Pandas: convert categories to numbers

Question

Suppose I have a dataframe with countries that goes as:

cc | temp
US | 37.0
CA | 12.0
US | 35.0
AU | 20.0

I know that there is a pd.get_dummies function to convert the countries to 'one-hot encodings'. However, I wish to convert them to indices instead such that I will get cc_index = [1,2,1,3] instead.

I'm assuming that there is a faster way than using the get_dummies along with a numpy where clause as shown below:

[np.where(x) for x in df.cc.get_dummies().values]

This is somewhat easier to do in R using 'factors' so I'm hoping pandas has something similar.

Answer 1

First, change the type of the column:

df.cc = pd.Categorical(df.cc)

Now the data look similar but are stored categorically. To capture the category codes:

df['code'] = df.cc.cat.codes

Now you have:

   cc  temp  code
0  US  37.0     2
1  CA  12.0     1
2  US  35.0     2
3  AU  20.0     0

If you don't want to modify your DataFrame but simply get the codes:

df.cc.astype('category').cat.codes

Or use the categorical column as an index:

df2 = pd.DataFrame(df.temp)
df2.index = pd.CategoricalIndex(df.cc)

Answer 2

If you wish only to transform your series into integer identifiers, you can use pd.factorize.

Note this solution, unlike pd.Categorical, will not sort alphabetically. So the first country will be assigned 0. If you wish to start from 1, you can add a constant:

df['code'] = pd.factorize(df['cc'])[0] + 1

print(df)

   cc  temp  code
0  US  37.0     1
1  CA  12.0     2
2  US  35.0     1
3  AU  20.0     3

If you wish to sort alphabetically, specify sort=True:

df['code'] = pd.factorize(df['cc'], sort=True)[0] + 1 

Answer 3

If you are using the sklearn library you can use LabelEncoder. Like pd.Categorical, input strings are sorted alphabetically before encoding.

from sklearn.preprocessing import LabelEncoder

LE = LabelEncoder()
df['code'] = LE.fit_transform(df['cc'])

print(df)

   cc  temp  code
0  US  37.0     2
1  CA  12.0     1
2  US  35.0     2
3  AU  20.0     0