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dhruvnandan1-blog · 3 years

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Visualizations using graphs

Python Code:

# -*- coding: utf-8 -*-import pandas as pd

import numpy as np import seaborn as sb import matplotlib.pyplot as plt # load gapminder dataset data = pd.read_csv(‘gapminder.csv’,low_memory=False) # lower-case all DataFrame column names data.columns = map(str.lower, data.columns) # bug fix for display formats to avoid run time errors pd.set_option('display.float_format’, lambda x:’%f’%x) # setting variables to be numeric data['suicideper100th’] = data['suicideper100th’].convert_objects(convert_numeric=True) data['breastcancerper100th’] = data['breastcancerper100th’].convert_objects(convert_numeric=True) data['hivrate’] = data['hivrate’].convert_objects(convert_numeric=True) data['employrate’] = data['employrate’].convert_objects(convert_numeric=True) # display summary statistics about the data # print(“Statistics for a Suicide Rate”) # print(data['suicideper100th’].describe()) # subset data for a high suicide rate based on summary statistics sub = data[(data['suicideper100th’]>12)] #make a copy of my new subsetted data sub_copy = sub.copy() # Univariate graph for breast cancer rate for people with a high suicide rate plt.figure(1) sb.distplot(sub_copy[“breastcancerper100th”].dropna(),kde=False) plt.xlabel('Breast Cancer Rate’) plt.ylabel('Frequency’) plt.title('Breast Cancer Rate for People with a High Suicide Rate’) # Univariate graph for hiv rate for people with a high suicide rate plt.figure(2) sb.distplot(sub_copy[“hivrate”].dropna(),kde=False) plt.xlabel('HIV Rate’) plt.ylabel('Frequency’) plt.title('HIV Rate for People with a High Suicide Rate’) # Univariate graph for employment rate for people with a high suicide rate plt.figure(3) sb.distplot(sub_copy[“employrate”].dropna(),kde=False) plt.xlabel('Employment Rate’) plt.ylabel('Frequency’) plt.title('Employment Rate for People with a High Suicide Rate’) # Bivariate graph for association of breast cancer rate with HIV rate for people with a high suicide rate plt.figure(4) sb.regplot(x=“hivrate”,y=“breastcancerper100th”,fit_reg=False,data=sub_copy) plt.xlabel('HIV Rate’) plt.ylabel('Breast Cancer Rate’) plt.title('Breast Cancer Rate vs. HIV Rate for People with a High Suicide Rate’)

Output

This graph is unimodal, with its highest pick at 0-20% of breast cancer rate. It seems to be skewed to the right as there are higher frequencies in lower categories than the higher categories.

This graph is unimodal, with its highest pick at 0-1% of HIV rate. It seems to be skewed to the right as there are higher frequencies in lower categories than the higher categories.

This graph is unimodal, with its highest pick at the median of 55-60% employment rate. It seems to be a symmetric distribution as there are lower frequencies in lower and higher categories.

This graph plots the breast cancer rate vs. HIV rate for people with a high suicide rate. It shows that people with breast cancer are not infected with HIV.

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dhruvnandan1-blog · 3 years

Text

Data management

Python Code

# -*- coding: utf-8 -*-

import pandas as pd import numpy

# load gapminder dataset data = pd.read_csv(‘gapminder.csv’,low_memory=False) # lower-case all DataFrame column names data.columns = map(str.lower, data.columns) # bug fix for display formats to avoid run time errors pd.set_option('display.float_format’, lambda x:’%f’%x)

# setting variables to be numeric data['suicideper100th’] = data['suicideper100th’].convert_objects(convert_numeric=True) data['breastcancerper100th’] = data['breastcancerper100th’].convert_objects(convert_numeric=True) data['hivrate’] = data['hivrate’].convert_objects(convert_numeric=True) data['employrate’] = data['employrate’].convert_objects(convert_numeric=True)

# display summary statistics about the data print(“Statistics for a Suicide Rate”) print(data['suicideper100th’].describe())

# subset data for a high suicide rate based on summary statistics sub = data[(data['suicideper100th’]>12)] #make a copy of my new subsetted data sub_copy = sub.copy()

# BREAST CANCER RATE # frequency and percentage distritions for a number of breast cancer cases with a high suicide rate # include the count of missing data and group the variables in 4 groups by number of # breast cancer cases (1-23, 24-46, 47-69, 70-92) bc_max=sub_copy['breastcancerper100th’].max() # maximum of breast cancer cases # group the data in 4 groups by number of breast cancer cases and record it into new variable bcgroup4 sub_copy['bcgroup4’]=pd.cut(sub_copy.breastcancerper100th,[0*bc_max,0.25*bc_max,0.5*bc_max,0.75*bc_max,1*bc_max]) # frequency for 4 groups of breast cancer cases with a high suicide rate bc=sub_copy['bcgroup4’].value_counts(sort=False,dropna=False) # percentage for 4 groups of breast cancer cases with a high suicide rate pbc=sub_copy['bcgroup4’].value_counts(sort=False,dropna=False,normalize=True)*100

# cumulative frequency and cumulative percentage for 4 groups of breast cancer cases with a high suicide rate bc1=[] # Cumulative Frequency pbc1=[] # Cumulative Percentage cf=0 cp=0 for freq in bc: cf=cf+freq bc1.append(cf) pf=cf*100/len(sub_copy) pbc1.append(pf)

print('Number of Breast Cancer Cases with a High Suicide Rate’) fmt1 = ’%10s %9s %9s %12s %13s’ fmt2 = ’%9s %9.d %10.2f %9.d %13.2f’ print(fmt1 % (’# of Cases’,'Freq.’,'Percent’,'Cum. Freq.’,'Cum. Percent’)) for i, (key, var1, var2, var3, var4) in enumerate(zip(bc.keys(),bc,pbc,bc1,pbc1)): print(fmt2 % (key, var1, var2, var3, var4))

# HIV RATE # frequency and percentage distritions for HIV rate with a high suicide rate # include the count of missing data and group the variables in 4 groups by quartile function # group the data in 4 groups and record it into new variable hcgroup4 sub_copy['hcgroup4’]=pd.qcut(sub_copy.hivrate,4,labels=[“0% tile”,“25% tile”,“50% tile”,“75% tile”]) # frequency for 4 groups of HIV rate with a high suicide rate hc = sub_copy['hcgroup4’].value_counts(sort=False,dropna=False) # percentage for 4 groups of HIV rate with a high suicide rate phc = sub_copy['hcgroup4’].value_counts(sort=False,dropna=False,normalize=True)*100

# cumulative frequency and cumulative percentage for 4 groups of HIV rate with a high suicide rate hc1=[] # Cumulative Frequency phc1=[] # Cumulative Percentage cf=0 cp=0 for freq in hc: cf=cf+freq hc1.append(cf) pf=cf*100/len(sub_copy) phc1.append(pf)

print('HIV Rate with a High Suicide Rate’) print(fmt1 % ('Rate’,'Freq.’,'Percent’,'Cum. Freq.’,'Cum. Percent’)) for i, (key, var1, var2, var3, var4) in enumerate(zip(hc.keys(),hc,phc,hc1,phc1)): print(fmt2 % (key, var1, var2, var3, var4))

# EMPLOYMENT RATE # frequency and percentage distritions for employment rate with a high suicide rate # include the count of missing data and group the variables in 5 groups by # group the data in 5 groups and record it into new variable ecgroup4 def ecgroup4 (row): if row['employrate’] >= 32 and row['employrate’] < 51: return 1 elif row['employrate’] >= 51 and row['employrate’] < 59: return 2 elif row['employrate’] >= 59 and row['employrate’] < 65: return 3 elif row['employrate’] >= 65 and row['employrate’] < 84: return 4 else: return 5 # record for NAN values

sub_copy['ecgroup4’] = sub_copy.apply(lambda row: ecgroup4 (row), axis=1)

# frequency for 5 groups of employment rate with a high suicide rate ec = sub_copy['ecgroup4’].value_counts(sort=False,dropna=False) # percentage for 5 groups of employment rate with a high suicide rate pec = sub_copy['ecgroup4’].value_counts(sort=False,dropna=False,normalize=True)*100

# cumulative frequency and cumulative percentage for 5 groups of employment rate with a high suicide rate ec1=[] # Cumulative Frequency pec1=[] # Cumulative Percentage cf=0 cp=0 for freq in ec: cf=cf+freq ec1.append(cf) pf=cf*100/len(sub_copy) pec1.append(pf)

print('Employment Rate with a High Suicide Rate’) print(fmt1 % ('Rate’,'Freq.’,'Percent’,'Cum. Freq.’,'Cum. Percent’)) for i, (key, var1, var2, var3, var4) in enumerate(zip(ec.keys(),ec,pec,ec1,pec1)): print(fmt2 % (key, var1, var2, var3, var4))

Output with Frequency Tables at High Suicide Rate for Breast Cancer Rate, HIV Rate and Employment Rate Variables Statistics for a Suicide Rate count 191.000000 mean 9.640839 std 6.300178 min 0.201449 25% 4.988449 50% 8.262893 75% 12.328551 max 35.752872 Number of Breast Cancer Cases with a High Suicide Rate # of Cases Freq. Percent Cum. Freq. Cum. Percent (1, 23] 18 33.96 18 33.96 (23, 46] 15 28.30 33 62.26 (46, 69] 10 18.87 43 81.13 (69, 92] 8 15.09 51 96.23 nan 2 3.77 53 100.00 HIV Rate with a High Suicide Rate Rate Freq. Percent Cum. Freq. Cum. Percent 0% tile 18 33.96 18 33.96 25% tile 8 15.09 26 49.06 50% tile 11 20.75 37 69.81 75% tile 12 22.64 49 92.45 nan 4 7.55 53 100.00 Employment Rate with a High Suicide Rate Rate Freq. Percent Cum. Freq. Cum. Percent 1 10 18.87 10 18.87 2 24 45.28 34 64.15 3 5 9.43 39 73.58 4 13 24.53 52 98.11 5 1 1.89 53 100.00 Summary of Frequency Distributions I grouped the breast cancer rate, HIV rate and employment rate variables to create three new variables: bcgroup4, hcgroup4 and ecgroup4 using three different methods in Python. The grouped data also includes the count for missing data. 1) For the breast cancer rate, I grouped the data into 4 groups by number of breast cancer cases (1-23, 24-46, 47-69, 70-92) using pandas.cut function. People with lower breast cancer rate experience a high suicide rate. 2) For the HIV rate, I grouped the data into 4 groups by quartile pandas.qcut function. People with lower HIV rate experience a high suicide rate. 3) For the employment rate, I grouped the data into 5 categorical groups using def and apply functions: (1:32-50, 2:51-58, 3:59-64, 4:65-83, 5:NAN). The employment rate is between 51%-58% for people with a high suicide rate.

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dhruvnandan1-blog · 3 years

Text

Breast Cancer and Suicide Rates

Python Program

import pandas as pd import numpy as np

# display summary statistics about the data print(“Statistics for a Suicide Rate”) print(data['suicideper100th’].describe())

# subset data for a high suicide rate based on summary statistics sub = data[(data['suicideper100th’]>12)] #make a copy of my new subsetted data sub_copy = sub.copy()

# BREAST CANCER RATE # frequency and percentage distritions for a number of breast cancer cases with a high suicide rate #print('frequency for a number of breast cancer cases with a high suicide rate’) bc = sub_copy['breastcancerper100th’].value_counts(sort=False,bins=10) #print(bc)

#print('percentage for a number of breast cancer cases with a high suicide rate’) pbc = sub_copy['breastcancerper100th’].value_counts(sort=False,bins=10,normalize=True)*100 #print(pbc)

# cumulative frequency and cumulative percentage for a number of breast cancer cases with a high suicide rate bc1=[] # Cumulative Frequency pbc1=[] # Cumulative Percentage cf=0 cp=0 for freq in bc: cf=cf+freq bc1.append(cf) pf=cf*100/len(sub_copy) pbc1.append(pf) #print('cumulative frequency for a number of breast cancer cases with a high suicide rate’) #print(bc1) #print('cumulative percentage for a number of breast cancer cases with a high suicide rate’) #print(pbc1)

print('Number of Breast Cancer Cases with a High Suicide Rate’) fmt1 = ’%s %7s %9s %12s %12s’ fmt2 = ’%5.2f %10.d %10.2f %10.d %12.2f’ print(fmt1 % (’# of Cases’,'Freq.’,'Percent’,'Cum. Freq.’,'Cum. Percent’)) for i, (key, var1, var2, var3, var4) in enumerate(zip(bc.keys(),bc,pbc,bc1,pbc1)): print(fmt2 % (key, var1, var2, var3, var4)) fmt3 = ’%5s %10s %10s %10s %12s’ print(fmt3 % ('NA’, '2’, '3.77’, '53’, '100.00’))

# HIV RATE # frequency and percentage distritions for HIV rate with a high suicide rate #print('frequency for HIV rate with a high suicide rate’) hc = sub_copy['hivrate’].value_counts(sort=False,bins=7) #print(hc) #print('percentage for HIV rate with a high suicide rate’) phc = sub_copy['hivrate’].value_counts(sort=False,bins=7,normalize=True)*100 #print(phc) # cumulative frequency and cumulative percentage for HIV rate with a high suicide rate hc1=[] # Cumulative Frequency phc1=[] # Cumulative Percentage cf=0 cp=0 for freq in bc: cf=cf+freq hc1.append(cf) pf=cf*100/len(sub_copy) phc1.append(pf) #print('cumulative frequency for HIV rate with a high suicide rate’) #print(hc1) #print('cumulative percentage for HIV rate with a high suicide rate’) #print(phc1) print('HIV Rate with a High Suicide Rate’) fmt1 = ’%5s %12s %9s %12s %12s’ fmt2 = ’%5.2f %10.d %10.2f %10.d %12.2f’ print(fmt1 % ('Rate’,'Freq.’,'Percent’,'Cum. Freq.’,'Cum. Percent’)) for i, (key, var1, var2, var3, var4) in enumerate(zip(hc.keys(),hc,phc,hc1,phc1)): print(fmt2 % (key, var1, var2, var3, var4)) fmt3 = ’%5s %10s %10s %10s %12s’ print(fmt3 % ('NA’, '2’, '3.77’, '53’, '100.00’)) # EMPLOYMENT RATE # frequency and percentage distritions for employment rate with a high suicide rate #print('frequency for employment rate with a high suicide rate’) ec = sub_copy['employrate’].value_counts(sort=False,bins=10) #print(ec) #print('percentage for employment rate with a high suicide rate’) pec = sub_copy['employrate’].value_counts(sort=False,bins=10,normalize=True)*100 #print(pec) # cumulative frequency and cumulative percentage for employment rate with a high suicide rate ec1=[] # Cumulative Frequency pec1=[] # Cumulative Percentage cf=0 cp=0 for freq in bc: cf=cf+freq ec1.append(cf) pf=cf*100/len(sub_copy) pec1.append(pf) #print('cumulative frequency for employment rate with a high suicide rate’) #print(ec1) #print('cumulative percentage for employment rate with a high suicide rate’) #print(pec1) print('Employment Rate with a High Suicide Rate’) fmt1 = ’%5s %12s %9s %12s %12s’ fmt2 = ’%5.2f %10.d %10.2f %10.d %12.2f’ print(fmt1 % ('Rate’,'Freq.’,'Percent’,'Cum. Freq.’,'Cum. Percent’)) for i, (key, var1, var2, var3, var4) in enumerate(zip(ec.keys(),ec,pec,ec1,pec1)): print(fmt2 % (key, var1, var2, var3, var4)) fmt3 = ’%5s %10s %10s %10s %12s’ print(fmt3 % ('NA’, '2’, '3.77’, '53’, '100.00’))

Output with Frequency Tables at High Suicide Rate for Breast Cancer Rate.

Statistics for a Suicide Rate count 191.000000 mean 9.640839 std 6.300178 min 0.201449 25% 4.988449 50% 8.262893 75% 12.328551 max 35.752872 Number of Breast Cancer Cases with a High Suicide Rate # of Cases Freq. Percent Cum. Freq. Cum. Percent 6.51 6 11.32 6 11.32 15.14 14 26.42 20 37.74 23.68 5 9.43 25 47.17 32.22 7 13.21 32 60.38 40.76 2 3.77 34 64.15 49.30 4 7.55 38 71.70 57.84 5 9.43 43 81.13 66.38 1 1.89 44 83.02 74.92 3 5.66 47 88.68 83.46 4 7.55 51 96.23 NA 2 3.77 53 100.00

HIV Rate with a High Suicide Rate Rate Freq. Percent Cum. Freq. Cum. Percent 0.03 39 73.58 6 11.32 2.64 4 7.55 20 37.74 5.23 2 3.77 25 47.17 7.81 0 0.00 32 60.38 10.40 0 0.00 34 64.15 12.98 2 3.77 38 71.70 15.56 1 1.89 43 81.13 18.15 0 0.00 44 83.02 20.73 0 0.00 47 88.68 23.32 1 1.89 51 96.23 NA 2 3.77 53 100.00 Employment Rate with a High Suicide Rate Rate Freq. Percent Cum. Freq. Cum. Percent 37.35 2 3.77 6 11.32 41.98 2 3.77 20 37.74 46.56 7 13.21 25 47.17 51.14 8 15.09 32 60.38 55.72 16 30.19 34 64.15 60.30 4 7.55 38 71.70 64.88 5 9.43 43 81.13 69.46 2 3.77 44 83.02 74.04 3 5.66 47 88.68 78.62 3 5.66 51 96.23 NA 2 3.77 53 100.00

Summary of Frequency Distributions Question 1: What is a number of breast cancer cases associated with a high suicide rate? Answer: The high suicide rate is associated with the low number of breast cancer cases.

Question 2: How HIV rate is associated with a high suicide rate?

The high suicide rate is associated with the low HIV rate. Question 3: How employment rate is associated with a high suicide rate? The high suicide rate occurs at 55% of employment rate.

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