Simple data visualisations in Python that you will find useful

Simple data visualisations in Python that you will find useful

Simple data visualisations in Python that you will find useful. In this post, we will look at 5 types of plots with Seaborn and Matplotlib and their example applications for a data science project.

Being able to use data visualisation effectively is an important skill for Data Scientists. Visualising data helps us digest information easily and extract insights that are otherwise hard to extract. In this post, we will look at 5 types of plots with Seaborn _and _Matplotlib and their example applications for a data science project.

Photo by Paweł Czerwiński on Unsplash

In September 2020, Seaborn had a major release: v0.11.0. We will use some of the new features and enhancements from this release in this post. Particularly, you will find that column names for the example dataset and functionality of distribution plots in sections 3 and 4 are different if you are on an earlier version. Therefore, make sure your Seaborn version is updated. You can find detailed information about the release from here.

0. Dataset 📦

Let’s import packages and update default settings for charts to save time from tweaking individual plots and to add a little bit of personal style to the charts:

## Import packages
import seaborn as sns
import matplotlib.pyplot as plt

## Update default settings
sns.set(style='whitegrid', context='talk', 
        palette=['#62C370', '#FFD166', '#EF476F'])

If you want to learn more about tweaking chart default settings, you may find this post useful. We will use Seaborn’s built-in dataset on penguins:

## Import dataset
df = sns.load_dataset('penguins').rename(columns={'sex': 'gender'})
df

data-visualization matplotlib data-science seaborn python

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