Python Data Visualization with Matplotlib 2.x

Before we go ahead in our journey of plotting figures, we need to understand the basic structure which will let us code well and get the right output.

For an informative figure, we typically have a number of text elements, including the title,labels of axes and ticks, legend, and any additional annotations. This video will show you, how you can customize and adjust the text formats for these elements.

Many times, we may want to customize the appearance of Lines and Marker to better distinguish datasets, or for better and more consistent styling. This video demonstrates the same in detail.

Now that you know how to customize Grids and Ticks, let’s go ahead to customize axes in our plots.

We have learned to set the style details step by step so far. Let’s now move forward to use style sheets.

How can you facilitate quick comprehension of data context. Title and legends is the answer to this. This video will teach you to work with these amazing elements.

Figure layout, including the size and location of plots, directs the focus of readers. A figure with good layout facilitates data presentation in a logical flow. It is thus important to familiarize ourselves with layout settings when plotting. Let's see how to assign proper sizes, positions, and spacing to our plots.

Let’s take a step further to adjust the subplot axes or the plot area to fit the inner layout by using several ways.

Let us now learn to add text annotation to our figures by specifying the desired locations through some built-in functions in Matplotlib.

How can you increase the interest of the readers of your plots to stay focused when you have a lot of text already added for explanation? Image annotations are an answer to this. Let’s add image annotations to make our graph more attractive!

APIs are important because they are the medium of offering data in websites. It is important to know the data formats used in them. Here, we will look at the most important formats used.

Once we have imported the two datasets, we can set out on a further visualization journey. Let’s visualize the data trend in this video.

As a follow up to the previous video, we will now learn how to visualize two variations in one graph. This improves the visualization of the data.

In this video, we are going to implement a naive algorithm for classifying populations into one of the three categories. After that, we will explore different techniques of visualizing categorical data.

Color is perhaps the most important aspect of figure style, and thus it deserves its own video

This video will show you some practical steps to work with the Quandl dataset and retrieve data for End-of-Day stock.

This video introduces three major ways to create faceted plots, which are seaborn.factorplot(), seaborn.FacetGrid(), and seaborn.pairplot().

This section will walk you through the two special plot types that come in handy if you want the maximize space efficiency. Those are Heatmaps and Candlestick plots.

This video will put more focus on 3D scatter plots and bar charts.

These data portals often provide Application Programming Interfaces for programmatic access to data. However, APIs are not available for some datasets; hence, we resort to good old web scraping techniques to extract information from websites.

Matplotlib backends differ much more than just in the support of graphical formats. Backends handle so many things behind the scenes! Let’s have a look at the Non-interactive backends first.

Matplotlib was not originally designed for creating animations, and there are GPU-accelerated Python animation packages that may be more suitable for such a task.

Let’s revisit more variants of bar charts–stacked bar chart and layered histograms, which are commonly used in scientific publications to summarize and describe data and make it more interesting.

In the era of big data analysis, it is common to deal with datasets with a large number of features or dimensions. Visualization of data with high dimensionality is extremely challenging. This video will teach you

• Install scikit-learn via PyPI
• Look at Principal Component Analysis (PCA)
• Use t-distrubuted Stochastic Neighbor Embedding (t-SNE)

Let’s take a step ahead to work with some real-world dataset which is really huge and visualize the statistics through plots.

This video will demonstrate how you can incorporate a map-based visualization, which is powered by the GeoPandas library.

This video will show you how you could combine both geographical and population health information of the US. Here we go!

Since we've spent a significant amount of time discussing death rate, let us conclude this section with one final analysis of two cancer datasets, in this video.

• Initialize a new subplot
• Extract the Overall Survival Status
• Perform log rank test