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import pandas import numpy import seaborn import matplotlib.pyplot as plt

# any additional libraries would be imported here

#Set PANDAS to show all columns in DataFrame pandas.set_option('display.max_columns', None) #Set PANDAS to show all rows in DataFrame pandas.set_option('display.max_rows', None)

# bug fix for display formats to avoid run time errors pandas.set_option('display.float_format', lambda x:'%f'%x)

data = pandas.read_csv('_c10361280c0613304594ab464c014f47_nesarc_pds.csv', low_memory=False)

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 (updated) data['TAB12MDX'] = pandas.to_numeric(data['TAB12MDX']) data['CHECK321'] = pandas.to_numeric(data['CHECK321']) data['S3AQ3B1'] = pandas.to_numeric(data['S3AQ3B1']) data['S3AQ3C1'] = pandas.to_numeric(data['S3AQ3C1']) data['AGE'] = pandas.to_numeric(data['AGE'])

#subset data to young adults age 18 to 25 who have smoked in the past 12 months sub1=data[(data['AGE']>=18) & (data['AGE']<=25) & (data['CHECK321']==1)]

#make a copy of my new subsetted data sub2 = sub1.copy()

#SETTING MISSING DATA # recode missing values to python missing (NaN) sub2['S3AQ3B1']=sub2['S3AQ3B1'].replace(9, numpy.nan) # recode missing values to python missing (NaN) sub2['S3AQ3C1']=sub2['S3AQ3C1'].replace(99, numpy.nan)

recode1 = {1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1} sub2['USFREQ']= sub2['S3AQ3B1'].map(recode1)

recode2 = {1: 30, 2: 22, 3: 14, 4: 5, 5: 2.5, 6: 1} sub2['USFREQMO']= sub2['S3AQ3B1'].map(recode2)

# A secondary variable multiplying the number of days smoked/month and the approx number of cig smoked/day sub2['NUMCIGMO_EST']=sub2['USFREQMO'] * sub2['S3AQ3C1']

#univariate bar graph for categorical variables # First hange format from numeric to categorical sub2["TAB12MDX"] = sub2["TAB12MDX"].astype('category')

seaborn.countplot(x="TAB12MDX", data=sub2) plt.xlabel('Nicotine Dependence past 12 months') plt.title('Nicotine Dependence in the Past 12 Months Among Young Adult Smokers in the NESARC Study')

#Univariate histogram for quantitative variable: seaborn.distplot(sub2["NUMCIGMO_EST"].dropna(), kde=False); plt.xlabel('Number of Cigarettes per Month') plt.title('Estimated Number of Cigarettes per Month among Young Adult Smokers in the NESARC Study') ############################################################################### # Code for Week 4 Python Lesson 3 - Measures of Center & Spread

# standard deviation and other descriptive statistics for quantitative variables print ('describe number of cigarettes smoked per month') desc1 = sub2['NUMCIGMO_EST'].describe() print (desc1)

c1= sub2.groupby('NUMCIGMO_EST').size() print (c1)

print ('describe nicotine dependence') desc2 = sub2['TAB12MDX'].describe() print (desc2)

c1= sub2.groupby('TAB12MDX').size() print (c1)

print ('mode') mode1 = sub2['TAB12MDX'].mode() print (mode1)

print ('mean') mean1 = sub2['NUMCIGMO_EST'].mean() print (mean1)

print ('std') std1 = sub2['NUMCIGMO_EST'].std() print (std1)

print ('min') min1 = sub2['NUMCIGMO_EST'].min() print (min1)

print ('max') max1 = sub2['NUMCIGMO_EST'].max() print (max1)

print ('median') median1 = sub2['NUMCIGMO_EST'].median() print (median1)

print ('mode') mode1 = sub2['NUMCIGMO_EST'].mode() print (mode1)

c1= sub2.groupby('TAB12MDX').size() print (c1)

p1 = sub2.groupby('TAB12MDX').size() * 100 / len(data) print (p1)

c2 = sub2.groupby('NUMCIGMO_EST').size() print (c2)

p2 = sub2.groupby('NUMCIGMO_EST').size() * 100 / len(data) print (p2)

# A secondary variable multiplying the number of days smoked per month and the approx number of cig smoked per day # A secondary variable multiplying the number of days smoked per month and the approx number of cig smoked per day sub2['PACKSPERMONTH']=sub2['NUMCIGMO_EST'] / 20

c2= sub2.groupby('PACKSPERMONTH').size() print (c2)

sub2['PACKCATEGORY'] = pandas.cut(sub2.PACKSPERMONTH, [0, 5, 10, 20, 30, 147])

# change format from numeric to categorical sub2['PACKCATEGORY'] = sub2['PACKCATEGORY'].astype('category')

print ('pack category counts') c7 = sub2['PACKCATEGORY'].value_counts(sort=False, dropna=True) print(c7)

print ('describe PACKCATEGORY') desc3 = sub2['PACKCATEGORY'].describe() print (desc3)

sub2['TAB12MDX'] = pandas.to_numeric(sub2['TAB12MDX'])

# bivariate bar graph C->Q seaborn.catplot(x="PACKCATEGORY", y="TAB12MDX", data=sub2, kind="bar", ci=None) plt.xlabel('Packs per Month') plt.ylabel('Proportion Nicotine Dependent')

#creating 3 level smokegroup variable def SMOKEGRP (row): if row['TAB12MDX'] == 1 : return 1 elif row['USFREQMO'] == 30 : return 2 else : return 3

sub2['SMOKEGRP'] = sub2.apply (lambda row: SMOKEGRP (row),axis=1)

c3= sub2.groupby('SMOKEGRP').size() print (c3)

#creating daily smoking variable def DAILY (row): if row['USFREQMO'] == 30 : return 1 elif row['USFREQMO'] != 30 : return 0

sub2['DAILY'] = sub2.apply (lambda row: DAILY (row),axis=1)

c4= sub2.groupby('DAILY').size() print (c4)

seaborn.catplot(x='ETHRACE2A', y='DAILY', data=sub2, kind="bar", ci=None) plt.xlabel('Ethnic Group') plt.ylabel('Proportion Daily Smokers')

# you can rename categorical variable values for graphing if original values are not informative # first change the variable format to categorical if you havenâ€™t already done so sub2['ETHRACE2A'] = sub2['ETHRACE2A'].astype('category') # second create a new variable (PACKCAT) that has the new variable value labels sub2['ETHRACE2A']=sub2['ETHRACE2A'].cat.rename_categories(["White", "Black", "NatAm", "Asian", "Hispanic"])

# bivariate bar graph C->C seaborn.catplot(x='ETHRACE2A', y='DAILY', data=sub2, kind="bar", ci=None) plt.xlabel('Ethnic Group') plt.ylabel('Proportion Daily Smokers')

#check to see if missing data were set to NaN print ('counts for S3AQ3C1 with 99 set to NAN and number of missing requested') c4 = sub2['S3AQ3C1'].value_counts(sort=False, dropna=False) print(c4)

print ('counts for TAB12MDX - past 12 month nicotine dependence') c5 = sub2['TAB12MDX'].value_counts(sort=False) print(c5)

#ADDHEALTH DATA EXAMPLE

import pandas import numpy

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

#making individual ethnicity variables numeric data['H1GI4'] = pandas.to_numeric(data['H1GI4']) data['H1GI6A'] = pandas.to_numeric(data['H1GI6A']) data['H1GI6B'] = pandas.to_numeric(data['H1GI6B']) data['H1GI6C'] = pandas.to_numeric(data['H1GI6C']) data['H1GI6D'] = pandas.to_numeric(data['H1GI6D'])

#Set missing data to NAN data['H1GI4']=data['H1GI4'].replace(6, numpy.nan) data['H1GI4']=data['H1GI4'].replace(8, numpy.nan) data['H1GI6A']=data['H1GI6A'].replace(6, numpy.nan) data['H1GI6A']=data['H1GI6A'].replace(8, numpy.nan) data['H1GI6B']=data['H1GI6B'].replace(6, numpy.nan) data['H1GI6B']=data['H1GI6B'].replace(8, numpy.nan) data['H1GI6C']=data['H1GI6C'].replace(6, numpy.nan) data['H1GI6C']=data['H1GI6C'].replace(8, numpy.nan) data['H1GI6D']=data['H1GI6D'].replace(6, numpy.nan) data['H1GI6D']=data['H1GI6D'].replace(8, numpy.nan)

#count of number of ethnicity categories endorsed, NUMETHNIC data['NUMETHNIC']=data['H1GI4'] + data['H1GI6A'] + data['H1GI6B'] + data['H1GI6C'] + data['H1GI6D']

print ('counts for NUMETHNIC') c10 = data['NUMETHNIC'].value_counts(sort=False) print(c10)

#new ETHNICITY variable, categorical 1 through 6 def ETHNICITY (row): if row['NUMETHNIC'] > 1 : return 1 if row['H1GI4'] == 1 : return 2 if row['H1GI6A'] == 1: return 3 if row['H1GI6B'] == 1: return 4 if row['H1GI6C'] == 1: return 5 if row['H1GI6D'] == 1: return 6 data['ETHNICITY'] = data.apply (lambda row: ETHNICITY (row),axis=1)

# subset variables in new data frame, sub1 sub1=data[['AID','H1GI4', 'H1GI6A', 'H1GI6B', 'H1GI6C', 'H1GI6D', 'NUMETHNIC', 'ETHNICITY']] a = sub1.head (n=25) print(a)

#frequency distributions for primary and secondary ethinciity variables print ('counts for Hispanic/Latino') c10 = sub1['H1GI4'].value_counts(sort=False) print(c10)

print ('percentages for Hispanic/Latino') p10 = sub1['H1GI4'].value_counts(sort=False, normalize=True) print (p10)

print ('counts for Black/African American') c11 = sub1['H1GI6A'].value_counts(sort=False) print(c11)

print ('percentages for Black/African American') p11= sub1['H1GI6A'].value_counts(sort=False, normalize=True) print (p11)

print ('counts for American Indian/Native American') c12 = sub1['H1GI6B'].value_counts(sort=False) print(c12)

print ('percentages for American Indian/Native American') p12 = sub1['H1GI6B'].value_counts(sort=False, normalize=True) print (p12)

print ('counts for Asian/Pacific Islander') c13 = sub1['H1GI6C'].value_counts(sort=False) print(c13)

print ('percentages for Asian/Pacific Islander') p13 = sub1['H1GI6C'].value_counts(sort=False, normalize=True) print (p13)

print ('counts for White') c14 = sub1['H1GI6D'].value_counts(sort=False) print(c14)

print ('percentages for White') p14 = sub1['H1GI6D'].value_counts(sort=False, normalize=True) print (p14)

print ('counts for number of races/ethnicities endorsed') c15 = sub1['NUMETHNIC'].value_counts(sort=False) print(c15)

print ('percentages for number of races/ethnicities endorsed') p16 = sub1['NUMETHNIC'].value_counts(sort=False, normalize=True) print (p16)

# GAPMINDER DATA EXAMPLE

import pandas import numpy import seaborn import matplotlib.pyplot as plt

# any additional libraries would be imported here data = pandas.read_csv('gapminder.csv', low_memory=False)

data = data.replace(r'^\s*$', numpy.NaN, regex=True)

#setting variables you will be working with to numeric data['internetuserate'] = pandas.to_numeric(data['internetuserate']) data['urbanrate'] = pandas.to_numeric(data['urbanrate'])

desc1 = data['urbanrate'].describe() print (desc1)

desc2 = data['internetuserate'].describe() print (desc2)

#basic scatterplot: Q->Q scat1 = seaborn.regplot(x="urbanrate", y="internetuserate", fit_reg=False, data=data) plt.xlabel('Urban Rate') plt.ylabel('Internet Use Rate') plt.title('Scatterplot for the Association Between Urban Rate and Internet Use Rate')

scat2 = seaborn.regplot(x="urbanrate", y="internetuserate", data=data) plt.xlabel('Urban Rate') plt.ylabel('Internet Use Rate') plt.title('Scatterplot for the Association Between Urban Rate and Internet Use Rate')

scat3 = seaborn.regplot(x="incomeperperson", y="internetuserate", data=data) plt.xlabel('Income per Person') plt.ylabel('Internet Use Rate') plt.title('Scatterplot for the Association Between Income per Person and Internet Use Rate')

scat4 = seaborn.regplot(x="incomeperperson", y="hivrate", data=data) plt.xlabel('Income per Person') plt.ylabel('HIV Rate') plt.title('Scatterplot for the Association Between Income per Person and HIV Rate')

# quartile split (use qcut function & ask for 4 groups - gives you quartile split) print ('Income per person - 4 categories - quartiles') data['INCOMEGRP4']=pandas.qcut(data.incomeperperson, 4, labels=["1=25th%tile","2=50%tile","3=75%tile","4=100%tile"]) c10 = data['INCOMEGRP4'].value_counts(sort=False, dropna=True) print(c10)

# bivariate bar graph C->Q seaborn.catplot(x='INCOMEGRP4', y='hivrate', data=data, kind="bar", ci=None) plt.xlabel('income group') plt.ylabel('mean HIV rate')

c11= data.groupby('INCOMEGRP4').size() print (c11)

result = data.sort(['INCOMEGRP4'], ascending=[1]) print(result)

SUMMARY

We use seaborn and matplot.lib library as additional library for plotting graphs for our dataset.

By setting the missing data using replace function we can avoid confusion for our computer for generating results or for unknown values who are not assigned with proper name or description.

We create univariate bar graph for categorical variables and change the format to numerical format to categorial format. Using plt.xlabel we plot on x ais and using plt.tittle we provide a tittle to our bar chart.

There are three distribution model

The first distribution is unimodal.

It has one mode, roughly at 10, around which the observations are concentrated.

The second distribution is bimodal. It has two modes, roughly at 10 and 20,

around which the observations are concentrated.

The third distribution is kind of flat or uniform. The distribution has no modes, or no value around which the observations are concentrated. Instead, the observations are roughly uniformly distributed among

the different values. A distribution is called skewed-right.

With the right tail, the larger values is much longer than the left tail, or smaller values.

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MANEUVER PROBLEM(JAVA)

class maneuver { static int maneuver(int currX , int currY , int dimensionX , int dimensionY) { if(currX==dimensionX-1 && currY ==dimensionY-1) { return 1; } if(currX >= dimensionX || currY >=dimensionY ) { return 0; } return maneuver(currX, currY+1, dimensionX, dimensionY) + maneuver(currX+1, currY, dimensionX, dimensionY); } public static void main(String[] args) { int dimensionX = 5 ; int dimensionY = 7; System.out.println(maneuver(0, 0, dimensionX, dimensionY)); } }

import pandas import numpy

data = pandas.read_csv('_c10361280c0613304594ab464c014f47_nesarc_pds.csv',low_memory=False)

print(len(data)) print(len(data.columns))

#data["TAB12MDX"] = data["TAB12MDX"].convert_objects(convert_numeric=True) #data["CHECK321"] = data["CHECK321"].convert_objects(convert_numeric=True) #data["S3AQ3B1"] = data["S3AQ3B1"].convert_objects(convert_numeric=True) #data["S3AQ3C1"] = data["S3AQ3C1"].convert_objects(convert_numeric=True)

C1 = data["TAB12MDX"].value_counts(sort = True) print(C1) p1 = data["TAB12MDX"].value_counts(sort= False, normalize= True)*100/len(data) print(p1)

sub1 = data[(data["AGE"]>=18) & (data["AGE"]<=25) & (data["CHECK321"]==1)] print(sub1) s1 = sub1["AGE"].value_counts(sort = False) print(s1) s2 = sub1["CHECK321"].value_counts(sort = False) print(s2) sub2 = sub1.copy() print("Percentage of age") p5 = sub1["AGE"].value_counts(sort=False,normalize=True) print(p5) #SETTING ASISE THE MISSING DATA print("Count for S3AQ3B1") c2= sub2["S3AQ3B1"].value_counts(sort= False) print(c2) sub2["S3AQ3B1"]=sub2["S3AQ3B1"].replace(9,numpy.nan) print("Count for S3AQ3B1 when 9 set to nan") c3= sub2["S3AQ3B1"].value_counts(sort= False, dropna= False) print(c3)

print("Count for S3AQ3C1") c4= sub2["S3AQ3C1"].value_counts(sort= True) print(c4) sub2["S3AQ3C1"]=sub2["S3AQ3C1"].replace(99,numpy.nan) print("Count for S3AQ3C1 when 99 set to nan") c5= sub2["S3AQ3C1"].value_counts(sort= False, dropna= False) print(c5)

data.loc[(data["S2AQ3"]!=9)& (data["S2AQ8A"].isnull()),"S2AQ8A"]=11

recode1 = {1:6,2:5,3:4,4:3,5:2,6:1} sub2["USFREQ"]=sub2["S3AQ3B1"].map(recode1)

recode2 = {1:30,2:22,3:14,4:5,5:2,6:1} sub2["USFREQMO"]=sub2["S3AQ3B1"].map(recode2)

print("Counts for USFREQMO") c6=sub2["USFREQMO"].value_counts(sort=False,dropna=False) print(c6) p6=sub2["USFREQMO"].value_counts(sort=False,normalize=True) print(p6)

sub2["NUMCIGMO_EST"]=sub2["USFREQMO"] * sub2["S3AQ3C1"] sub3= sub2[["IDNUM","S3AQ3C1","USFREQMO","NUMCIGMO_EST"]] sub3.head(25) print(sub3)

#QUARTILE SPLIT print("AGE FROM CATEGORI - QUARTILE") sub2["AGEGROUP4"]=pandas.qcut(sub2.AGE, 4,labels=["1=25%tile","2=50%tile","3=75%tile","4=100%tile"]) c9=sub2["AGEGROUP4"].value_counts(sort=False,dropna=True) print(c9)

SUMMARY

By using addhealth dataset we have examined few age groups between 18-25 years old on basis of how much they smoke in a day and in a week and in a month by using few artihmetic opertors. We made secondary variables and set aside missing function using replace function and changing it ot numpy.nan

import pandas import numpy

data = pandas.read_csv('_c10361280c0613304594ab464c014f47_nesarc_pds.csv',low_memory=False) #TO PRINT THE LENGTH OF DATA SET IN COLUMNS AND ROWS print(len(data)) print(len(data.columns)) #TO COUNT THE VARIABLE FEED C1 = data["TAB12MDX"].value_counts(sort = True) print(C1) # TO KNOW THE PERCENTAGE AND NO OF INOUT IN FOLLOWING VARIABLE. p1 = data["TAB12MDX"].value_counts(sort= False, normalize= True)*100/len(data) print(p1) #TO GET OUTPUR WITH A PEATICULAR CONFITION sub1 = data[(data["AGE"]>=18) & (data["AGE"]<=25) & (data["CHECK321"]==1)] print(sub1) s1 = sub1["AGE"].value_counts(sort = False) print(s1) s2 = sub1["CHECK321"].value_counts(sort = False) print(s2)

SUMMARY:

Here we have imported two python library which we use to analyse and visualize data present in our dataset. I have use keyword like print, data, value_counts to ease my task using python programming language. My dataset is based on NESARC nicotine consumtion and a specific entry where I have analyze the consumption of tobacco by age group of 18 to 25 years.

#python #frequencydistribution #numpy #pandas #variablesort

What are the evidence of life on mars?

No liquid water is present on the surface of Mars, but researchers have suggested that a lake may have once existed on the surface of the red planet.

The researchers suggested that these possible underground water sources may have allowed microbes to live below the Martian surface.

The discovery of aluminum ore on Mars is evidence that ancient Martians built space shuttles. Scientists have not found life on Mars. And although they think they have found evidence of past surface water, they can only speculate about present subsurface water. But water cannot produce life. Even if microbial life were eventually found on Mars, its existence would still require a miracle.

IF LIFE IS FOUND THEN WE HAVE ANSWER THAT WE ARE NOT ALONE IN THIS UNIVERSE. THERE IS CIVILIZATION OTHER THAN US.

IF THERE IS CIVILIZATION IS IT ADAVANCE THAN US OR BEHIND US?

IS THERE ANY OTHER RACE WHO’S TRYING TO LOCATE ANOTHER SPECIED JUST LIKE US?

WHAT IS THE MEANING OF LIFE ON MARS?

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Mars_crater Have water presence?

It is being found that very young and preserved water bearing debris has been found on mid lattitude crater of mars which can prove prsence of life on mars.

It will be a new home for human race with new environment and atmospheric layer and more to explore for science. It’s not going to be out of box exploraiton but its gonna be a out of planet exploration.

#mars

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