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Basic Plotting with Matplotlib

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Data Visualization with Python

4.6 (4,093 ratings) | 32K Students Enrolled

Course 7 of 9 in the IBM Data Science Specialization

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Skills You'll Learn

Python Programming, Data Virtualization, Data Visualization (DataViz), Matplotlib

Reviews

4.6 (4,093 ratings)

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DD

Mar 03, 2019

The best course in this specialization, so far. Great balance between theory and practice. Interesting and demanding exercises and assignment. Theory explained in friendly way.

NV

Jul 31, 2019

The course contents and lab work are appropriate and useful, however the final lab assignment using folium 0.5 to generate a choropleth layer was fraught with technical errors.

From the lesson

Introduction to Data Visualization Tools

In this module, you will learn about data visualization and some of the best practices to keep in mind when creating plots and visuals. You will also learn about the history and the architecture of Matplotlib and learn about basic plotting with Matplotlib. In addition, you will learn about the dataset on immigration to Canada, which will be used extensively throughout the course. Finally, you will briefly learn how to read csv files into a pandas dataframe and process and manipulate the data in the dataframe, and how to generate line plots using Matplotlib.

Introduction to Data Visualization4:36

Introduction to Matplotlib6:26

Basic Plotting with Matplotlib4:39

Dataset on Immigration to Canada2:43

Taught By

Alex Aklson
Ph.D., Data Scientist

Transcript

In this video, we will learn how to use Matplotlib to create plots and we will do so using the Jupyter notebook as our environment. Now Matplotlib is a well-established data visualization library that is well supported in different environments such as in Python scripts, in the iPython shell, web application servers, in graphical user interface toolkits as well as the Jupyter notebook. Now for those of you who don't know what the Jupyter notebook is, it's an open source web application that allows you to create and share documents that contain live code visualizations and some explanatory text as well. Jupyter has some specialized support for Matplotlib and so if you start a Jupyter notebook, all you have to do is import Matplotlib and you're ready to go. In this course, we will be working mostly with the scripting interface. In other words, we will learn how to create almost all of the visualization tools using the scripting interface. As we proceed in the course, you will appreciate the power of this interface when you find out that you can literally create almost all of the conventional visualization tools such as histograms, bar charts, box plots and many others using one function only: the plot function. Let's start with an example. Let's first import the scripting interface as plt and let's plot a circular mark at the position (5, 5), so x equals 5 and y equals 5. Notice how the plot was generated within the browser and not in a separate window for example. If the plot gets generated in a new window then you can enforce generating plots within the browser using what's called a magic function. a magic function starts with % Matplotlib, and to enforce plots to be rendered within the browser, you pass in inline as the backend. Matplotlib has a number of different backends available. One limitation of this backend is that you cannot modify a figure once it's rendered. So after rendering the above figure, there is no way for us to add, for example, a figure title or label its axes. You will need to generate a new plot and add a title and the axes labels before calling the show function. A backend that overcomes this limitation is the notebook backend. With the notebook backend in place, if a plt function is called, it checks if an active figure exists, and any functions you call will be applied to this active figure. If a figure does not exist, it renders a new figure. So when we call the plt.plot function to plot a circular mark at position (5, 5), the backend checks if an active figure exists since there isn't an active figure it generates a figure and adds a circular mark to position (5, 5). And what is beautiful about this back end is that now we can easily add a title for example or labels to the axes after the plot was rendered, without the need to regenerate the figure. Finally, another thing that is great about Matplotlib is that pandas also has a built-in implementation of it. Therefore, plotting in pandas is as simple as calling the plot function on a given pandas series or dataframe. So, say we have a data frame of the number of immigrants from India and China to Canada from 1980 to 1996 and say we're interested in generating a line plot of these data. All we have to do is call the plot function on this dataframe which we called India_China_df and set the parameter kind to line and there you have it: a line plot of the data in the data frame. Plotting a histogram of the data is not any different. So say we would like to plot a histogram of the India column in our dataframe. All we have to do is call the plot function on that column and set the parameter kind to hist, for histogram. And there you have it. A histogram of the number of Indian immigrants to Canada from 1980 to 1996. This concludes our video on basic plotting with Matplotlib. See you in the next video.

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