Writing an iterator to load data in chunks (3)

You're getting used to reading and processing data in chunks by now. Let's push your skills a little further by adding a column to a DataFrame.

In this exercise, you will be using a list comprehension to create the values for a new column 'Total Urban Population' from the list of tuples that you generated earlier. Recall from the previous exercise that the first and second elements of each tuple consist of, respectively, values from the columns 'Total Population' and 'Urban population (% of total)'. The values in this new column 'Total Urban Population', therefore, are the product of the first and second element in each tuple. Furthermore, because the 2nd element is a percentage, you need to divide the entire result by 100, or alternatively, multiply it by 0.01.

You will also plot the data from this new column to create a visualization of the urban population data.

You're going to use the data from 'ind_pop_data.csv', available in your current directory. The packages pandas and matplotlib.pyplot have been imported as pd and plt respectively for your use.

Instructions

Use pd.read_csv() to read in the file 'ind_pop_data.csv' in chunks of size 1000. Assign the result to urb_pop_reader.
Write a list comprehension to generate a list of values from pops_list for the new column 'Total Urban Population'. Use tup as the iterator variable. The output expression should be the product of the first and second element in each tuple in pops_list. Because the 2nd element is a percentage, you also need to either multiply the result by 0.01 or divide it by 100. In addition, note that the column 'Total Urban Population' should only be able to take on integer values. To ensure this, make sure you cast the output expression to an integer with int().
Create a scatter plot where the x-axis are values from the 'Year' column and the y-axis are values from the 'Total Urban Population' column.

Take Hint (-30xp)