Getting ready

To execute this recipe, you will need the pandas module.

No other prerequisites are required.

How to do it…

Once again, pandas already has a method that does all of this for us (the data_dummy_code.py file):

# dummy code the column with the type of the property
csv_read = pd.get_dummies(
    csv_read,
    prefix='d',
    columns=['type']
)

How it works…

The .get_dummies(...) method converts categorical variables into dummy variables. For example, consider a variable with three different levels:

1  One
2  Two
3  Three

We will need three columns to code it:

1  One  1  0  0
2  Two  0   1  0
3  Three  0  0  1

Sometimes, we can get away with using only two additional columns. However, we can use this trick only if one of the levels is, effectively, null:

1  One  1  0
2  Two  0  1
3  Zero  0  0

The first parameter to the .get_dummies(...) method is the DataFrame. The columns parameter specifies the column (or columns, as we can also pass a list) in the DataFrame to the dummy code. Specifying the prefix, we instruct the method that the names of the new columns generated should have the d_ prefix; in our example, the generated dummy-coded columns will have d_Condo names (as an example). The underscore _ character is default but can also be altered by specifying the prefix_sep parameter.