Year-wise population graph using Scrapy

I have always been amazed by phenomena like entropy and chaos. At the same time, I feel pleased while seeing straight lines and symmetric curves. So, I love to find patterns in chaos. Have you heard about the Bell curve ? Almost every natural phenomena if studied a long time follows the Bell curve. I thought, why not plot the world population vs year graph, and see how much it matches. We accomplish this by scraping the Worldometer website . To do this, we’ll use the Python framework scrapy . Then we’ll use Python’s plotting library to plot that curve. Let’s begin!

1. Scraping #

I’ll assume that you already have scrapy installed on your machine. We’ll begin by creating a project which I’ll name popscrape.

$ scrapy startproject popscrape

To keep things simple, I’ll use a single spider and no proxies. So to create the spider, we’ll put a file named main.py at popscrape/popscrape/spider.

import scrapy

class mainSpider(scrapy.Spider):
    name = 'main'
    start_urls = [
        'https://www.worldometers.info/world-population/world-population-by-year/'
    ]

    def parse(self, response):
        raw = response.css('td::text')
        i = 0
        for _ in range(int(len(raw)/7)):
            yield {
                'year': raw[i+0].get(),
                'population': raw[i+1].get(),
            }
            i += 7

The code should be pretty straightforward if you’re used to scrapy, but it can be little intimidating for newbie readers so I’ll try to explain it. The code initializes a spider named main which crawls URLs from the list start_urls. The receieved response is then parsed using parse(). The list raw stores the raw data from the table on the website. The following loop extracts data from raw and yields year and population data. To store the crawled data, we run following command in the project’s root directory.

$ scrapy crawl main -o main.jl

This gives us a JSON file named main.json.

2. Plotting #

We’ll plot the graph using matplotlib for which we’ll create plotter.py file at the project’s root directory.

import json
import matplotlib.pyplot as plt

with open('main.json', 'r') as f:
    main_dict = json.load(f)
year=[]
population=[]
for main in main_dict:
    year.append(int(main['year']))
    population.append(int((main['population'].replace(',',''))))
year.reverse()
population.reverse()
year = year[18:]
population = population[18:]

plt.plot(year,population)
plt.savefig('plot.pdf')

plotter.py reads the main.json and then stores the population and year data in lists. I dropped the first few elements to make the graph clearer. The data is thus plotted on a graph and saved as plot.pdf.

Conclusion #

That’s what we wanted to see! Thus the plot we got can be considered as the starting part of the bell curve. So we can safely assume that we are going to last at least a few more centuries. That concludes this post.
To practice more, I suggest trying to draw a similar graph for month-wise cases of COVID-19. Feel free to comment if you have any doubts.
Happy coding.