Learning Path: Python:Data Visualization with Matplotlib 2.x

This video gives an overview of entire course.

This video explains why do we need to plot data andwhat plots are appropriate for what kind of data.

This video explains how to get your machine ready to develop plots using matplotlib.

In this video, we will see how we can make some of the most common types of plots.

This video explains how the simplest kind of data is co relational and how x versus y. plot() and scatter() can be used to visualize this kind of data.

In this video, we will see how we can represent ordinal data, or the distribution of scalar datausing bar charts and histograms.

In this video, we will see how we can represent 3D scalar fields (that is, data with x,y, and z components) with images and contours.

This video explains that not all numbers are created equal: some have uncertainties associated with them!

This video explains what if a region or area is what we want to show and Also, what if a scatter plot is too dense.

In this video, we will see how we can show multiple plots together.

In this video, we will see how we can annotate the colors or styles in our plots.

In this video, we will see what components are in a matplotlib plot and how do they work together.

In this video, we will learn which container objects are used for a plot,how can we construct and use them.

In this video, we will see which coordinate systems are used in a matplotlib plot and how does a data point get translated to a position in the plot image.

In this video, we will see how matplotlib constructs the x and y axis andhow does it decide where the ticks go.

In this video, we will learn how matplotlib formats the labels for the ticks on the x and y axis.

In this video, we will see how matplotlib actually displays a plot on the screen or write to a file.

In this video, we will see how we can use Jupyter and the notebook to present and work with matplotlib effectively.

In this video, we will see how we can work with Pandas to make data manipulation easier.

In this video, we will see how we can use the rich features of Pandas Dataframes to select and manipulate data.

In this video, we will see how we can use Pandas together with matplotlib to make plots efficiently.

This video gives a glimpse of what this course offers you.

Before you start working on any new platform, it is important to understand the essential concepts mentioned in it. This video is that first step to Matplotlib!

This video is your next essential step to explore Matplotlib, where you will learn to download, install and set up the environment on your machine. Let’s do it together right now!

Now that we are all set with the environment set up, it’s time to get our hands dirty and begin writing some code.

When you have to work with different types of data and dataset, you need to know the correct way to deal with them and load it for the processing purpose. This video will show you how to load data.

It’s time to end our curiosity and jump right into plotting our first graph. Let’s have a look at some practical steps to plot a simple graph.

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.

Many elements in a Matplotlib figure can have their colors specified. There are several ways to do so. Let’s explore these methods.

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.

Lines of grids, ticks, and axes help us to visually locate and measure data values. Their distribution and style determine whether they make good visual aids for the plot or clutter the figure. Let’s demonstrate the basic methods for these, in this video.

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.

There are frequent occasions when you would prefer to have different plots aligned in the same figure. How to go about it? This video, shows few important steps to do this.

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

While aligning relevant plots side by side, we also have the option to embed a smaller plot in the main figure to visualize data of a different scope. This video, will walk you through the steps to achieve this. 

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.

Beside NumPy and SciPy, pandas is one of the most common scientific computing libraries for Python. Hence, we will learn about them in this video.

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.

Seaborn makes the task of visualizing the distribution of a dataset much easier. Let’s see how this works!

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.

While we can use Matplotlib to customize the figure aesthetics, Seaborn comes with several handy functions to make customization easier. Let’s try that here.

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.

Through the use of interactive backends, plots in Matplotlib can be embedded in Graphical User Interface (GUI) applications

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.

This video gives you some essential and general rules while plotting your figure and to make it more effective and appealing

Now you should be clear about the purpose and overall plan for every plot to make. It is time to make it good. This video will teach you to adapt your figures to make them more digestible for the brain.

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 

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.