What is the most efficient & pythonic way to recode a pandas column?

Question

I'd like to 'anonymize' or 'recode' a column in a pandas DataFrame. What's the most efficient way to do so? I wrote the following, but it seems likely there's a built-in function or better way.

dataset = dataset.sample(frac=1).reset_index(drop=False) # reorders dataframe randomly (helps anonymization, since order could have some meaning)

# make dictionary of old and new values
value_replacer = 1
values_dict = {}   
for unique_val in dataset[var].unique():
    values_dict[unique_val] = value_replacer
    value_replacer += 1

# replace old values with new
for k, v in values_dict.items():
    dataset[var].replace(to_replace=k, value=v, inplace=True)

Answer 1

IIUC you want to factorize your values:

dataset[var] = pd.factorize(dataset[var])[0] + 1

Demo:

In [2]: df
Out[2]:
   col
0  aaa
1  aaa
2  bbb
3  ccc
4  ddd
5  bbb

In [3]: df['col'] = pd.factorize(df['col'])[0] + 1

In [4]: df
Out[4]:
   col
0    1
1    1
2    2
3    3
4    4
5    2

Answer 2

Alternative way

df.col.astype('category').cat.codes.add(1)
Out[697]: 
0    1
1    1
2    2
3    3
4    4
5    2
dtype: int8

Prefer using the answer of MaxU:)

%timeit df.col.astype('category').cat.codes.add(1)#Wen
1000 loops, best of 3: 437 µs per loop
%timeit df['col'] = pd.factorize(df['col'])[0] + 1#MaxU
1000 loops, best of 3: 194 µs per loop