technicallyjovialfestival

[technicallyjovialfestival]

Alcohol Impact - Research -Python script and output

Following is the overview of what was consider for writing the below mentioned python script for Alcohol impact research.

Frequency distribution variables considered in below script:

VarAlcoholDependentCount - List the count of people who require more Alcohol consumption every time and who does not.

VarAlcoholDependentPercent - List the Percentage of people who require more Alcohol consumption every time and who doe not.

VarYoungGroupList - Age wise count of people (between age 15 to 25) who show tendency of having increase in alcohol consumption then last time and they have developed alcohol dependence.

VarMiddleAgeGroupList - Age wise count of people (between age 26 to 40) who show tendency of having increase in alcohol consumption then last time and they have developed alcohol dependence.

VarOldAgeGroupList - Age wise count of people (between age 26 to 40) who show tendency of having increase in alcohol consumption then last time and they have developed alcohol dependence.

The missing data in this research is Blank data & data assign against value 2 & 9. Where data for value 2& 9 are people who are not showing increase in alcohol consumption or who have reported not applicable.

As per analysis we found that people with age 18 have more tendency to increase consumption of alcohol and have alcohol dependence.

#-------Python Script Start -------------------

# -*- coding: utf-8 -*- """ Created on Fri Jan 28 10:50:43 2022

@author: Renal R """

import pandas import numpy # any additional libraries would be imported here

data = pandas.read_csv('nesarc_pds.csv', low_memory=False)

#Conversion to numeric is failing as there are around 19% blank records. #data['S2BQ1A4'] = pandas.to_numeric(data['S2BQ1A4']) #data['S2BQ2D'] = pandas.to_numeric(data['S2BQ2D'])

#counts and percentages (i.e. frequency distributions) for each variable print("Count of the people who do and donot show the increase in Alcohol dependency /n") VarAlcoholDependentCount = data['S2BQ1A4'].value_counts(sort=False) print (VarAlcoholDependentCount) print("Percent of the people who do and donot show the increase in Alcohol dependency /n") VarAlcoholDependentPercent = data['S2BQ1A4'].value_counts(sort=False, normalize=True) print (VarAlcoholDependentPercent)

#As per this project we have 3 groups. Let us identify the subset of each group and print it. print("The list of young group (age 15 to 25) of people who show gradual increase in alcohol consumption and show alcohol dependence ") YoungGroupList = data[(data['S2BQ2D']>=str(15)) & (data['S2BQ2D']<=str(25)) & (data['S2BQ1A4']==str(1))] YoungGroupList2 = YoungGroupList.copy() VarYoungGroupList = YoungGroupList2['S2BQ2D'].value_counts(sort=False) print (VarYoungGroupList)

print("The list of middle age group (age 26 to 40) of people who show gradual increase in alcohol consumption and show alcohol dependence ") MiddleAgeGroupList = data[(data['S2BQ2D']>=str(26)) & (data['S2BQ2D']<=str(40)) & (data['S2BQ1A4']==str(1))] MiddleAgeGroupList2 = MiddleAgeGroupList.copy() VarMiddleAgeGroupList = MiddleAgeGroupList2['S2BQ2D'].value_counts(sort=False) print (VarMiddleAgeGroupList)

print("The list of old group (age 41 to 80) of people who show gradual increase in alcohol consumption and show alcohol dependence ") OldAgeGroupList = data[(data['S2BQ2D']>=str(41)) & (data['S2BQ2D']<=str(80)) & (data['S2BQ1A4']==str(1))] OldAgeGroupList2 = OldAgeGroupList.copy() VarOldAgeGroupList = OldAgeGroupList2['S2BQ2D'].value_counts(sort=False) print (VarOldAgeGroupList)

# bug fix for display formats to avoid run time errors - put after code for loading data above pandas.set_option('display.float_format', lambda x:'%f'%x)

#-------Python Script End --------------------

#-------Python Script Output - Start --------------------

In [11]: runfile('C:/Users/rerodrig/.spyder-py3/temp.py', wdir='C:/Users/rerodrig/.spyder-py3')

Count of the people who do and donot show the increase in Alcohol dependency /n

     8266

2    31467

1     3048

9      312

Name: S2BQ1A4, dtype: int64

Percent of the people who do and donot show the increase in Alcohol dependency /n

   0.191818

2   0.730211

1   0.070731

9   0.007240

Name: S2BQ1A4, dtype: float64

The list of young group (age 15 to 25) of people who show gradual increase in alcohol consumption and show alcohol dependence

18    269

20    172

16     99

21    158

23     77

24     49

15     58

25    109

22    116

17    134

19    193

Name: S2BQ2D, dtype: int64

The list of middle age group (age 26 to 40) of people who show gradual increase in alcohol consumption and show alcohol dependence

37    13

34    20

27    30

40    33

30    69

28    39

33    16

39    12

31    16

36    16

29    22

26    31

35    41

38    22

32    27

Name: S2BQ2D, dtype: int64

The list of old group (age 41 to 80) of people who show gradual increase in alcohol consumption and show alcohol dependence

42     8

57     2

48    10

58     1

51     1

43     8

44     9

65     2

41     9

56     7

50     7

45    22

49     4

47     3

6      1

53     2

70     1

80     1

46     8

64     1

67     1

52     1

8      2

66     1

55     2

59     2

60     1

63     1

Name: S2BQ2D, dtype: int64

In [12]:

#-------Python Script Output - End --------------------

[technicallyjovialfestival]

Alcohol Impact

With this data analysis I would like to research if someone starts consuming liquor, is there a tendency of gradual increase in the quantity of liquor consumption. We will further compare and identify which age group have developed the alcohol dependence. Thus confirming the hypothesis of the risk of small start of liquor consumption could lead to gradual increase in quantity of consumption and alcohol dependence.

The results will be divided in following age groups:

Young (15 to 25), Middle age (26 to 40), Older (40 to 90).

For research of data I have come up with below mentioned question referring to codebook for the NESARC study. 

“At what age group people increase drinking Liquor as formal quantity of consumption no longer gave desire effect?”

Codebook reference: S2BQ1A4, S2BQ2D.

Following is my codebook:

S2BQ1A4 - Increase drinking because amount formerly consumed no longer gave desired effect 1 - Increase drinking 2 - No effect 9 - Unknown BL - Not applicable

S2BQ2D - Age at alcohol dependence 6 to 80 - Age of alcohol dependence 99 - Unknown BL - Not applicable