Title: Convert A String Categorical Variable With Patsy
Slug: pandas_convert_string_categorical_to_numeric_with_patsy
Summary: Convert A String Categorical Variable With Patsy
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon

import modules



In [1]:

    
import pandas as pd
import patsy

Create dataframe



In [2]:

    
raw_data = {'patient': [1, 1, 1, 0, 0], 
        'obs': [1, 2, 3, 1, 2], 
        'treatment': [0, 1, 0, 1, 0],
        'score': ['strong', 'weak', 'normal', 'weak', 'strong']} 
df = pd.DataFrame(raw_data, columns = ['patient', 'obs', 'treatment', 'score'])
df

Convert df['score'] into a categorical variable ready for regression (i.e. set one category as the baseline)



In [3]:

    
# On the 'score' variable in the df dataframe, convert to a categorical variable, and spit out a dataframe
patsy.dmatrix('score', df, return_type='dataframe')









    Out[3]:






  
    
      
      Intercept
      score[T.strong]
      score[T.weak]
    
  
  
    
      0
      1.0
      1.0
      0.0
    
    
      1
      1.0
      0.0
      1.0
    
    
      2
      1.0
      0.0
      0.0
    
    
      3
      1.0
      0.0
      1.0
    
    
      4
      1.0
      1.0
      0.0

Convert df['score'] into a categorical variable without setting one category as baseline

This is likely what you will want to do



In [4]:

    
# On the 'score' variable in the df dataframe, convert to a categorical variable, and spit out a dataframe
patsy.dmatrix('score - 1', df, return_type='dataframe')









    Out[4]:






  
    
      
      score[normal]
      score[strong]
      score[weak]
    
  
  
    
      0
      0.0
      1.0
      0.0
    
    
      1
      0.0
      0.0
      1.0
    
    
      2
      1.0
      0.0
      0.0
    
    
      3
      0.0
      0.0
      1.0
    
    
      4
      0.0
      1.0
      0.0

Create a variable that is "1" if the variables of patient and treatment are both 1



In [5]:

    
patsy.dmatrix('patient + treatment + patient:treatment-1', df, return_type='dataframe')









    Out[5]:






  
    
      
      patient
      treatment
      patient:treatment
    
  
  
    
      0
      1.0
      0.0
      0.0
    
    
      1
      1.0
      1.0
      1.0
    
    
      2
      1.0
      0.0
      0.0
    
    
      3
      0.0
      1.0
      0.0
    
    
      4
      0.0
      0.0
      0.0