I have a data frame like this:
df = pd.DataFrame([{'A': 1, 'B': 'p'}, {'A': 1, 'B': 'q'}, {'A': 2, 'B': 'o'}, {'A': 3, 'B': 'p'}])
df
A B
0 1 p
1 1 q
2 2 o
3 3 p
I could encode and decode it correctly with with code.
le = LabelEncoder()
df_encoded = pd.DataFrame(columns=df.columns)
df_decoded = pd.DataFrame(columns=df.columns)
for col in df.columns:
df_encoded[col] = le.fit_transform(df[col])
df_encoded
A B
0 0 1
1 0 2
2 1 0
3 2 1
for col in df.columns:
le = le.fit(df[col])
df_decoded[col] = le.inverse_transform(df_encoded[col])
df_decoded
A B
0 1 p
1 1 q
2 2 o
3 3 p
Now if I have a data frame like this, how can I encode and decode it?
dj = pd.DataFrame([{'A': [1,2], 'B': 'p'}, {'A': 1, 'B': ['p','q']}, {'A': 2, 'B': 'o'}, {'A': 3, 'B': 'p'}])
I want to have a code for each cell of ['p','q'] instead of a code for ['p','q'].