Zerotopandas Course Project 70 Years Of F1
70 Years of Formula 1 - An Exploratory Data Analysis
Formula 1, the pinnacle of motorsports, is celebrating its 70th anniversary this year. From the British Grand Prix in 1950, this sport has not just enthralled racing fans around the world but also has been a platform for innovations and cutting edge technological developments. Being an F1 fan myself, I thought I would do my first data analysis project on the 70-year history of the sport! Thanks to Ergast Developer API and their well-kept database images for making this happen. Here, we will be using their data up to the 2020 Russian GP held on 27th September. This project is a part of the course Data Analysis with Python: Zero to Pandas hosted by Jovian. I will be using Numpy and Pandas libraries in Python to do some primary data exploration on the driver and constructor records and a few other aspects of F1. Matplotlib and Seaborn libraries are used for plotting and visualisations.
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
Install Conda by following these instructions. Add Conda binaries to your system
PATH, so you can use the
condacommand on your terminal.
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
- 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
- Enter the newly created directory using
cd directory-nameand start the 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.
We will first import the Python libraries required in this notebook.
# Importing python libraries from urllib.request import urlretrieve import os import pandas as pd import numpy as np