#output:

by cutting/          /and pasting

the old version of himself over his body.

If you are looking for ways to increase the energy output from your solar panels, then you have come to the right place. In this article, we will provide you with three tips to help you maximize the performance of your solar panels and get the most out o

If you are looking for ways to increase the energy output from your solar panels, then you have come to the right place. In this article, we will provide you with three tips to help you maximize the performance of your solar panels and get the most out of your investment. We will discuss the importance of proper installation and orienting your solar panels correctly, the benefits of regular…

2 sub_groups were built for male and female (Variable: SEX) who smoked in the pasted 12 months (CHECK321).

Frequency distributions was done for each sub group on nicotine dependency and age.

Output:  Nicotine dependancy slightly higher for male smoker (54,9%) than for female (50,2%). And, male smoker are older than female.

Programming Code:

# Import libraries (packages) import pandas import numpy

# define individual name of dataset data = pandas.read_csv('nesarc_pds.csv', low_memory=False)

# upper-case all DataFrame column names- place after code for loading data above data.columns = list(map(str.upper, data.columns))

# 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)

print(len(data))  # number of observations (rows) print(len(data.columns))  # number of variables (columns)

# checking the format of your variables data['ETHRACE2A'].dtype

# setting variables you will be working with to numeric data['TAB12MDX'] = pandas.to_numeric(data['TAB12MDX']) data['CHECK321'] = pandas.to_numeric(data['CHECK321']) data['S3AQ3B1'] = pandas.to_numeric(data['S3AQ3B1']) data['SEX'] = pandas.to_numeric(data['SEX']) data['AGE'] = pandas.to_numeric(data['AGE'])

print("counts 1 -male, 2- female") c1 = data['SEX'].value_counts(sort=False, dropna=False) print(c1)

print("percentages") p1 = data['SEX'].value_counts(sort=False, normalize=True) * 100 print(p1) print()

# create sub group for male and female and smoked in the past 12 months sub_male = data[(data['SEX'] == 1) & (data['CHECK321']==1)] sub_female = data[(data['SEX'] == 2) & (data['CHECK321']==1)]

print("Nicotine dependence 0-No  1-yes for male smoked past 12 months") c2 = sub_male['TAB12MDX'].value_counts(sort=False) print(c2)

p2 = sub_male['TAB12MDX'].value_counts(sort=False, normalize=True) * 100 print(p2,'in %') print()

print("Nicotine dependence 0-No  1-yes for female smoked past 12 months") c3 = sub_female['TAB12MDX'].value_counts(sort=False) print(c3)

p3 = sub_female['TAB12MDX'].value_counts(sort=False, normalize=True) * 100 print(p3,'in %') print()

print('count male - AGE in percentage') p4 = sub_male['AGE'].value_counts(sort=True, normalize=True) * 100 print(p4) print()

print('count femal Age in percentage') p5 = sub_female['AGE'].value_counts(sort=True, normalize=True) * 100 print(p5)

Output:

counts 1 -male, 2- female 1    18518 2    24575 Name: SEX, dtype: int64 percentages 1   42.972176 2   57.027824 Name: SEX, dtype: float64

Nicotine dependence 0-No  1-yes for male smoked past 12 months 0    2659 1    2184 Name: TAB12MDX, dtype: int64 0   54.903985 1   45.096015 Name: TAB12MDX, dtype: float64 in %

Nicotine dependence 0-No  1-yes for female smoked past 12 months 1    2547 0    2523 Name: TAB12MDX, dtype: int64 1   50.236686 0   49.763314 Name: TAB12MDX, dtype: float64 in %

count male smoker  - Age in percentage 47   2.787528 45   2.787528 40   2.766880 42   2.560396 38   2.560396 Name: AGE, Length: 72, dtype: float64

This week, I experienced the genuine thrill of sitting through my first Python lesson -- and the disappointment of not being able to extract “clean” data from a very complex set of numbers.  As explained last week, I was interested in examining the presence, if any, of a correlation between countries’ per-child spending at the primary school level and female literacy rates. I used two sets of data: primary-school expenditure per student as a percentage of total GDP, collected by the World Bank, and the literacy rate among females aged 15-24, as collected by the United Nations Educational, Scientific and Cultural Organization (UNESCO).  Unfortunately, I realized too late that I didn’t pick a data set that would lend itself to a frequency distribution analysis as modeled in the videos. I’ll explain my struggles after showing my program (which just designates the two sets of data I’m working with and outputs their size): 

Here’s the output: 

The number of countries in the per-child spending data (209) is correct, so is the number of years under examination(1998 to 2007), if you remove one row.  The number of countries in the female literacy table is also correct (209) but I ran into the problem of the program only recognizing ONE column for years, when in reality I adjusted the data to 2002-2011 (to accommodate scant information and reflect a four-year interval between investment and return).  The problem with this type of data is that it is anything but neat. Typically, this is what the top of the female literacy table looks like: