Coursera Blog

The following program creates a subset of the NESARC dataset that limits respondents to those who have had episodes in which they continued to drink to feel an effect and who were between 18 and 39 years old at the time of the survey. This subset is then used to identify respondents who have sought and received treatment for alcohol dependence. The variables include: Age & Sex of respondents Responses to questions on the type of treatment sought/received A variable was created to sum the number of different types of treatments respondents sought. Another variable was derived from that to indicate whether a respondent had received any type or treatment or not. Response values that did not indicate a yes or no to a question on treatment type were converted to NaN. Frequency counts and percentage distributions were computed for: Age Age Group Sex Number of treatment types sought Categories of number of treatment types sought Whether treatment of any kind was sought Thi...

 Assignment for Week 2: First Program Code : # -*- coding: utf-8 -*- """ Created on Sat Jul  6 10:48:29 2019 @author: gdeal """ # program to import nesarc dataset limited to respondents who ever needed to drink more to get the intended effect import pandas import numpy # option to avoid runtime errors/warnings pandas.set_option('display.float_format',lambda x: '%f'%x) # read in full nesarc dataset nesarc_df=pandas.read_csv('nesarc_pds.csv',low_memory=False) # convert variable names to upper case nesarc_df.columns=map(str.upper,nesarc_df.columns) # show number of observations and columns in base dataset # print(len(nesarc_df)) # print(len(nesarc_df.columns)) # print("printing values of sex for subset S2BQ1A2") # count_test = nesarc_df['S2BQ1A2'].value_counts(sort=False) # print(count_test) nesarc_df['S2BQ1A2'] = nesarc_df['S2BQ1A2'].convert_objects(convert_numeric=True) nesarc_df['AGE']...