Powerlifting

I will be working here with a public Kaggle dataset, containing a snapshot of the OpenPowerlifting database as of April 2019. This database is a "public-domain archive of powerlifting history. Powerlifting is a sport in which competitors compete to lift the most weight for their class in three separate barbell lifts: the Squat, Bench, and Deadlift", per the description provided with the dataset.

Ever since I started lifting for health, strength and fitness a couple years ago, I've always wanted to enter a powerlifting meet at some point and this would be a great way to get a sense of the competitive landscape. It would be interesting to see the distribution of strength (as defined by the sport of powerlifting), especially in my weight class in 2019 and maybe see how it's changed over time. I would also be curious to see how performance scales depending on factors such as weight, gender and even over time!

This is my course project for Data Analysis with Python: Zero to Pandas, generously presented by Jovian.ml in collaboration with freecodecamp.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

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

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade

3. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:

jovian clone notebook-owner/notebook-id

4. Enter the newly created directory using cd directory-name and start the Jupyter notebook.

jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

This is a dataset I was able to pull from Kaggle. I will be downloading it into this notebook directly with the opendatasets module.

Let's begin by downloading the data, and listing the files within the dataset.