In this video, things will start getting more exciting. We will generate our first

visualization tool: the line plot. So what is a line plot? As its name suggests, it

is a plot in the form of a series of data points connected by straight line

segments. It is one of the most basic type of chart and is common in many

fields not just data science. The more important question is when to use line

plots. The best use case for a line plot is when you have a continuous dataset

and you're interested in visualizing the data over a period of time. As an

example, say we're interested in the trend of immigrants from Haiti to Canada.

We can generate a line plot and the resulting figure will depict the trend

of Haitian immigrants to Canada from 1980 to 2013. Based on this line plot, we

can then research for justifications of obvious anomalies or changes. So in this

example, we see that there is a spike of immigration from Haiti to Canada in 2010.

A quick Google search for major events in Haiti in 2010 would return the tragic

earthquake that took place in 2010, and therefore this influx of immigration to

Canada was mainly due to that tragic earthquake. Okay now how can we generate

this line plot? Before we go over the code to do that, let's do a quick recap

of our dataset. 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 querying specific countries 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 dataframe df_canada. So now

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

let's generate the line plot corresponding to immigration from Haiti.

First, we import Matplotlib as mpl and its scripting interface as plt. Then,

we call the plot function on the row corresponding to Haiti and we set kind

equals line to generate a line plot. Note that we used years which is a list

containing string format of years from 1980 to 2013 in order to exclude the

column of total immigration that we added. 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 this is the code to generate the line plot using the

magic function % matplotlib with the inline backend. And there you have it: a

line plot that depicts immigration from Haiti to Canada from 1980 to 2013.

In the lab session, we explore line plots in more details so make sure to

complete this module's lab session. This concludes our video on line plots. I'll

see you in the next video.