3 Simple Visuals in Python
(From Lukas Blazek on Unsplash)
Introduction
It's much easier to gain insights by looking at visuals, rather than looking at rows and columns of data. So, in this post, I'll share 3 visuals that you can make using the Seaborn library, which is provided by Python.
The Dataset
As a demonstration, I'll be using a dataset on all the pro tennis matches in 2022. Here's the link, if you want to follow along: https://github.com/JeffSackmann/tennis_atp/blob/master/atp_matches_2022.csv
(From Moises Alex on Unsplash)
Importing the Libraries and Dataset in Python
In the first cell of our Jupyter notebook, we're going to import 3 libraries: Pandas, Seaborn, and Matplotlib.
Pandas will allow us to load our tennis dataset, and Matplotlib is needed since Seaborn is built on top of it.
To load the dataset with Pandas, we say this:
By running this line of code, we've created a Pandas dataframe called "df". The .read_csv method takes in the file name of the dataset, and it'll be different for you depending on where you have saved it on your local machine.
Now, we are ready to create visuals from our dataframe, and it's quite simple!
Countplot
The first visual I'll make is a countplot. This is basically a simple bar chart that counts the number of unique values in a categorical variable.
We're gonna use one of the methods from Seaborn called .countplot. Here's what it looks like in Python:
The second line of code creates the countplot. The first argument inside the .countplot method is the dataframe, which was "df". In the second argument, you must provide what column you want to see be counted, which was "surface".
The third line of code basically sets a title to the graph called "Number of Tournaments by Surface", using the .title method provided by Matplotlib.
The first line of code changes the color theme of the graph. In this case, I used the "darkgrid" style. It's completely optional, and it's up to you whether you want to include it or not.
Here's what the countplot looks like:
It's a very simple visual, but just by glancing at it for 1 second, you can already see that hard courts were the most common surface in tennis tournaments in 2022.
2 More Visuals!
If you want to know how to create the last 2 types of visuals (a scatterplot and a boxplot), then make sure to check out the video below. I've provided timestamps in the description so you can skip around the video. Feel free to let me know if there are any questions that you have!