Writing Reusable Code using Functions in Python
Part 4 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:
- First Steps with Python and Jupyter
- A Quick Tour of Variables and Data Types
- Branching using Conditional Statements and Loops
- Writing Reusable Code Using Functions
- Reading from and Writing to Files
- Numerical Computing with Python and Numpy
- Analyzing Tabular Data using Pandas
- Data Visualization using Matplotlib & Seaborn
- Exploratory Data Analysis - A Case Study
This tutorial covers the following topics:
- Creating and using functions in Python
- Local variables, return values, and optional arguments
- Reusing functions and using Python library functions
- Exception handling using
- Documenting functions using docstrings
How to run the code
This tutorial is an executable Jupyter notebook hosted on Jovian. 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.
Creating and using functions
A function is a reusable set of instructions that takes one or more inputs, performs some operations, and often returns an output. Python contains many in-built functions like
len, etc., and provides the ability to define new ones.
today = "Saturday"
print("Today is", today)
Today is Saturday