pandas: Convert string column to ordered Category?

I’m working with pandas for the first time. I have a column with survey responses in, which can take ‘strongly agree’, ‘agree’, ‘disagree’, ‘strongly disagree’, and ‘neither’ values.

This is the output of describe() and value_counts() for the column:

count      4996
unique        5
top       Agree
freq       1745
dtype: object
Agree                1745
Strongly agree        926
Strongly disagree     918
Disagree              793
Neither               614
dtype: int64

I want to do a linear regression on this question versus overall score. However, I have a feeling that I should convert the column into a Category variable first, given that it’s inherently ordered. Is this correct? If so, how should I do this?

I’ve tried this:

df.EasyToUseQuestionFactor = pd.Categorical.from_array(df.EasyToUseQuestion)
print df.EasyToUseQuestionFactor

This produces output that looks vaguely right, but it seems that the categories are in the wrong order. Is there a way that I can specify ordering? Do I even need to specify ordering?

This is the rest of my code right now:

df = pd.read_csv('./data/responses.csv')
lm1 = ols('OverallScore ~ EasyToUseQuestion', data).fit()
print lm1.rsquared 

Answer

Yes you should convert it to categorical data and this should do the trick

likert_scale = {'strongly agree':2, 'agree':1, 'neither':0, 'disagree':-1, 'strongly disagree':-2}
df['categorical_data'] = df.EasyToUseQuestion.apply(lambda x: likert_scale[x])