Wikipedia Pull Data from table sample

I was researching some data sets and had the need to pull population data from Wikipedia. I looked at several articles on doing this and they all seemed either very basic or not current. I had already done an article on the Wikipedia Python package so I decided to try to write one on pulling table data out of a Wikipedia page. The packages I used were urllib and Beautiful Soup. I could have done this with other packages (requests comes to mind) but this is very simple and it works. Many Wikipedia pages offer tables to display data and most of them happen to be the only tables on the page or have unique IDs. Unfortunately, on the "List_of_states_and_territories_of_the_United_States_by_population" page on Wikipedia, There is a table for state rankings and another for rankings by region as well. So I would need to only pull table data for the specific table I needed, which was not too hard.

So to start, how do you pull data from a Wikipedia page? urllib works for me so that's what I used:

So we import urlopen from urllib.request and beautiful soup from bs4. Setup the url and then open and read in the content. The next step is to setup your data with BeautifulSoup. Quick note: I have hardcoded some things and removed the code from the class it was in for simplicity sake. The next code snippet reads through the Beautiful soup(BS) dataset and parses the data for me.

for row in tableData.select('table.wikitable.sortable tbody tr'):
            rowCount += 1
            if done:
                break
            if rowCount >= 3:  # data_starts at row 3:
                colCount = 0
                for col in row.findAll('td'):
                    colCount += 1
                    if colCount == 1 and '—' in col.get_text():  # This ends the Pop data I want
                        done = True
                        break
                    if colCount == 3: # state is in column 3
                        state_data[col.get_text()] = ['None', 'None', 'None']
                        state_temp = col.get_text()
                    elif colCount == 4:  # column where 2019 population estimate is
                        state_data[state_temp][0] = (col.get_text())
                    elif colCount == 5:  # column where 2010 census population is
                        state_data[state_temp][1] = (col.get_text())
                    elif colCount == 12: # column where US population percent is
                        state_data[state_temp][2] = (col.get_text())

That first line is where most of the magic happens. Most tables on the Wikipedia site use the table classes, wikitable and sortable. BS has a great interface called select where you can use HTML tags (table in this case) and then further find the exact table you want using css tags, so that explains the .wikitable.sortable . Then we tell BS that we want to further filter our page table with the tbody and tr tags that are found within the table we want. The rest is just simplified for loop to loop through the table rows and pull out the State name and population statistics I needed. The result is the data shown below.

Here are the results for U.S Population by State:

State	2019 Population Estimate	2010 Census Population	Percent of US Population
39,237,836	37,253,956	5.3%	11.80%
29,527,941	25,145,561	17.4%	8.70%
21,781,128	18,801,310	15.85%	6.43%
19,835,913	19,378,102	2.36%	6.03%
12,964,056	12,702,379	2.06%	3.88%
12,671,469	12,830,632	–0.1%	3.82%
11,780,017	11,536,504	2.3%	3.52%
10,799,566	9,687,653	10.6%	3.20%
10,551,162	9,535,483	9.5%	3.12%
10,050,811	9,883,640	2.0%	3.01%
9,267,130	8,791,894	5.7%	2.77%
8,642,274	8,001,024	7.9%	2.58%
7,738,692	6,724,540	14.6%	2.30%
7,276,316	6,392,017	11.9%	2.13%
6,984,723	6,547,629	7.4%	2.10%
6,975,218	6,346,105	8.9%	2.06%
6,805,985	6,483,802	4.7%	2.02%
6,165,129	5,773,552	7.0%	1.84%
6,168,187	5,988,927	2.8%	1.84%
5,895,908	5,686,986	3.6%	1.76%
5,812,069	5,029,196	14.8%	1.72%
5,707,390	5,303,925	7.6%	1.70%
5,190,705	4,625,364	10.7%	1.53%
5,039,877	4,779,736	5.1%	1.50%
4,624,047	4,533,372	2.0%	1.39%
4,505,836	4,339,367	3.8%	1.35%
4,237,256	3,831,074	10.6%	1.26%
3,959,353	3,751,351	5.5%	1.18%
3,605,944	3,574,097	0.9%	1.08%
3,285,874	3,725,789	–‍11.8%	0.98%
3,271,616	2,763,885	18.4%	0.98%
3,190,369	3,046,355	4.7%	0.95%
3,104,614	2,700,551	15.0%	0.93%
3,025,891	2,915,918	3.8%	0.90%
2,961,279	2,967,297	–0.2%	0.88%
2,937,880	2,853,118	3.0%	0.88%
2,117,522	2,059,179	2.8%	0.63%
1,961,504	1,826,341	7.4%	0.59%
1,839,106	1,567,582	17.3%	0.55%
1,793,716	1,852,994	–3.2%	0.54%
1,441,553	1,360,301	6.0%	0.43%
1,388,992	1,316,470	5.5%	0.41%
1,362,359	1,328,361	2.6%	0.41%
1,097,379	1,052,567	4.3%	0.33%
1,084,225	989,415	9.6%	0.32%
989,948	897,934	10.2%	0.30%
886,667	814,180	8.9%	0.26%
779,094	672,591	15.8%	0.23%
733,391	710,231	3.3%	0.22%
689,545	601,723	14.6%	0.21%
643,077	625,741	2.8%	0.19%
576,851	563,626	2.3%	0.17%
153,836	159,358	–3.5%	0.05%
87,146	106,405	–18.1%	0.03%
49,710	55,519	–10.5%	0.01%
47,329	53,883	–12.2%	0.01%
Back to top	Total States and Territories found: 56