Month: September 2020

Some of what I write about here is inspired by challenges I encounter at work. Often the hardest part in describing those challenges is substituting public data and scenarios for my work-specific ones. Sites like kaggle.com and wikipedia.org really come to the rescue.

Recently, I had a circumstance where I need to process a dataframe in which one of the columns contained a list of values for each field. I think I came up with a clever way of dealing with that obstacle and would like to discuss it in these pages, but first…what sort of public data can I collect to replicate my scenario? How about some English football?! Let’s take some some football titles–say, the UEFA Champions League champion, the UEFA Europa League champion, and the UEFA Super Cup champion–and build a dataframe that shows which English football clubs have ever won any of these titles.

Step 1: Collect the raw data for each of these titles

I’ve written a few times before on how to use Python to collect public data from the Internet. This time around, I went with a slightly more manual approach. I went to each of the three Wikipedia pages, hit F12 to bring up my developer tools, and used those tools to copy just the HTML code for the data tables I was interested in–the ones listing each club team and the number of times they won the particular title. I simply copied the HTML to three different data files for subsequent processing.

Step 2: Read in the data files

The pandas read_html function is such a time saver here. Here’s the code I came up with to read in my data files:

import pandas as pd
from bs4 import BeautifulSoup
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

%matplotlib inline  # for jupyter notebook
matplotlib.style.use('seaborn')

df_champions = pd.read_html('./data/uefa_champions.txt')[0]
df_champions = df_champions[df_champions.Titles>0]
df_champions['title'] = 'UEFA Champions League champ'

df_europa = pd.read_html('./data/uefa_europa_league.txt')[0]
df_europa = df_europa[df_europa.Winners>0]
df_europa['title'] = 'UEFA Europa League champ'

df_super = pd.read_html('./data/uefa_super.txt')[0]
df_super = df_super[df_super.Winners>0]
df_super['title'] = 'UEFA Super Cup champ'

The df_champions dataframe looks like this:

These dataframes are looking good, but how do I know which teams are the English teams? Wikipedia identifies each team’s nationality with a flag icon, but pandas isn’t pulling in that data. Time for a little HTML parsing with BeautifulSoup.

Step 3: Collect the names of the English teams

Since pandas didn’t pull in the nationality information, I had to revisit each of the HTML data files and parse out that information with BeautifulSoup:

epl_teams = []
for filepath in ['./data/uefa_champions.txt', './data/uefa_europa_league.txt', './data/uefa_super.txt']:
    with open(filepath, 'r') as f:
        soup = BeautifulSoup(f)

        for th in soup.findAll('th'):
            span = th.find('span', {'class':'flagicon'})
            if span:
                a = span.find('a', {'title':'England'})
                if a:
                    epl_teams.append(th.text.strip())

Step 4: Filter each title list down to just the English teams

With the names of the English teams, I can filter my dataframes down accordingly:

df_champions = df_champions[df_champions.Club.isin(epl_teams)]
df_europa = df_europa[df_europa.Club.isin(epl_teams)]
df_super = df_super[df_super.Club.isin(epl_teams)]

Step 5: Merge the dataframes together

I need to merge my three dataframes into a single one. The pandas merge function did the trick:

df_combo = df_champions[['Club','title']].merge(df_europa[['Club','title']], on='Club', how='outer')
df_combo = df_combo.merge(df_super[['Club','title']], on='Club', how='outer')

And the results:

Step 6: Combine the “title” columns together into one

The final step–to just get this public data into a shape to replicate my original problem–is to merge the three “title” columns into a single one. Two lines do the deed:

df_combo['title'] = df_combo.apply(lambda r: [t for t in [r.title_x, r.title_y, r.title] if t is not np.nan], axis=1)
df_combo = df_combo[['Club', 'title']]

Phew. That was a fair amount of work just to pull together some public data to replicate one of my work datasets. And the actual code I wrote to analyze a dataframe containing lists in a column? Well, that will have to wait for a future post.