statsmodels.tools.tools.categorical¶
-
statsmodels.tools.tools.
categorical
(data, col=None, dictnames=False, drop=False)[source]¶ Construct a dummy matrix from categorical variables
- Parameters
- dataarray_like
A structured array, recarray, array, Series or DataFrame. This can be either a 1d vector of the categorical variable or a 2d array with the column specifying the categorical variable specified by the col argument.
- col{
str
,int
,None
} If data is a DataFrame col must in a column of data. If data is a Series, col must be either the name of the Series or None. If data is a structured array or a recarray, col can be a string that is the name of the column that contains the variable. For all other arrays col can be an int that is the (zero-based) column index number. col can only be None for a 1d array. The default is None.
- dictnamesbool,
optional
If True, a dictionary mapping the column number to the categorical name is returned. Used to have information about plain arrays.
- dropbool
Whether or not keep the categorical variable in the returned matrix.
- Returns
- dummy_matrixarray_like
A matrix of dummy (indicator/binary) float variables for the categorical data.
- dictnames
dict
[int
,str
],optional
Mapping between column numbers and categorical names.
Notes
This returns a dummy variable for each distinct variable. If a a structured or recarray is provided, the names for the new variable is the old variable name - underscore - category name. So if the a variable ‘vote’ had answers as ‘yes’ or ‘no’ then the returned array would have to new variables– ‘vote_yes’ and ‘vote_no’. There is currently no name checking.
Examples
>>> import numpy as np >>> import statsmodels.api as sm
Univariate examples
>>> import string >>> string_var = [string.ascii_lowercase[0:5], ... string.ascii_lowercase[5:10], ... string.ascii_lowercase[10:15], ... string.ascii_lowercase[15:20], ... string.ascii_lowercase[20:25]] >>> string_var *= 5 >>> string_var = np.asarray(sorted(string_var)) >>> design = sm.tools.categorical(string_var, drop=True)
Or for a numerical categorical variable
>>> instr = np.floor(np.arange(10,60, step=2)/10) >>> design = sm.tools.categorical(instr, drop=True)
With a structured array
>>> num = np.random.randn(25,2) >>> struct_ar = np.zeros((25,1), ... dtype=[('var1', 'f4'),('var2', 'f4'), ... ('instrument','f4'),('str_instr','a5')]) >>> struct_ar['var1'] = num[:,0][:,None] >>> struct_ar['var2'] = num[:,1][:,None] >>> struct_ar['instrument'] = instr[:,None] >>> struct_ar['str_instr'] = string_var[:,None] >>> design = sm.tools.categorical(struct_ar, col='instrument', drop=True)
Or
>>> design2 = sm.tools.categorical(struct_ar, col='str_instr', drop=True)