Count the max number of consecutive 1 and 0 in Pandas Dataframe
To check if a value has changed, you can use .diff and check if it's non-zero with .ne(0) (the NaN in the top will be considered different than zero), and then count the changes with .cumsum, like this:
df['counter'] = df.diff().ne(0).cumsum()

Afterward, you can create a second dataframe, where the indices are the groups of consecutive values, and the column values are the value (0 or 1, in your case) and length (which is what you ultimately want):
df2 = df.groupby('counter')['column1'].min().to_frame(name='value').join(
df.groupby('counter')['column1'].count().rename('number'))

The resulting max_consec0, max_consec1 are just the values in the [number] column, filtered by the [value] column:
max_consec0 = df2[df2['value']==0]['number'].tolist()
max_consec1 = df2[df2['value']==1]['number'].tolist()

You can verify that the result is [2, 1, 2, 1] and [1, 1, 2, 3], as desired.