Bar Charts

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En provenance du cours de IBM

Data Visualization with Python

101 notes

IBM

Data Visualization with Python

101 notes

Cours 2 sur 4 dans 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.

À partir de la leçon

Basic and Specialized Visualization Tools

In this module, you learn about area plots and how to create them with Matplotlib, histograms and how to create them with Matplotlib, bar charts, and how to create them with Matplotlib, pie charts, and how to create them with Matplotlib, box plots and how to create them with Matplotlib, and scatter plots and bubble plots and how to create them with Matplotlib.

Rencontrer les enseignants

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

In this video, we will learn about an additional visualization tool, namely the

bar chart, and learn how to create it using Matplotlib. A bar chart is a very

popular visualization tool. Unlike a histogram, a bar chart also known as a

bar graph is a type of plot where the length of each bar is proportional to

the value of the item that it represents. It is commonly used to compare the

values of a variable at a given point in time. For example, say we're interested in

visualizing in a discrete fashion how immigration from Iceland to Canada

looked like from 1980 to 2013. One way to do that is by building a bar chart where

the height of the bar represents the total immigration from Iceland to Canada

in a particular year. So how do we do that with Matplotlib. Before we go over

the code to do that, let's do a quick recap of our dataset. Recall that each

row represents a country and contains metadata about the country such as where

it is located geographically and whether it is developing or developed. Each row

also contains numerical figures of annual immigration from that country to

Canada from 1980 to 2013.

Now let's process the dataframe so that the country name becomes the index of

each row. This should make retrieving rows pertaining to specific countries a

lot easier. Also let's add an extra column which represents the cumulative

sum of annual immigration from each country from 1980 to 2013.

So for Afghanistan for example, it is 58,639, total, and for Albania it is

15,699 and so on. And let's name our dataframe, df_canada. So now

that we know how our data is stored in the dataframe, df_canada,

let's see how we can use Matplotlib to generate a bar chart to visualize how

immigration from Iceland to Canada looked like from

1980 to 2013. As usual, we start by importing Matplotlib and its scripting interface.

Then we use the years variable to create a new dataframe; let's name it

df_iceland, which includes the data pertaining to annual immigration

from Iceland to Canada and excluding the total column. Then we call the plot

function on df_iceland and we set kind equals bar to generate a bar

chart. Then to complete the figure we give it a title and we label its axes.

Finally, we call the show function to display the figure. And there you have it.

A bar chart that depicts the immigration from Iceland to Canada from 1980 to 2013.

By examining the bar chart we notice that immigration to Canada from Iceland

has seen an increasing trend since 2010. I'm sure that the curious among you are

already wondering who the culprit behind this increasing trend is. In the lab

session, we reveal the reason and we also learn how to create a bar chart

with horizontal bars, so make sure to complete this module's lab session. And

with this, we conclude our video on bar charts. I'll see you in the next video.

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