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Home / Business & Management / Big Data & Analytics / Data Visualisation with Python: Seaborn and Scatter Plots / Getting started with Seaborn

Getting started with Seaborn

Video on getting started with Seaborn
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0.1
(lively music) (mouse clicking) Similar to matplotlib, seaborn is a Python plotting library for creating data in charts. It builds on top of Matplotlib to allow you to create better looking charts and data comparisons using fewer lines of code. Here is a comparison of drawing bar charts using matplotlib versus seaborn. It integrates well with Pandas DataFrames and allows you to build complex plots of relationships. It can operate on a whole DataFrame and pull out the data itself. Let’s look at some of the differences between the two libraries. Matplotlib is procedural. Each step to generate a visualisation needs to be provided, whereas seaborn is more imperative and semantic.
47.6
Technically, for matplotlib if we say draw the plot with x values petal length and y values petal width for Setosa. Draw the plot for x values petal length and y values petal width for Versicolor. The colour of set Setosa points should be Green and the colour of set Versicolor points should be Blue. Then for seaborn, we effectively just say show me a comparison of petal width and length with different colours for each variety. Seaborn should be preferred to matplotlib if you want to make comparisons between datasets quickly. In this section of the course, you will explore numerical plots and learn to draw them.
89.7
Plots are the way for visualising the relationship between variables, and these variables can either be numerical or categorical in nature, such as group, class or division. In this section, we’ll look at numerical plots, which involve numerical variable. Compare and realise trends over time. Scatter plots and line plots are examples of numerical plots. You’ll learn to create scatter plots from an Iris dataset to depict the various species of the flower. You’ll also learn to create line plots using a dataset of the number of passengers taking flights in the years 1949 to 1960. However, before we jump straight to the topic, we would love to know what are you expecting to learn in this section?
137.8
Is there anything specific you’re excited to learn about? Don’t forget to share your thoughts in the comments after this video. So let’s get started.

Before we delve any deeper and get ready for an immersive experience of learning to build plots using Seaborn, watch this video to:

  • explore the various features of Seaborn and the importance of such Python libraries for data visualisation

  • understand how Seaborn is different from or, even in some cases, similar to Matplotlib

  • identify the various aspects of the library that you will learn in this step of the course.

Download your Jupyter Notebook

Before you progress any further this week, download your accompanying Jupyter Notebook. The Notebook contains only the code snippets that you can run to get an immersive and interactive experience as well as instant outputs of the codes alongside the explanations.

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This content is taken from
FutureLearn online course,

Data Visualisation with Python: Seaborn and Scatter Plots

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Make best use of this opportunity to familiarise yourself with using the Notebook.

Download: Introduction to Seaborn

You’ll find a set of interesting activities to check your knowledge later on in this course.

Data sets

Download the zipped file here containing the datasets as CSV files that you would require to conduct the demonstrations in this week. Be sure to extract the files and save them individually in the same folder as your Jupyter Notebooks.

Download: Data sets

All the data in the zip file is sourced from the Github link. [1]

References

  1. Github dataset [Internet]. Github; [date unknown]. Available from: https://github.com/mwaskom/seaborn-data

Want to keep learning?

This content is taken from FutureLearn online course
Data Visualisation with Python: Seaborn and Scatter Plots
View Course
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