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First Steps with Python and Jupyter


Part 1 of "Data Analysis with Python: Zero to Pandas"

This tutorial series is a beginner-friendly introduction to programming and data analysis using the Python programming language. These tutorials take a practical and coding-focused approach. The best way to learn the material is to execute the code and experiment with it yourself. Check out the full series here:

  1. First Steps with Python and Jupyter
  2. A Quick Tour of Variables and Data Types
  3. Branching using Conditional Statements and Loops
  4. Writing Reusable Code Using Functions
  5. Reading from and Writing to Files
  6. Numerical Computing with Python and Numpy
  7. Analyzing Tabular Data using Pandas
  8. Data Visualization using Matplotlib & Seaborn
  9. Exploratory Data Analysis - A Case Study

This tutorial covers the following topics:

  • Performing arithmetic operations using Python
  • Solving multi-step problems using variables
  • Evaluating conditions using Python
  • Combining conditions with logical operators
  • Adding text styles using Markdown

How to run the code

This tutorial is an executable Jupyter notebook hosted on Jovian (don't worry if these terms seem unfamiliar; we'll learn more about them soon). You can run this tutorial and experiment with the code examples in a couple of ways: using free online resources (recommended) or on your computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing the code is to click the Run button at the top of this page and select Run on Binder. You can also select "Run on Colab" or "Run on Kaggle", but you'll need to create an account on Google Colab or Kaggle to use these platforms.

Option 2: Running on your computer locally

To run the code on your computer locally, you'll need to set up Python, download the notebook and install the required libraries. We recommend using the Conda distribution of Python. Click the Run button at the top of this page, select the Run Locally option, and follow the instructions.

Jupyter Notebooks: This tutorial is a Jupyter notebook - a document made of cells. Each cell can contain code written in Python or explanations in plain English. You can execute code cells and view the results, e.g., numbers, messages, graphs, tables, files, etc. instantly within the notebook. Jupyter is a powerful platform for experimentation and analysis. Don't be afraid to mess around with the code & break things - you'll learn a lot by encountering and fixing errors. You can use the "Kernel > Restart & Clear Output" menu option to clear all outputs and start again from the top.

Performing Arithmetic Operations using Python

Let's begin by using Python as a calculator. You can write and execute Python using a code cell within Jupyter.

Working with cells: To create a new cell within Jupyter, you can select "Insert > Insert Cell Below" from the menu bar or just press the "+" button on the toolbar. You can also use the keyboard shortcut Esc+B to create a new cell. Once a cell is created, click on it to select it. You can then change the cell type to code or markdown (text) using the "Cell > Cell Type" menu option. You can also use the keyboard shortcuts Esc+Y and Esc+M. Double-click a cell to edit the content within the cell. To apply your changes and run a cell, use the "Cell > Run Cells" menu option or click the "Run" button on the toolbar or just use the keyboard shortcut Shift+Enter. You can see a full list of keyboard shortcuts using the "Help > Keyboard Shortcuts" menu option.

Run the code cells below to perform calculations and view their result. Try changing the numbers and run the modified cells again to see updated results. Can you guess what the //, %, and ** operators are used for?

2 + 3 + 9