Basic Plotting with Matplotlib

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Из курса от партнера IBM

Data Visualization with Python

101 оценка

IBM

Data Visualization with Python

101 оценка

Курс 2 из 4 — Specialization Applied Data Science

"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.

Из урока

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

Познакомьтесь с преподавателями

Alex Aklson
Ph.D., Data Scientist
IBM Developer Skills Network

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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