What is the average age of an MP in each party?

import urllib.request
import re
from bs4 import BeautifulSoup

base_url = 'http://en.wikipedia.org/wiki/'
list_page = 'List_of_MPs_elected_in_the_United_Kingdom_general_election,_2015'

# Wikipedia URL for list of MPs.
url = base_url + list_page
# Get the page contents and parse.
req = urllib.request.urlopen(url)
article = req.read()
soup = BeautifulSoup(article.decode(), 'html.parser')

def get_title_and_link(cell):
    """Get the title and href attributes from the a tag in the cell element."""

    a = cell.find_all('a')[0]
    # The name link title is the MPs name, sometimes followed by
    # ' (politician)', ' (British politician)', and so on, so strip out that
    # sort of thing.
    title = a['title'].split(' (')[0]
    # strip '/wiki/' from the link
    link = a['href'][6:]
    return title, link

# The names and links we want are in the only sortable table on the page.
table = soup.find_all('table', class_='sortable')[0]
# Iterate over its rows, extracting the href and title attributes of the a
# tag (link) inside the 3rd column, which holds the MPs names. Skip the header.
for i, row in enumerate(table.find_all('tr')[1:]):
    cells = row.find_all('td')
    constituency_cell, party_cell, name_cell = cells[:5:2]
    constituency_name, constituency_link =get_title_and_link(constituency_cell)
    party, _ = get_title_and_link(party_cell)
    name, link = get_title_and_link(name_cell)
    print('\t'.join((str(i+1), name, link, party, constituency_name,
                     constituency_link)))

import urllib.request

base_url = 'http://en.wikipedia.org/wiki/'

list_name = 'MP_wikis.tsv'
for line in open(list_name):
    fields = line.split('\t')
    mp_id, name, link = fields[:3]
    print(mp_id, name)
    # Wikipedia URL for list of MPs.
    url = base_url + link
    # Get the page contents and parse.
    req = urllib.request.urlopen(url)
    article = req.read().decode()
    with open('MP-wikis/MP-{}.html'.format(mp_id), 'w') as fo:
        fo.write(article)

import glob
from bs4 import BeautifulSoup

NUMBER_OF_MPS = 650

for mp_id in range(1, NUMBER_OF_MPS+1):
    filename = 'MP-wikis/MP-{}.html'.format(mp_id)
    soup = BeautifulSoup(open(filename), 'html.parser')
    bday = soup.find_all('span', attrs={'class': 'bday'})
    try:
        print('{}\t{}'.format(mp_id, bday[0].text))
    except IndexError:
        pass

from collections import namedtuple
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt

class MemberOfParliament:
    """A class representing a member of parliament."""

    def __init__(self):
        self.age = None

    @classmethod
    def read_MP_line(self, line):
        """Create a MemberOfParliament object from a line of MP_wikis.tsv."""

        fields = line.split('\t')
        mp = MemberOfParliament()
        (s_mp_id, mp.name, mp.wiki_link, mp.party, mp.constituency,
         mp.constituency_wiki_link) = fields[:6]
        mp.mp_id = int(s_mp_id)
        return mp

    def set_age_from_dob(self, dob, now=None):
        """Set the MP's age from the provided date of birth, dob."""

        if now is None:
            now = datetime.now()
        self.age = now.year - dob.year
        if (now.month, now.day) < (dob.month, dob.day):
            self.age -= 1


def read_MPs_details(list_name='MP_wikis.tsv'):
    """Read in all MPs details from tsv file list_name."""

    MP = {}
    with open(list_name) as fi:
        for line in fi:
            mp = MemberOfParliament.read_MP_line(line)
            MP[mp.mp_id] = mp
    return MP
MPs = read_MPs_details()

# Add MP age to each MemberOfParliament instance.
now = datetime.now()
for line in open('MP_dobs.tsv'):
    fields = line.split()
    mp_id = int(fields[0])
    dob = datetime.strptime(fields[1].strip(), '%Y-%m-%d')
    MPs[mp_id].set_age_from_dob(dob, now)

# Create separate arrays of MP ages for Conservative, Labour and Other parties.
CON, LAB = 'Conservative Party', 'Labour Party'
con_mp_ages = np.array([mp.age for mp in MPs.values()
                    if mp.party==CON and mp.age is not None])
lab_mp_ages = np.array([mp.age for mp in MPs.values()
                    if mp.party==LAB and mp.age is not None])
other_mp_ages = np.array([mp.age for mp in MPs.values()
                    if mp.party not in (CON, LAB) and mp.age is not None])

# Plot a stacked barchart from the three groups of MP ages.
fig, ax = plt.subplots()
bins = np.arange(17.5,100,5)
con_hist, bin_edges = np.histogram(con_mp_ages, bins=bins)
lab_hist, bin_edges = np.histogram(lab_mp_ages, bins=bins)
other_hist, bin_edges = np.histogram(other_mp_ages, bins=bins)
bin_centres = 0.5 * (bin_edges[:-1] + bin_edges[1:])
kwargs = {'alpha': 0.8, 'width': 4, 'ec': 'none', 'align': 'center'}
colors = ['b','r','gray']
ax.bar(bin_centres, con_hist, fc=colors[0], label=CON, **kwargs)
ax.bar(bin_centres, lab_hist, bottom=con_hist, fc=colors[1], label=LAB,
       **kwargs)
ax.bar(bin_centres, other_hist, bottom=con_hist+lab_hist, fc=colors[2],
       label='Other', **kwargs)

# Calculate mean and standard deviation of each group
means = [con_mp_ages.mean(), lab_mp_ages.mean(), other_mp_ages.mean()]
stds = [con_mp_ages.std(), lab_mp_ages.std(), other_mp_ages.std()]

# Plot the mean with errorbars at ±1σ (y-positioning by hand)
ypos = [142, 133, 124]
ax.scatter(means, ypos, c=colors, marker='s', edgecolors='none', s=60)

for mean, y, std, color in zip(means, ypos, stds, colors):
    ax.errorbar(mean, y, xerr=std, color=color, elinewidth=2, capsize=5,
                capthick=2)

# Add the mean ages alongside the error bars
for i in range(3):
    ax.annotate('{:.1f}'.format(means[i]), (30, ypos[i]), va='center',
                color=colors[i])

# Tidy up and save figure.
ax.set_xlabel('Age /years')
ax.set_title('Age distribution of MPs after the 2015 election')
ax.legend(frameon=False)
ax.set_xlim(15,110)
ax.set_ylim(0,150)
plt.show()