Tracking German election polling surveys
Polling surveys of German parties with data aggregated daily from different opinion research centers
The data for this script is pulled from https://www.wahlrecht.de/umfragen. Below the code, you can find the output figures.
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import matplotlib.dates as mdates
from scipy.ndimage import gaussian_filter1d
table_urls = ["https://www.wahlrecht.de/umfragen/allensbach.htm",
"https://www.wahlrecht.de/umfragen/emnid.htm",
"https://www.wahlrecht.de/umfragen/forsa.htm",
"https://www.wahlrecht.de/umfragen/politbarometer.htm",
"https://www.wahlrecht.de/umfragen/gms.htm",
"https://www.wahlrecht.de/umfragen/dimap.htm",
"https://www.wahlrecht.de/umfragen/yougov.htm",
#"https://www.wahlrecht.de/umfragen/insa.htm" # broken data
]
names = ["Allensbach", "Kantar", "Forsa" "Forsch’gr. Wahlen", "GMS", "Infratest", "dimap", "Yougov", "INSA"]
# get the election results
election_results = pd.read_html("https://www.wahlrecht.de/umfragen/")[1]
election_results["percent"] = election_results["Bundes-tagswahl"].iloc[1:].str.replace(',', '.').str.rstrip('%').astype('float')
election_results = election_results.set_index("Institut")
# get polling results
party_to_watch = "AfD"
def get_party_results(party_to_watch = "AfD"):
print(f"---------- {party_to_watch} ----------")
plt.figure(figsize=(5, 3), dpi=300)
for i, table_url in enumerate(table_urls):
# hotfix for linke table entry
party_name = "LINKE" if party_to_watch == "DIE LINKE" else party_to_watch
print(f"Getting data from {names[i]} ...")
# magic function to get tables from a website, <3
df = pd.read_html(table_url)[1]
# cut last 4 lines which are trash
df = df.iloc[:-4]
df = df.replace("–")
# convert to datetime
df["Datetime"] = pd.to_datetime(df["Datum"], format="%d.%m.%Y") if "Datum" in df.columns else pd.to_datetime(df["Unnamed: 0"], format="%d.%m.%Y")
# get rid of trash
df = df.drop(columns=["Unnamed: 0", "Unnamed: 1"])
# list of parties
parties = df.columns[:6]
for party in parties:
df[party] = df[party].str.replace(',', '.').str.rstrip('%').astype('float')
smoothed = gaussian_filter1d(df[party_name], 1)
print(f"\tLast value: {df[party_name].iloc[0]}%")
print(f"\tLast datapoint: {df['Datetime'].iloc[0].date()}")
plt.plot(df["Datetime"], smoothed,label=names[i], c="C" + str(i))
plt.plot(df["Datetime"], df[party_name], ls=':', lw=1, c="C" + str(i))
from_weeks = 24 * 4
to_weeks = 4
# add line that marks results from last elections
plt.hlines(election_results.loc[party_to_watch, "percent"], datetime.datetime.now() - datetime.timedelta(weeks=from_weeks), datetime.datetime.now() + datetime.timedelta(weeks=to_weeks), color='k', ls='--', label=f'{party_to_watch} Bundestagswahl 2017')
# adjust plot settings
plt.title(f"Watch the {party_to_watch} go pew")
plt.xlim([datetime.datetime.now() - datetime.timedelta(weeks=from_weeks), datetime.datetime.now() + datetime.timedelta(weeks=to_weeks)])
myFmt = mdates.DateFormatter("%d.%m.%Y")
plt.gca().xaxis.set_major_formatter(myFmt)
plt.grid()
plt.legend(fontsize=8, bbox_to_anchor=(1.0, 1.0))
plt.ylabel("Polling result [%]")
plt.setp(plt.gca().get_xticklabels()[::2], visible=False)
plt.xticks(rotation=45)
plt.savefig(f"../images/icon_{party_to_watch.replace('/', '_').replace(' ' , '_').lower()}.png");
plt.show()
