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.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)