In week four, we finally get to see the results of our efforts over the last few weeks: the visualization of interaction of our chosen variables. In review: I chose to look at the effect of religiosity on racial acceptance. Thus, religiosity is my independent variable, and racial acceptance is my dependent variable. These are both quantitative variables, so I chose to make histograms for each:
Here we see that religiosity ranged from 0.0 to 2.5 (this is simply an index; the numbers themselves are only meaningful in their own context), with a single mode at about 0.5, though the mean is 0.75. There is an obvious right skew (the long tail is on the right).
Racial acceptance is also skewed, but to the left, with a mode about 0.8 and mean of 0.69 in a range of 0.0 to 1.0.
My next step is to make a scatterplot of the two variables.
My hypothesis was that religiosity and racial acceptance would be positively correlated, but this plot clearly shows no correlation between the two. In fact, data points cluster in the area indicating relatively low religiosity and high acceptance. To better examine my original hypothesis, I would seek out more high-religiosity individuals.
One caveat: the 2012 Outlook on Life survey (which I used for this project) included individuals from various ethnicities/races, but the proportion does not reflect the general population: about half were black, 40% white, and 10% others. Therefore, I would hesitate to apply these conclusions to the general population.
My code is below in case you are interested.
"""
Analysis of Outlook on Life 2012 dataset using Python
created Tuesday, November 10, 2020
@author Joel Caren
"""
import pandas
import numpy
import seaborn
import matplotlib.pyplot as plt
data = pandas.read_csv('oolpds.csv',low_memory = False) # read data file into 'data' using pandas and fend off a memory error
print (len(data)) #number of observations (rows)
print (len(data.columns)) #number of variables (columns)
#check format of variables
data['W1_M1'].dtype
#setting variables you will be working with to numeric; errors="coerce" fixes missing data
#data['W1_C1'] = pandas.to_numeric(data['W1_C1'], errors = "coerce") #pol party? (R=1,D=2,I=3,other=4)
data['W1_D11'] = pandas.to_numeric(data['W1_D11'], errors = "coerce") #rate republicans (0-100)
data['W1_D12'] = pandas.to_numeric(data['W1_D12'], errors = "coerce") #rate dems (0-100)
data['W1_E4'] = pandas.to_numeric(data['W1_E4'], errors = "coerce") #date outside race? y=1
data['W1_E61_C'] = pandas.to_numeric(data['W1_E61_C'], errors = "coerce") #against biracial kids (black), 1=agree--4=disagree
data['W1_E62_C'] = pandas.to_numeric(data['W1_E62_C'], errors = "coerce") #against biracial kids (latino), 1=agree--4=disagree
data['W1_E63_C'] = pandas.to_numeric(data['W1_E63_C'], errors = "coerce") #against biracial kids (white), 1=agree--4=disagree
data['W1_E64_C'] = pandas.to_numeric(data['W1_E64_C'], errors = "coerce") #against biracial kids (asian), 1=agree--4=disagree
data['W1_M1'] = pandas.to_numeric(data['W1_M1'], errors = "coerce") #which religion? none=13
data['W1_M1A'] = pandas.to_numeric(data['W1_M1A'], errors = "coerce") #born again? y=1,n=2,not asked=3,refuse=4
data['W1_M2'] = pandas.to_numeric(data['W1_M2'], errors = "coerce") #church race? B=1,W=2,L=3,A=4,other=5,don't know=6
data['W1_M4'] = pandas.to_numeric(data['W1_M4'], errors = "coerce") #allow women clergy? 1=agree--4=disagree
data['W1_M5'] = pandas.to_numeric(data['W1_M5'], errors = "coerce") #how often attend church? mult times/wk=1,never=6
data['W1_M6'] = pandas.to_numeric(data['W1_M6'], errors = "coerce") #active church member? y=1
data['W1_N1B'] = pandas.to_numeric(data['W1_N1B'], errors = "coerce") #rate native americans (0-100)
data['W1_N1C'] = pandas.to_numeric(data['W1_N1C'], errors = "coerce") #rate latinos (0-100)
data['W1_N1D'] = pandas.to_numeric(data['W1_N1D'], errors = "coerce") #rate whites (0-100)
data['W1_N1E'] = pandas.to_numeric(data['W1_N1E'], errors = "coerce") #rate blacks (0-100)
data['W1_N1F'] = pandas.to_numeric(data['W1_N1F'], errors = "coerce") #rate asians (0-100)
data['W1_O1'] = pandas.to_numeric(data['W1_O1'], errors = "coerce") #blacks should teach kids vs racism, 1=no--5=yes
data['W1_O3'] = pandas.to_numeric(data['W1_O3'], errors = "coerce") #blacks should teach kids not all whites racist, 1=no--5=yes
data['W1_QA5D'] = pandas.to_numeric(data['W1_QA5D'], errors = "coerce") #blacks should segregate, 1=agree--4=disagree
data['W2_QL2B'] = pandas.to_numeric(data['W2_QL2B'], errors = "coerce") #gays as priests? 1=agree--4=disagree
data['W2_QL3'] = pandas.to_numeric(data['W2_QL3'], errors = "coerce") #homosex immoral? 1=agree--4=disagree
data['W2_QL4'] = pandas.to_numeric(data['W2_QL4'], errors = "coerce") #laws vs gay discrimination? y=1--n=5
data['PPETHM'] = pandas.to_numeric(data['PPETHM'], errors = "coerce") #your race? w=1,b=2,other=3,L=4,mix=5
# recode missing values to python missing (NaN)
#data['W1_C1']=data['W1_C1'].replace(-1, numpy.nan)
data['W1_D11']=data['W1_D11'].replace(998, numpy.nan)
data['W1_D12']=data['W1_D12'].replace(998, numpy.nan)
data['W1_E4']=data['W1_E4'].replace(-1, numpy.nan)
data['W1_E61_C']=data['W1_E61_C'].replace(-1, numpy.nan)
data['W1_E62_C']=data['W1_E62_C'].replace(-1, numpy.nan)
data['W1_E63_C']=data['W1_E63_C'].replace(-1, numpy.nan)
data['W1_E64_C']=data['W1_E64_C'].replace(-1, numpy.nan)
data['W1_M1']=data['W1_M1'].replace(-1, numpy.nan)
data['W1_M1A']=data['W1_M1A'].replace(-1, numpy.nan)
data['W1_M2']=data['W1_M2'].replace(-1, numpy.nan)
data['W1_M4']=data['W1_M4'].replace(-1, numpy.nan)
data['W1_M5']=data['W1_M5'].replace(-1, numpy.nan)
data['W1_M6']=data['W1_M6'].replace(-1, numpy.nan)
data['W1_N1B']=data['W1_N1B'].replace(998, numpy.nan)
data['W1_N1C']=data['W1_N1C'].replace(998, numpy.nan)
data['W1_N1D']=data['W1_N1D'].replace(998, numpy.nan)
data['W1_N1E']=data['W1_N1E'].replace(998, numpy.nan)
data['W1_N1F']=data['W1_N1F'].replace(998, numpy.nan)
data['W1_N1B']=data['W1_N1B'].replace(-1, numpy.nan)
data['W1_N1C']=data['W1_N1C'].replace(-1, numpy.nan)
data['W1_N1D']=data['W1_N1D'].replace(-1, numpy.nan)
data['W1_N1E']=data['W1_N1E'].replace(-1, numpy.nan)
data['W1_N1F']=data['W1_N1F'].replace(-1, numpy.nan)
data['W1_O1']=data['W1_O1'].replace(-1, numpy.nan)
data['W1_O3']=data['W1_O3'].replace(-1, numpy.nan)
data['W1_QA5D']=data['W1_QA5D'].replace(-1, numpy.nan)
data['W2_QL2B']=data['W2_QL2B'].replace(-1, numpy.nan)
data['W2_QL3']=data['W2_QL3'].replace(-1, numpy.nan)
data['W2_QL4']=data['W2_QL4'].replace(-1, numpy.nan)
#create new variables that can be added together for religiosity from current variables
recode1 = {1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:0}
data['M1'] = data['W1_M1'].map(recode1)
recode2 = {1:2, 2:0, 3:0} #yes=2pts, no=0
data['M1A'] = data['W1_M1A'].map(recode2)
data['M4'] = data['W1_M4'] / 4 #0-1pt in 0.25 increments
recode3 = {1:2.5, 2:2, 3:1.5, 4:1, 5:0.5, 6:0, 7:0}
data['M5'] = data['W1_M5'].map(recode3) #0-2.5pts based on church attentance
data['M6'] = (2 - data['W1_M6']) * 2 #yes=2pts, no=0
data['QL2'] = data['W2_QL2B'] / 4 #0-1pt in 0.25 increments
data['QL3'] = (5 - data['W2_QL3']) / 4 #0-1pt in 0.25 increments
data['QL4'] = data['W2_QL4'] / 5 #0-1pt in 0.2 increments
#create data subset for religiosity calculation
relig = data[['M1', 'M1A', 'M4', 'M5', 'M6', 'QL2', 'QL3', 'QL4']]
a = relig.head (n=10)
print ("Values for religiosity questions")
print(a)
#create new secondary variable, RELIG, the index of religiosity
data['RELIG']= relig.sum(axis=1) #axis=1 means the horizontal axis
b = data['RELIG'].head (n=10)
print ()
print ("Sum of Religiosity values for each respondent")
print (b)
data['CNT'] = relig.count(axis=1)
d = data['CNT'].head (n=10)
print ()
print ("Number of religiosity questions with valid answers")
print (d)
data['RELIND'] = relig.sum(axis=1) / relig.count(axis=1) #religious index=sum of religious variables/num of questions answered
c = data['RELIND'].head (n=10)
print ()
print ("Religious Index: sum of values / number of questions")
print (c)
#Manage acceptance variables
recode7 = {1:2, 2:1, 3:5, 4:3, 5:6}
data['ETH'] = data['PPETHM'].map(recode7) #remap ethnicity to match M2 church race
recode4 = {1:1, 2:0}
data['E4']=data['W1_E4'].map(recode4) #recode 'would you date outside your race?' to 1=Y, 0=N
recode5 = {1:0, 2:0.33, 3:0.66, 4:1}
data['E61'] = data['W1_E61_C'].map(recode5) #recode scale to 0--1 from 1--4
data['E62'] = data['W1_E62_C'].map(recode5) #concerned about biracial kids?
data['E63'] = data['W1_E63_C'].map(recode5)
data['E64'] = data['W1_E64_C'].map(recode5)
data['QA5D'] =data['W1_QA5D'].map(recode5)
data['N1B'] = data['W1_N1B'] / 100 #change 0--100 to 0.0--1.0 scale
data['N1C'] = data['W1_N1C'] / 100 #rate races
data['N1D'] = data['W1_N1D'] / 100
data['N1E'] = data['W1_N1E'] / 100
data['N1F'] = data['W1_N1F'] / 100
recode6 = {1:0, 2:0.25, 3:0.5, 4:0.75, 5:1.0}
data['O1'] = data['W1_O1'].map(recode6) #recode scale to 0--1 from 1--5
data['O3'] = data['W1_O3'].map(recode6)
#subdividing based on ethnicity
blk = data[(data['ETH'] == 1)] #black sub
wht = data[(data['ETH'] == 2)] #white sub
ltn = data[(data['ETH'] == 3)] #latino sub
oth = data[(data['ETH'] == 5)] #other sub
mix = data[(data['ETH'] == 6)] #mixed sub
def CHURAC (row): #1pt if church race != own race
if row['W1_M2'] > row['ETH']:
return 1
if row['W1_M2'] < row['ETH']:
return 1
if row['W1_M2'] == row['ETH']:
return 0
data['M2'] = data.apply (lambda row: CHURAC (row),axis=1)
#group the acceptance questions for each race (omit rating of own race)
baccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1C', 'N1D', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for blacks
waccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1C', 'N1E', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for whites
laccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1D', 'N1E', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for latinos
oaccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1C', 'N1D', 'N1E', 'O1', 'O3', 'QA5D']] #the questions for other
maccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1C', 'N1D', 'N1E', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for mixed
#add the columns for each race, but do it for all respondents
data['BACSUM'] = baccept.sum (axis=1)
data['WACSUM'] = waccept.sum (axis=1)
data['LACSUM'] = laccept.sum (axis=1)
data['OACSUM'] = oaccept.sum (axis=1)
data['MACSUM'] = maccept.sum (axis=1)
def ACCEP (row): #select the sum (from above) based on respondent's race
a = 0.0
if row['ETH'] == 1: #black
a = row['BACSUM']
if row['ETH'] == 2: #white
a = row['WACSUM']
if row['ETH'] == 3: #latino
a = row['LACSUM']
if row['ETH'] == 5: #other
a = row['OACSUM']
if row['ETH'] == 6: #mix
a = row['MACSUM']
return a
data['ACCEPT'] = data.apply (lambda row: ACCEP (row),axis=1)
a1 = data['ACCEPT'].head (n=10)
print ('acceptance')
print (a1)
#count number of valid acceptance answers for all respondents based on race
data['BACCNT'] = baccept.count(axis=1)
data['WACCNT'] = waccept.count(axis=1)
data['LACCNT'] = laccept.count(axis=1)
data['OACCNT'] = oaccept.count(axis=1)
data['MACCNT'] = maccept.count(axis=1)
def ACCNT (row): #select the count (from above) based on respondent's race
a = 0.0
if row['ETH'] == 1: #black
a = row['BACCNT']
if row['ETH'] == 2: #white
a = row['WACCNT']
if row['ETH'] == 3: #latino
a = row['LACCNT']
if row['ETH'] == 5: #other
a = row['OACCNT']
if row['ETH'] == 6: #mix
a = row['MACCNT']
return a
data['ACCNT'] = data.apply (lambda row: ACCNT (row),axis=1)
a2 = data['ACCNT'].head (n=10)
print ('# of valid answers to acceptance questions')
print (a2)
#calculate acceptance index: acceptance sum / valid question count
data['ACCIND'] = data['ACCEPT'] / data['ACCNT']
a3 = data['ACCIND'].head (n=10)
print ('Acceptance index')
print (a3)
descr = data['RELIND'].describe()
print (descr)
desca = data['ACCIND'].describe()
print (desca)
#Univariate histogram for religiosity:
hist1 = seaborn.histplot(data["RELIND"].dropna(), kde=False);
plt.xlabel('Index of Religiosity')
plt.title('Index of Religiosity')
print (hist1)
#Univariate histogram for acceptance:
hist2 = seaborn.histplot(data["ACCIND"].dropna(), kde=False);
plt.xlabel('Index of Racial Acceptance')
plt.title('Index of Racial Acceptance')
print (hist2)
#basic scatterplot: Q->Q
scat1 = seaborn.regplot(x="RELIND", y="ACCIND", fit_reg=False, data=data)
plt.xlabel('Religiosity')
plt.ylabel('Acceptance')
plt.title('Scatterplot for the Association Between Religiosity and Acceptance')
'''
data['BACCEP']= baccept.sum(axis=1) #axis=1 means the horizontal axis
b = data['BACCEP'].head (n=10)
print ()
print ("Sum of acceptance values for blacks")
print (b)
data['WACCEP']= waccept.sum(axis=1) #axis=1 means the horizontal axis
b = data['WACCEP'].head (n=10)
print ()
print ("Sum of acceptance values for whites")
print (b)
print ('number of blacks')
a1 = blk['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of whites')
a1 = wht['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of latinos')
a1 = ltn['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of other')
a1 = oth['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of mix')
a1 = mix['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('average religiosity of blacks')
b2 = blk['RELIND'].mean()
print (b2)
print ('average religiosity of whites')
b2 = wht['RELIND'].mean()
print (b2)
print ('average religiosity of latinos')
b2 = ltn['RELIND'].mean()
print (b2)
print ('average religiosity of others')
b2 = oth['RELIND'].mean()
print (b2)
print ('average religiosity of mixed')
b2 = mix['RELIND'].mean()
print (b2)
#counts and percentages (i.e. frequency distributions) for each variable
print ("counts for M1 respondent's religion, 1=religious, 0=atheist")
c1 = data['M1'].value_counts(sort=False, dropna=False)
print (c1)
print ("percentages for M1 respondent's religion, 1=religious, 0=atheist")
p1 = data['M1'].value_counts(sort=False, normalize=True)
print (p1)
print ('counts for M1A is respondent born again?, no=0, yes=2')
c2 = data['M1A'].value_counts(sort=False, dropna=False)
print (c2)
print ('percentages for M1A is respondent born again?, no=0, yes=2')
p2 = data['M1A'].value_counts(sort=False, normalize=True)
print (p2)
print ('counts for M5 how often does respondent attend church? 2.5=more than once/wk, 0=never')
c3 = data['M5'].value_counts(sort=False, dropna=False)
print (c3)
print ('percentages for M5 how often does respondent attend church? 2.5=more than once/wk, 0=never')
p3 = data['M5'].value_counts(sort=False, normalize=True)
print (p3)
'''
Analysis of Outlook on Life 2012 dataset using Python
created Tuesday, November 10, 2020
@author Joel Caren
"""
import pandas
import numpy
import seaborn
import matplotlib.pyplot as plt
data = pandas.read_csv('oolpds.csv',low_memory = False) # read data file into 'data' using pandas and fend off a memory error
print (len(data)) #number of observations (rows)
print (len(data.columns)) #number of variables (columns)
#check format of variables
data['W1_M1'].dtype
#setting variables you will be working with to numeric; errors="coerce" fixes missing data
#data['W1_C1'] = pandas.to_numeric(data['W1_C1'], errors = "coerce") #pol party? (R=1,D=2,I=3,other=4)
data['W1_D11'] = pandas.to_numeric(data['W1_D11'], errors = "coerce") #rate republicans (0-100)
data['W1_D12'] = pandas.to_numeric(data['W1_D12'], errors = "coerce") #rate dems (0-100)
data['W1_E4'] = pandas.to_numeric(data['W1_E4'], errors = "coerce") #date outside race? y=1
data['W1_E61_C'] = pandas.to_numeric(data['W1_E61_C'], errors = "coerce") #against biracial kids (black), 1=agree--4=disagree
data['W1_E62_C'] = pandas.to_numeric(data['W1_E62_C'], errors = "coerce") #against biracial kids (latino), 1=agree--4=disagree
data['W1_E63_C'] = pandas.to_numeric(data['W1_E63_C'], errors = "coerce") #against biracial kids (white), 1=agree--4=disagree
data['W1_E64_C'] = pandas.to_numeric(data['W1_E64_C'], errors = "coerce") #against biracial kids (asian), 1=agree--4=disagree
data['W1_M1'] = pandas.to_numeric(data['W1_M1'], errors = "coerce") #which religion? none=13
data['W1_M1A'] = pandas.to_numeric(data['W1_M1A'], errors = "coerce") #born again? y=1,n=2,not asked=3,refuse=4
data['W1_M2'] = pandas.to_numeric(data['W1_M2'], errors = "coerce") #church race? B=1,W=2,L=3,A=4,other=5,don't know=6
data['W1_M4'] = pandas.to_numeric(data['W1_M4'], errors = "coerce") #allow women clergy? 1=agree--4=disagree
data['W1_M5'] = pandas.to_numeric(data['W1_M5'], errors = "coerce") #how often attend church? mult times/wk=1,never=6
data['W1_M6'] = pandas.to_numeric(data['W1_M6'], errors = "coerce") #active church member? y=1
data['W1_N1B'] = pandas.to_numeric(data['W1_N1B'], errors = "coerce") #rate native americans (0-100)
data['W1_N1C'] = pandas.to_numeric(data['W1_N1C'], errors = "coerce") #rate latinos (0-100)
data['W1_N1D'] = pandas.to_numeric(data['W1_N1D'], errors = "coerce") #rate whites (0-100)
data['W1_N1E'] = pandas.to_numeric(data['W1_N1E'], errors = "coerce") #rate blacks (0-100)
data['W1_N1F'] = pandas.to_numeric(data['W1_N1F'], errors = "coerce") #rate asians (0-100)
data['W1_O1'] = pandas.to_numeric(data['W1_O1'], errors = "coerce") #blacks should teach kids vs racism, 1=no--5=yes
data['W1_O3'] = pandas.to_numeric(data['W1_O3'], errors = "coerce") #blacks should teach kids not all whites racist, 1=no--5=yes
data['W1_QA5D'] = pandas.to_numeric(data['W1_QA5D'], errors = "coerce") #blacks should segregate, 1=agree--4=disagree
data['W2_QL2B'] = pandas.to_numeric(data['W2_QL2B'], errors = "coerce") #gays as priests? 1=agree--4=disagree
data['W2_QL3'] = pandas.to_numeric(data['W2_QL3'], errors = "coerce") #homosex immoral? 1=agree--4=disagree
data['W2_QL4'] = pandas.to_numeric(data['W2_QL4'], errors = "coerce") #laws vs gay discrimination? y=1--n=5
data['PPETHM'] = pandas.to_numeric(data['PPETHM'], errors = "coerce") #your race? w=1,b=2,other=3,L=4,mix=5
# recode missing values to python missing (NaN)
#data['W1_C1']=data['W1_C1'].replace(-1, numpy.nan)
data['W1_D11']=data['W1_D11'].replace(998, numpy.nan)
data['W1_D12']=data['W1_D12'].replace(998, numpy.nan)
data['W1_E4']=data['W1_E4'].replace(-1, numpy.nan)
data['W1_E61_C']=data['W1_E61_C'].replace(-1, numpy.nan)
data['W1_E62_C']=data['W1_E62_C'].replace(-1, numpy.nan)
data['W1_E63_C']=data['W1_E63_C'].replace(-1, numpy.nan)
data['W1_E64_C']=data['W1_E64_C'].replace(-1, numpy.nan)
data['W1_M1']=data['W1_M1'].replace(-1, numpy.nan)
data['W1_M1A']=data['W1_M1A'].replace(-1, numpy.nan)
data['W1_M2']=data['W1_M2'].replace(-1, numpy.nan)
data['W1_M4']=data['W1_M4'].replace(-1, numpy.nan)
data['W1_M5']=data['W1_M5'].replace(-1, numpy.nan)
data['W1_M6']=data['W1_M6'].replace(-1, numpy.nan)
data['W1_N1B']=data['W1_N1B'].replace(998, numpy.nan)
data['W1_N1C']=data['W1_N1C'].replace(998, numpy.nan)
data['W1_N1D']=data['W1_N1D'].replace(998, numpy.nan)
data['W1_N1E']=data['W1_N1E'].replace(998, numpy.nan)
data['W1_N1F']=data['W1_N1F'].replace(998, numpy.nan)
data['W1_N1B']=data['W1_N1B'].replace(-1, numpy.nan)
data['W1_N1C']=data['W1_N1C'].replace(-1, numpy.nan)
data['W1_N1D']=data['W1_N1D'].replace(-1, numpy.nan)
data['W1_N1E']=data['W1_N1E'].replace(-1, numpy.nan)
data['W1_N1F']=data['W1_N1F'].replace(-1, numpy.nan)
data['W1_O1']=data['W1_O1'].replace(-1, numpy.nan)
data['W1_O3']=data['W1_O3'].replace(-1, numpy.nan)
data['W1_QA5D']=data['W1_QA5D'].replace(-1, numpy.nan)
data['W2_QL2B']=data['W2_QL2B'].replace(-1, numpy.nan)
data['W2_QL3']=data['W2_QL3'].replace(-1, numpy.nan)
data['W2_QL4']=data['W2_QL4'].replace(-1, numpy.nan)
#create new variables that can be added together for religiosity from current variables
recode1 = {1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1, 9:1, 10:1, 11:1, 12:1, 13:0}
data['M1'] = data['W1_M1'].map(recode1)
recode2 = {1:2, 2:0, 3:0} #yes=2pts, no=0
data['M1A'] = data['W1_M1A'].map(recode2)
data['M4'] = data['W1_M4'] / 4 #0-1pt in 0.25 increments
recode3 = {1:2.5, 2:2, 3:1.5, 4:1, 5:0.5, 6:0, 7:0}
data['M5'] = data['W1_M5'].map(recode3) #0-2.5pts based on church attentance
data['M6'] = (2 - data['W1_M6']) * 2 #yes=2pts, no=0
data['QL2'] = data['W2_QL2B'] / 4 #0-1pt in 0.25 increments
data['QL3'] = (5 - data['W2_QL3']) / 4 #0-1pt in 0.25 increments
data['QL4'] = data['W2_QL4'] / 5 #0-1pt in 0.2 increments
#create data subset for religiosity calculation
relig = data[['M1', 'M1A', 'M4', 'M5', 'M6', 'QL2', 'QL3', 'QL4']]
a = relig.head (n=10)
print ("Values for religiosity questions")
print(a)
#create new secondary variable, RELIG, the index of religiosity
data['RELIG']= relig.sum(axis=1) #axis=1 means the horizontal axis
b = data['RELIG'].head (n=10)
print ()
print ("Sum of Religiosity values for each respondent")
print (b)
data['CNT'] = relig.count(axis=1)
d = data['CNT'].head (n=10)
print ()
print ("Number of religiosity questions with valid answers")
print (d)
data['RELIND'] = relig.sum(axis=1) / relig.count(axis=1) #religious index=sum of religious variables/num of questions answered
c = data['RELIND'].head (n=10)
print ()
print ("Religious Index: sum of values / number of questions")
print (c)
#Manage acceptance variables
recode7 = {1:2, 2:1, 3:5, 4:3, 5:6}
data['ETH'] = data['PPETHM'].map(recode7) #remap ethnicity to match M2 church race
recode4 = {1:1, 2:0}
data['E4']=data['W1_E4'].map(recode4) #recode 'would you date outside your race?' to 1=Y, 0=N
recode5 = {1:0, 2:0.33, 3:0.66, 4:1}
data['E61'] = data['W1_E61_C'].map(recode5) #recode scale to 0--1 from 1--4
data['E62'] = data['W1_E62_C'].map(recode5) #concerned about biracial kids?
data['E63'] = data['W1_E63_C'].map(recode5)
data['E64'] = data['W1_E64_C'].map(recode5)
data['QA5D'] =data['W1_QA5D'].map(recode5)
data['N1B'] = data['W1_N1B'] / 100 #change 0--100 to 0.0--1.0 scale
data['N1C'] = data['W1_N1C'] / 100 #rate races
data['N1D'] = data['W1_N1D'] / 100
data['N1E'] = data['W1_N1E'] / 100
data['N1F'] = data['W1_N1F'] / 100
recode6 = {1:0, 2:0.25, 3:0.5, 4:0.75, 5:1.0}
data['O1'] = data['W1_O1'].map(recode6) #recode scale to 0--1 from 1--5
data['O3'] = data['W1_O3'].map(recode6)
#subdividing based on ethnicity
blk = data[(data['ETH'] == 1)] #black sub
wht = data[(data['ETH'] == 2)] #white sub
ltn = data[(data['ETH'] == 3)] #latino sub
oth = data[(data['ETH'] == 5)] #other sub
mix = data[(data['ETH'] == 6)] #mixed sub
def CHURAC (row): #1pt if church race != own race
if row['W1_M2'] > row['ETH']:
return 1
if row['W1_M2'] < row['ETH']:
return 1
if row['W1_M2'] == row['ETH']:
return 0
data['M2'] = data.apply (lambda row: CHURAC (row),axis=1)
#group the acceptance questions for each race (omit rating of own race)
baccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1C', 'N1D', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for blacks
waccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1C', 'N1E', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for whites
laccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1D', 'N1E', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for latinos
oaccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1C', 'N1D', 'N1E', 'O1', 'O3', 'QA5D']] #the questions for other
maccept = data[['E4', 'E61', 'E62', 'E63', 'E64', 'M2', 'N1B', 'N1C', 'N1D', 'N1E', 'N1F', 'O1', 'O3', 'QA5D']] #the questions for mixed
#add the columns for each race, but do it for all respondents
data['BACSUM'] = baccept.sum (axis=1)
data['WACSUM'] = waccept.sum (axis=1)
data['LACSUM'] = laccept.sum (axis=1)
data['OACSUM'] = oaccept.sum (axis=1)
data['MACSUM'] = maccept.sum (axis=1)
def ACCEP (row): #select the sum (from above) based on respondent's race
a = 0.0
if row['ETH'] == 1: #black
a = row['BACSUM']
if row['ETH'] == 2: #white
a = row['WACSUM']
if row['ETH'] == 3: #latino
a = row['LACSUM']
if row['ETH'] == 5: #other
a = row['OACSUM']
if row['ETH'] == 6: #mix
a = row['MACSUM']
return a
data['ACCEPT'] = data.apply (lambda row: ACCEP (row),axis=1)
a1 = data['ACCEPT'].head (n=10)
print ('acceptance')
print (a1)
#count number of valid acceptance answers for all respondents based on race
data['BACCNT'] = baccept.count(axis=1)
data['WACCNT'] = waccept.count(axis=1)
data['LACCNT'] = laccept.count(axis=1)
data['OACCNT'] = oaccept.count(axis=1)
data['MACCNT'] = maccept.count(axis=1)
def ACCNT (row): #select the count (from above) based on respondent's race
a = 0.0
if row['ETH'] == 1: #black
a = row['BACCNT']
if row['ETH'] == 2: #white
a = row['WACCNT']
if row['ETH'] == 3: #latino
a = row['LACCNT']
if row['ETH'] == 5: #other
a = row['OACCNT']
if row['ETH'] == 6: #mix
a = row['MACCNT']
return a
data['ACCNT'] = data.apply (lambda row: ACCNT (row),axis=1)
a2 = data['ACCNT'].head (n=10)
print ('# of valid answers to acceptance questions')
print (a2)
#calculate acceptance index: acceptance sum / valid question count
data['ACCIND'] = data['ACCEPT'] / data['ACCNT']
a3 = data['ACCIND'].head (n=10)
print ('Acceptance index')
print (a3)
descr = data['RELIND'].describe()
print (descr)
desca = data['ACCIND'].describe()
print (desca)
#Univariate histogram for religiosity:
hist1 = seaborn.histplot(data["RELIND"].dropna(), kde=False);
plt.xlabel('Index of Religiosity')
plt.title('Index of Religiosity')
print (hist1)
#Univariate histogram for acceptance:
hist2 = seaborn.histplot(data["ACCIND"].dropna(), kde=False);
plt.xlabel('Index of Racial Acceptance')
plt.title('Index of Racial Acceptance')
print (hist2)
#basic scatterplot: Q->Q
scat1 = seaborn.regplot(x="RELIND", y="ACCIND", fit_reg=False, data=data)
plt.xlabel('Religiosity')
plt.ylabel('Acceptance')
plt.title('Scatterplot for the Association Between Religiosity and Acceptance')
'''
data['BACCEP']= baccept.sum(axis=1) #axis=1 means the horizontal axis
b = data['BACCEP'].head (n=10)
print ()
print ("Sum of acceptance values for blacks")
print (b)
data['WACCEP']= waccept.sum(axis=1) #axis=1 means the horizontal axis
b = data['WACCEP'].head (n=10)
print ()
print ("Sum of acceptance values for whites")
print (b)
print ('number of blacks')
a1 = blk['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of whites')
a1 = wht['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of latinos')
a1 = ltn['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of other')
a1 = oth['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('number of mix')
a1 = mix['ETH'].value_counts(sort=False, dropna=False)
print (a1)
print ('average religiosity of blacks')
b2 = blk['RELIND'].mean()
print (b2)
print ('average religiosity of whites')
b2 = wht['RELIND'].mean()
print (b2)
print ('average religiosity of latinos')
b2 = ltn['RELIND'].mean()
print (b2)
print ('average religiosity of others')
b2 = oth['RELIND'].mean()
print (b2)
print ('average religiosity of mixed')
b2 = mix['RELIND'].mean()
print (b2)
#counts and percentages (i.e. frequency distributions) for each variable
print ("counts for M1 respondent's religion, 1=religious, 0=atheist")
c1 = data['M1'].value_counts(sort=False, dropna=False)
print (c1)
print ("percentages for M1 respondent's religion, 1=religious, 0=atheist")
p1 = data['M1'].value_counts(sort=False, normalize=True)
print (p1)
print ('counts for M1A is respondent born again?, no=0, yes=2')
c2 = data['M1A'].value_counts(sort=False, dropna=False)
print (c2)
print ('percentages for M1A is respondent born again?, no=0, yes=2')
p2 = data['M1A'].value_counts(sort=False, normalize=True)
print (p2)
print ('counts for M5 how often does respondent attend church? 2.5=more than once/wk, 0=never')
c3 = data['M5'].value_counts(sort=False, dropna=False)
print (c3)
print ('percentages for M5 how often does respondent attend church? 2.5=more than once/wk, 0=never')
p3 = data['M5'].value_counts(sort=False, normalize=True)
print (p3)
'''
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