pandas.factorize on an entire data frame

Question

pandas.factorize encodes input values as an enumerated type or categorical variable.

But how can I easily and efficiently convert many columns of a data frame? What about the reverse mapping step?

Example: This data frame contains columns with string values such as "type 2" which I would like to convert to numerical values - and possibly translate them back later.

Answer 1

You can use apply if you need to factorize each column separately:

df = pd.DataFrame({'A':['type1','type2','type2'],                    'B':['type1','type2','type3'],                    'C':['type1','type3','type3']})  print (df)        A      B      C 0  type1  type1  type1 1  type2  type2  type3 2  type2  type3  type3  print (df.apply(lambda x: pd.factorize(x)[0]))    A  B  C 0  0  0  0 1  1  1  1 2  1  2  1 

If you need for the same string value the same numeric one:

print (df.stack().rank(method='dense').unstack())      A    B    C 0  1.0  1.0  1.0 1  2.0  2.0  3.0 2  2.0  3.0  3.0 

---

If you need to apply the function only for some columns, use a subset:

df[['B','C']] = df[['B','C']].stack().rank(method='dense').unstack() print (df)        A    B    C 0  type1  1.0  1.0 1  type2  2.0  3.0 2  type2  3.0  3.0 

Solution with factorize:

stacked = df[['B','C']].stack() df[['B','C']] = pd.Series(stacked.factorize()[0], index=stacked.index).unstack() print (df)        A  B  C 0  type1  0  0 1  type2  1  2 2  type2  2  2 

Translate them back is possible via map by dict, where you need to remove duplicates by drop_duplicates:

vals = df.stack().drop_duplicates().values b = [x for x in df.stack().drop_duplicates().rank(method='dense')]  d1 = dict(zip(b, vals)) print (d1) {1.0: 'type1', 2.0: 'type2', 3.0: 'type3'}  df1 = df.stack().rank(method='dense').unstack() print (df1)      A    B    C 0  1.0  1.0  1.0 1  2.0  2.0  3.0 2  2.0  3.0  3.0  print (df1.stack().map(d1).unstack())        A      B      C 0  type1  type1  type1 1  type2  type2  type3 2  type2  type3  type3