Linear Regression with sklearn using categorical variables

Question

I am trying to run a usual linear regression in Python using sk-learn, but I have some categorical data that I don't know exactly how to handle, especially because I imported the data using pandas read.csv() and I have learned from previous experiences and reading that Pandas and sk-learn don't get along quite well (yet).

My data looks like this:

Salary  AtBat   Hits    League  EastDivision
475     315     81      1       0
480     479     130     0       0
500     496     141     1       1

I wanna predict Salary using AtBat, Hits, League and EastDivision, where League and EastDivision are categorical.

If I import the data via numpy's loadtext() I get a numpy array which in theory I could use with sklearn, but when I use DictVectorizer I get an error. My code is:

import numpy as np
from sklearn.feature_extraction import DictVectorizer as DV

nphitters=np.loadtxt('Hitters.csv',delimiter=',', skiprows=1)
vec = DV( sparse = False )
catL=vec.fit_transform(nphitters[:,3:4])

And I get the error when I run the last line catL=vec.fit_transform(nphitters[:,3:4]), the error is

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python2.7/dist-packages/sklearn/feature_extraction/dict_vectorizer.py", line 142, in fit_transform
    self.fit(X)
  File "/usr/lib/python2.7/dist-packages/sklearn/feature_extraction/dict_vectorizer.py", line 107, in fit
    for f, v in six.iteritems(x):
  File "/usr/lib/python2.7/dist-packages/sklearn/externals/six.py", line 268, in iteritems
    return iter(getattr(d, _iteritems)())
AttributeError: 'numpy.ndarray' object has no attribute 'iteritems'

I don't know how to fix it, and another thing is, once I get the categorical data working, how do I run the regression? Just as if the categorical variable were another numeric variable?

I have found several questions similar to mine, but none of them have really worked for me.

Answer 1

It looks like .fit_transform() expects a dict but .loadtxt() create a numpy array.

You can use .to_dict() after reading your data with pandas.