data Science Lab

Python Data Science Lab Setup

Before learning Python for Data Science and Automation, you need a reliable development environment. A properly configured environment allows you to write, execute, test, and manage Python programs and third-party libraries efficiently.

1. Why Do We Need a Python Environment?

A Python environment provides the tools required to write and run Python programs. In Data Science, a project normally requires additional libraries such as NumPy, pandas, Matplotlib, and Jupyter.

Different projects may require different versions of Python or different versions of packages. Environment management prevents these dependencies from interfering with one another.

2. What is Anaconda?

Anaconda is a Python distribution designed for data science, scientific computing, and related workflows.

It provides Python together with tools and packages commonly required for scientific and Data Science work.

Why Is Anaconda Popular for Data Science?

• Simplifies Python installation.
• Provides package and environment management.
• Works well with scientific computing libraries.
• Can be used with Jupyter Notebook.
• Helps maintain isolated project environments.

3. Anaconda vs Anaconda Navigator vs conda

These terms are related but they do not mean exactly the same thing.

4. Installing Anaconda

Download the appropriate Anaconda distribution for your operating system and follow the installation instructions provided by the installer.

General Installation Steps

1. Download the appropriate Anaconda installer.
2. Start the installer.
3. Accept the licence terms.
4. Select the installation location.
5. Complete the installation.
6. Open Anaconda Navigator or the Anaconda Prompt/terminal.
7. Verify that the environment is working.

5. Verify the Python Installation

After installation, verify that Python is available from the command line.

Check Python Version

python --version

On some systems, the Python launcher may be accessed using:

python3 --version

A successful command should display the installed Python version.

Check conda

conda --version

6. What is Jupyter Notebook?

Jupyter Notebook is an interactive environment that allows you to combine executable code, output, explanations, mathematical expressions, and visualisations in a notebook document.

It is widely used for experimentation, Data Science, education, analysis, visualisation, and demonstrations.

Why Use Jupyter Notebook?

• Execute code in small sections.
• Immediately view the output.
• Combine code and explanations.
• Display tables and visualisations.
• Experiment with datasets interactively.
• Document the reasoning behind an analysis.

7. Understanding a Jupyter Notebook

A Jupyter Notebook is organised into cells. Each cell can contain a particular type of content.

Example Code Cell

name = "Alex"

print("Welcome,", name)

Example Markdown Cell

# Python Data Science

This notebook demonstrates
basic Python operations.

8. Launching Jupyter Notebook

If Jupyter is installed in the active environment, it can generally be launched from a terminal using:

jupyter notebook

Depending on the installation, the command may open the Jupyter interface in a web browser.

Alternative

If using Anaconda Navigator, Jupyter applications can also be launched through its graphical interface.

9. Create Your First Jupyter Notebook

1. Launch Jupyter Notebook.
2. Navigate to your working folder.
3. Create a new Python notebook.
4. Rename the notebook.
5. Create a code cell.
6. Enter a Python statement.
7. Run the cell.

First Program

print("Hello, Data Science!")

The output should be:

Hello, Data Science!

10. What is pip?

pip is the package installer commonly used for installing Python packages from the Python Package Index and other supported package sources.

Check pip

pip --version

Install a Package

pip install pandas

Install Multiple Packages

pip install numpy pandas matplotlib

Upgrade a Package

pip install --upgrade pandas

Remove a Package

pip uninstall pandas

View Installed Packages

pip list

11. What is conda?

conda is a package and environment management system. It can create isolated environments and install packages into those environments.

Check conda

conda --version

List Environments

conda env list

List Packages

conda list

12. Why Use Virtual Environments?

A virtual environment creates an isolated space for a project's Python interpreter and packages.

Consider two projects:

• Project A requires one version of a package.
• Project B requires another version.

Installing everything globally can create dependency conflicts. Separate environments help keep project dependencies isolated.

13. Create a Data Science Environment with conda

Create an isolated environment specifically for this course.

conda create --name datascience python=3.12

The environment can then be activated.

conda activate datascience

Once activated, packages installed into the environment are associated with that environment.

Deactivate the Environment

conda deactivate

Remove an Environment

conda remove --name datascience --all

14. Install Essential Data Science Packages

After activating the environment, install the libraries required for the upcoming modules.

conda activate datascience

Packages can be installed with conda:

conda install numpy pandas matplotlib seaborn jupyter

Packages can also be installed using pip when appropriate:

pip install numpy pandas matplotlib seaborn jupyter

15. Verify the Data Science Environment

Create a small Python program to confirm that the important libraries can be imported.

import numpy
import pandas
import matplotlib
import seaborn

print("NumPy:", numpy.__version__)
print("pandas:", pandas.__version__)
print("Matplotlib:", matplotlib.__version__)
print("Seaborn:", seaborn.__version__)

If the program executes successfully, the libraries are available in the current Python environment.

16. Find Which Python Installation Is Running

When multiple Python installations exist, checking the executable location can help identify which interpreter is active.

Inside Python

import sys

print(sys.executable)

This displays the path to the Python interpreter being used by the current program or notebook.

17. pip vs conda

18. Alternative: Google Colab

Google Colab provides a hosted notebook environment that allows learners to execute Python code without setting up a complete local Python environment.

It is particularly useful when:

• You cannot install software on your computer.
• You want to experiment quickly.
• You want to share notebooks.
• You want a browser-based development environment.

19. First Data Science Lab Exercise

Create a notebook and execute the following program.

import pandas as pd

data = {
    "Name": ["Alex", "Jordan", "Taylor"],
    "Score": [82, 91, 76]
}

df = pd.DataFrame(data)

print(df)

This simple exercise confirms that Python can import pandas, create structured data, and display a DataFrame.

20. Common Setup Errors and Solutions

21. Python Environment Best Practices

• Create separate environments for major projects.
• Keep project dependencies documented.
• Avoid unnecessary global package installations.
• Check the active environment before installing packages.
• Keep Python and packages reasonably up to date.
• Avoid blindly copying installation commands from unknown websites.
• Use official documentation when troubleshooting package installation.
• Test the environment before beginning a major project.

22. Interview Questions

23. Examination Questions

Multiple Choice Questions

Short Answer Questions

1. Define a Python virtual environment.
2. State two advantages of using Anaconda for Data Science.
3. Differentiate between pip and conda.
4. What is the purpose of Jupyter Notebook?
5. Write the command used to create a conda environment.
6. Write the command used to activate a conda environment.

24. Practical Lab Task

25. Lab Completion Checklist

26. Python Environment Quick Cheatsheet