Exploratory Data Analysis using Python - A Case Study

Analyzing responses from the Stack Overflow Annual Developer Survey 2020

Part 9 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

The following topics are covered in this tutorial:

• Selecting and downloading a dataset
• Data preparation and cleaning
• Exploratory analysis and visualization
• Asking and answering interesting questions
• Summarizing inferences and drawing conclusions

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.

Introduction

In this tutorial, we'll analyze the StackOverflow developer survey dataset. The dataset contains responses to an annual survey conducted by StackOverflow. You can find the raw data & official analysis here: https://insights.stackoverflow.com/survey.

There are several options for getting the dataset into Jupyter:

• Download the CSV manually and upload it via Jupyter's GUI
• Use the urlretrieve function from the urllib.request to download CSV files from a raw URL
• Use a helper library, e.g., opendatasets, which contains a collection of curated datasets and provides a helper function for direct download.

We'll use the opendatasets helper library to download the files.