Let’s Learn Exploratory Data Analysis Practically

Let’s Learn Exploratory Data Analysis Practically

Using the ‘English Premier League Results’ dataset | A beginner friendly step-by-step approach

Using the ‘English Premier League Results’ dataset | A beginner friendly step-by-step approach

“That is beyond special” — Peter Drury | Image on Unsplash by Nathan Rogers

Season 2020–21 of English Premier League — Arguably the world’s most entertaining football league, has come to an end. Congratulations to Manchester City for winning the league. It has been a different season without fans in the stadium, but it has given us some nail biting finishes, wild and breathtaking matches, memorable goals and captivating moments. Before we get our studs and shin pads ready for next season, why not check some interesting trends in the 29 seasons of the beautiful game played so far and learn step-by-step approach to performing Exploratory Data Analysis along the way? Let’s dig in.

The dataset we’re using is from kaggle, containing information about more than 10,000 Premier League matches played. The dataset can be found here.

We will use python libraries NumPy,Pandas,Matplotlib and Seaborn in this project. If you read through, you will be in a position to perform EDA on any dataset of your choice!

Downloading the Dataset

Let’s start by downloading the dataset from Kaggle. Here we use the opendatasets library made for python to download the same. By passing the URL of the Kaggle page for the dataset to opendatasets.download(), we will download the dataset.

The dataset has been downloaded and extracted.

Data Preparation and Cleaning

In any data analysis project, while working with real world raw data, it is very necessary to ready the data for our analysis. There could be wrong entries, missing values that need to be dealt with. Along with that, we might want to add new columns to the dataset, which are useful for our analysis or we might want to merge a few datasets together, this should be done as a preliminary step.

In our case, let’s start by converting the dataset into a Pandas dataframePandas is a python library which gives us handy functions for data cleaning, merging, operations etc. It creates an object called as DataFrame, which is basically data represented in tabular form. We can read different types of files eg. CSVJSONXLSX etc. and create a data frame using the same.

premier-league exploratory-data-analysis data-analysis

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