Once we have imported the two datasets, we can set out on a further visualization journey. Let's begin by plotting the world population trends from 1950 to 2017. To select rows based on the value of a column, we can use the following syntax: df[df.variable_name == "target"] or df[df['variable_name'] == "target"], where df is the dataframe object. Other conditional operators, such as larger than > or smaller than <, are also supported. Multiple conditional statements can be chained together using the "and" operator &, or the "or" operator |.
To aggregate the population across all age groups within a year, we are going to rely on df.groupby().sum(), as shown in the following example:
import matplotlib.pyplot as plt # Select the aggregated population data from the world for both genders, # during 1950 to 2017. selected_data = data[(data.Location == 'WORLD') & (data.Sex == 'Both') & (data.Time <= 2017) ] # Calculate aggregated population data...