Area Plots

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Aus dem Kurs von IBM

Data Visualization with Python

2004 Bewertungen

IBM

Data Visualization with Python

2004 Bewertungen

Kurs 7 von 9 in Specialization IBM Data Science Professional Certificate

"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.

Aus der Unterrichtseinheit

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.

Treffen Sie die Kursleiter

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

In this video, we will learn about another visualization tool: the area plot,

which is actually an extension of the line plot that we learned about in an

earlier video. So what is an area plot? An area plot also known as an area chart or

graph is a type of plot that depicts accumulated totals using numbers or

percentages over time. It is based on the line plot and is commonly used when

trying to compare two or more quantities. So how can we generate an area plot 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, it is 58,639, total, and for Albania, it is 15,699 and so

on, and let's name our data frame df_canada. So now that we know

how our data is stored in the dataframe, df_canada, let's try to

generate area plots for the countries with the highest number of immigration

to Canada. We can try to find these countries by sorting our dataframe in

descending order of cumulative total immigration from 1980 to 2013. We use the

sort_values function to sort our dataframe in descending order and

here is the result. So it turns out that India followed by China then the UK, Philippines,

and Pakistan are the top five countries with the highest number of

immigration to Canada. So can we now go ahead and generate the area plots using

the first five rows of this dataframe? Not quite yet. First we need to create a

new dataframe of only these five countries and we need to exclude the

total column. More importantly, to generate the area plots for these

countries, we need the years to be plotted on the horizontal axis and the

annual immigration to be plotted on the vertical axis.

Note that Matplotlib plots the indices of a dataframe on the horizontal axis,

and with the dataframe as shown, the countries will be plotted on the

horizontal axis. So to fix this, we need to take the transpose of the dataframe.

Let's see how we can do this. After we sort our dataframe in descending order

of cumulative annual immigration, we create a new dataframe of the top five

countries and let's call it df_top5. We then select only

the columns representing the years 1980 to 2013 in order to exclude the total

column before applying the transpose method. The resulting dataframe is

exactly what we want, with five columns where each column represents one of the

top five countries and the years being the indices. Now we can go ahead and call

the plot function on dataframe df_top5 to generate the area

plots. To do that, first we import Matplotlib as mpl and its

scripting interface as plt. Then we call the plot function on the dataframe df_top5

and we set kind equals area to generate an area plot.

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. Note that here we're generating

the area plot using the inline backend. And there you have it: an area plot that

depicts the immigration trend of the five countries with

the highest immigration to Canada from 1980 to 2013. In the lab session, we

explore area plots in more details, so make sure to complete this module's lab

session. And with this, we conclude our video on area plots. I'll see you in the

next video.

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