Python: creating plot based on observation dates (not as a time series)

I have the following dataset

df
id medication_date 
1  2000-01-01
1  2000-01-04
1  2000-01-06
2  2000-04-01
2  2000-04-02
2  2000-04-03

I would like to first reshape the data set into days after the first observation per patient:

id day1 day2 day3 day4 
1  yes  no   no   yes 
2  yes  yes  yes  no

in order to ultimately create a plot with the above table: columns the dates and in black if yes, and white if not.

any help really appreciated it

Transform the sparse Series ('yes' medication) to dense Series by adding missing days ('no' medication) then reset the Series index (2000-01-01 -> 0, 2000-04-01 -> 0). Finally, reshape your dataframe.

def f(sr):
    # Create missing dates
    dti = pd.date_range(sr.min(), sr.max(), freq='D')
    # Fill the Series with 'yes' or 'no'
    return (pd.Series('yes', index=sr.tolist())
              .reindex(dti, fill_value='no')
              .reset_index(drop=True))

df['medication_date'] = pd.to_datetime(df['medication_date'])
out = (df.groupby('id')['medication_date'].apply(f).unstack(fill_value='no')
         .rename(columns=lambda x: f'day{x+1}').reset_index())

Output:

>>> out
   id day1 day2 day3 day4 day5 day6
0   1  yes   no   no  yes   no  yes
1   2  yes  yes  yes   no   no   no

Update

import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

colors = ["white", "black"] 
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
plt.matshow(out.set_index('id').eq('yes').astype(int), cmap=cmap)
plt.show()