11. Boxplots | Data Visualization with Python | Tech2Teach

In this video, we will learn about another visualization tool: the box plot,
and we will learn how to create a using Matplotlib. So what is a box plot? A box
plot is a way of statistically representing the distribution of given
data through five main dimensions. The first dimension is minimum, which is the
smallest number in the sorted data. The second dimension is first quartile, which
is the point 25% of the way through the sorted data. In other words, a
quarter of the datapoints are less than this value. The third dimension is median,
which is the median of the sorted data. The fourth dimension is third quartile,
which is the point 75% of the way through the sorted data. In other words,
three-quarters of the data points are less than this value. And the final
dimension is maximum, which is the highest number in the sorted data. Now
let's see how we can create a box 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 for example, it is 58,639, total, and for Albania, it is
15,699 and so on. And let's name our data frame df_canada.

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