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Indian Premier League Data Analysis

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This notebook provides the data analysis of matches that have taken place in Indian Premier League (IPL) from 2008 to 2019. The dataset used in this analysis is taken from https://www.kaggle.com/nowke9/ipldata. Once downloaded, there are two different datasets. One having information about the matches and the results (matches.csv). The other one has ball-by-ball data for all seasons (deliveries.csv). For this project, I have analysed the data from matches.csv.

The analysis done in this project is from a historical point of view, giving readers an overview of what has happended in the IPL. Tools such as Pandas, Matplotlib and Seaborn along with Python have been used to give a visual as well as numeric representation of the data in front of us.

The learnings about these tools have been received through the course Data Analysis with Python: Zero to Pandas conducted by Jovian.ml. The course was offered at no cost and made my journey of learning really easy and interesting. The course was done in partnership 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

  1. 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

  1. 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.

!pip install jovian --upgrade --quiet
!pip install pandas --upgrade
Requirement already up-to-date: pandas in c:\users\s\anaconda3\envs\courseproject\lib\site-packages (1.1.2) Requirement already satisfied, skipping upgrade: numpy>=1.15.4 in c:\users\s\anaconda3\envs\courseproject\lib\site-packages (from pandas) (1.19.2) Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in c:\users\s\anaconda3\envs\courseproject\lib\site-packages (from pandas) (2.8.1) Requirement already satisfied, skipping upgrade: pytz>=2017.2 in c:\users\s\anaconda3\envs\courseproject\lib\site-packages (from pandas) (2020.1) Requirement already satisfied, skipping upgrade: six>=1.5 in c:\users\s\anaconda3\envs\courseproject\lib\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)
!pip install matplotlib seaborn --upgrade --quiet