["python","pandas","series","categorical-data","binning"]
Pandas: convert categories to numbers

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.
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.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').codes Or use the categorical column as an index: df2 = pd.DataFrame(df.temp) df2.index = pd.CategoricalIndex(df.cc)