Pandas One-Hot-Encoding with deterministic order

Question

say I have a categorical column in a DataFrame (for example weekday). And I want to encode it to one-hot-encoding. I am using pandas.get_dummies() to do this. But I can't see a way how to make the order deterministic. For example, I have these two dataframes

df1 = pd.DataFrame({'visitors':[220, 240, 180, 210, 220, 260, 270], 'weekday': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']})
df2 = pd.DataFrame({'visitors':[240, 180, 210, 220, 260, 270, 220], 'weekday': ['Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'Mon']})

And if I call get_dummies() on both, I get two different encodings. I can see that, as the order is different. But is there a way, how I can have any DataFrame have the same encoding, regardless of how the values come in? So for example: Mon=1000000, Tue=0100000, etc?

pd.get_dummies(df1['weekday'])
pd.get_dummies(df2['weekday'])

Answer 1

The behavior of get_dummies is consistent. You're seeing the difference because the position of visitors has changed.

print(df1)
print(pd.get_dummies(df1['weekday']))

print()

print(df2)
print(pd.get_dummies(df2['weekday']))

Here's the output:

   visitors weekday
0       220     Mon
1       240     Tue
2       180     Wed
3       210     Thu
4       220     Fri
5       260     Sat
6       270     Sun
   Fri  Mon  Sat  Sun  Thu  Tue  Wed
0    0    1    0    0    0    0    0
1    0    0    0    0    0    1    0
2    0    0    0    0    0    0    1
3    0    0    0    0    1    0    0
4    1    0    0    0    0    0    0
5    0    0    1    0    0    0    0
6    0    0    0    1    0    0    0

   visitors weekday
0       240     Tue
1       180     Wed
2       210     Thu
3       220     Fri
4       260     Sat
5       270     Sun
6       220     Mon
   Fri  Mon  Sat  Sun  Thu  Tue  Wed
0    0    0    0    0    0    1    0
1    0    0    0    0    0    0    1
2    0    0    0    0    1    0    0
3    1    0    0    0    0    0    0
4    0    0    1    0    0    0    0
5    0    0    0    1    0    0    0
6    0    1    0    0    0    0    0