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 Continue reading...
