Matplotlib is one of the most popular visualization libraries used by data analysts and data scientists working in Python, but can often be intimidating to use. This course serves to make working with Matplotlib easy and simple.
This course will focus on making Matplotlib accessible and easily understandable to a Data Scientist or Business Analyst who needs to quickly and visually come to grips with relationships in a large dataset. In this course, Building Data Visualizations Using Matplotlib, you'll discover the basic components which make up a plot and see how you can tweak parameters and attributes to have the visualizations customized to exactly how you want it. First, you’ll grow to understand the basic APIs available in Matplotlib and where they are used and learn how to customize the display, colors, and other attributes of these plots which will have multiple axes. Next, you’ll build intermediate and advanced plots, drawing shapes and Bezier curves, using text and annotations to highlight plot elements, and normalizing the scales that are used on the x and y-axis. Lastly, you’ll use some real-world data to visualize statistical data such as mean, median mode, and outliers, cover box plots, violin plots, histograms, pie charts, stem and stack plots and autocorrelations graphs. By the end of this course you’ll not only have explored all the nitty gritty that Matplotlib has to offer; but you’ll also be capable of building production-ready visuals to embed with your UI or to display within reports and presentations.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
Course Overview (Music playing) Hi, my name is Janani Ravi and welcome to this course on Building Data Visualizations Using Matplotlib. A little about myself, I have a master's in Electrical Engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. In this course, we'll understand the basic components which make up a plot, and see how we can tweak parameters and attributes to have the visualizations customized to exactly how we want them to be. We start off by understanding the basic APIs available in Matplotlib and where they are used. We'll build basic plots and learn how to customize the display colors and other attributes of these plots. We'll work with plots which have multiple axes and also with watermarks. We'll then move onto building intermediate and advanced plots, drawing shapes and Bezier curves, using text and other annotations to highlight plot elements, and normalizing this case that we use on the X as well as the Y axis. We'll then use some real world data to visualize statistical data, such as mean, median, mode, and outliers in our data. We'll cover box plots, violin plots, histograms, pie charts, stem and stat plots, and autocorrelation graphs. This course will allow you to explore all of the nitty-gritty that Matplotlib has to offer, so you can build production-ready visuals who embed with your UI or to display within reports and presentations.
Working with the Matplotlib and Pyplot APIs Hi, and welcome to this course on Building Data Visualizations Using Matplotlib. If you're a data analyst or a data scientist and you worked with Python for any length of time, chances are you've used Matplotlib for your visualizations. Matplotlib is one of the most important packages for data visualizations, and an important reason for that is its seamless integration with other Python packages in the PyData stack. NumPy, Pandas, and even the SciKit learn libraries integrate smoothly with Matplotlib. It's possible to produce very cool-looking graphs and charts with Matplotlib without really understanding what exactly is going on, and that's what this course looks to address. In this very first module, we'll start off with the basic anatomy of a figure in Matplotlib, what it's made up of, and how you can exercise granular control over each part of this figure. This course is very, very hands-on, and you'll learn all of the features of Matplotlib with real code, basic plots, labels, titles, markers, and watermarks. This is what we'll cover in this module. We'll also see how we can build and customize multiple plots on the same figure using the axes and subplot components of Matplotlib.
Building Basic, Intermediate, and Advanced Plots with Matplotlib Hi and welcome to this module where we'll see how we can build basic, intermediate, and advanced plots with Matplotlib. If you have a visualization that uses some kind of shape, you can use Matplotlib to plot these shapes on a graph. Matplotlib can plot extremely complex shapes, arbitrary polygons, and Bezier curves that are curves plotted according to a mathematical formula. Often when you plot a graph or a visualization, there might be a single data point or multiple data points that are interesting and you want to annotate or highlight it in some way. We'll see how we can add text annotations to graphs to convey information. In this module, we'll also cover how you can use Matplotlib's twin axis functionality in order to plot different units on the same axis by making a mirror copy or a twin of the original axis. By default, Matplotlib uses the linear scale on both its X and Y axis. You can change the scales on the specific axis in order to better display your data.
Visualizing Statistical Data with Matplotlib Hi, and welcome to this module where we'll visualize statistical data in Matplotlib. So far we worked on very granular components where we configured how a plot looks and feels when we finally view it. Here we'll work on many real world datasets in order to visualize these datasets using different kinds of interesting plots. We start off by seeing how we can use box plots and violin plots to represent information and see when we would choose to use the violin plot over the box plot. Histograms and pie charts are two of the most common types of plots used to represent information. Matplotlib has built-in functions for both of these. If you're working with time series data, you often want to figure out correlations between your dataset, or if you have just one dataset, you want to see whether it's autocorrelated. We'll see how we can plot autocorrelated data using Matplotlib. We'll also see how stem plots work, which allow us to plot deltas from the previous record or from a baseline. We'll close this module out by taking a look at how we can set up stack plots in Matplotlib, which stacks information one on top of the other so that we can see their relative significance or importance.