File:Wmffund2017-2022.png

import pandas as pd
import matplotlib.pyplot as plt

rdf = pd.read_csv('https://frdata.wikimedia.org/yeardata-day-vs-sum.csv')

dates = []
dollars = []
for column, series in rdf.transpose().iterrows():
  if len(dates) > 0:
   d = dates.copy()
  for index, value in series.items():
    if column == 'date':
      dates.append(value)
    else:
      if value > 0: # drop zeros and nans
        try: # drop impossible leap days, why aren't they nans or zeros lol?
          dollars.append((pd.to_datetime(column + d.pop(0)[4:]), value))
        except:
          pass
df = pd.DataFrame(dollars, columns=['date', 'dollars'])
df.set_index('date', inplace=True)

colors = ['orange', 'purple', 'brown', 'red', 'green', 'blue']

fig, ax = plt.subplots()

for year in range(2017, 2023):
  pdf = df.loc[(df.index >= str(year) + '-11-22') & 
               (df.index <= str(year+1) + '-01-05')]
  ax.plot((pdf.index - pd.Timestamp(str(year) + '-11-22')).days, 
             pdf['dollars']/1000000, color=colors.pop(0), label=str(year))

ax.set_xlabel('Days since November 22nd')
ax.set_ylabel('Dollars (millions)')
ax.set_title('Fundraising amount received per day')

plt.legend(loc='best')
plt.show()

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