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RUNNING A RANDOM FOREST

The main drawback to a decision tree is that the tree is highly specific to the dataset it was built on; if you bring in new data to try and predict outcomes, you may not find the same high correlations that your decision tree featured. One method to overcome this is with a random forest. Instead of building one tree from your whole dataset, you subset the data randomly and build a number of trees. Each tree will be different, but the relationships between your variables will tend to appear consistently. In general though, because decision trees are intrinsically connected to the specific data they were built with, decision trees are better as a tool to analyze trends within a known dataset than to create a model for predicting the outcomes of future data.

With those caveats, I decided to build a random forest using the same data as from my previous post, that is, a response variable of internet use rate and explanatory variables of income per person, employment rate, female employment rate, and polity score, from the GapMinder dataset.

Load the data, convert the variables to numeric, convert the response variable to binary, and remove NA values.

In [3]:''' Code for Peer-graded Assignments: Running a Random Forest Course: Data Management and Visualization Specialization: Data Analysis and Interpretation ''' import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cross_validation import train_test_split import sklearn.metrics from sklearn.ensemble import ExtraTreesClassifier data = pd.read_csv('c:/users/greg/desktop/gapminder.csv', low_memory=False) data['internetuserate'] = pd.to_numeric(data['internetuserate'], errors='coerce') data['incomeperperson'] = pd.to_numeric(data['incomeperperson'], errors='coerce') data['employrate'] = pd.to_numeric(data['employrate'], errors='coerce') data['femaleemployrate'] = pd.to_numeric(data['femaleemployrate'], errors='coerce') data['polityscore'] = pd.to_numeric(data['polityscore'], errors='coerce') binarydata = data.copy() # convert response variable to binarydef internetgrp (row): if row['internetuserate'] < data['internetuserate'].median(): return 0 else: return 1 binarydata['internetuserate'] = binarydata.apply (lambda row: internetgrp (row),axis=1) # Clean the dataset binarydata_clean = binarydata.dropna()

Build the model from the training set

In [10]:predictors = binarydata_clean[['incomeperperson','employrate','femaleemployrate','polityscore']] targets = binarydata_clean.internetuserate pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4) from sklearn.ensemble import RandomForestClassifier classifier_r=RandomForestClassifier(n_estimators=25) classifier_r=classifier_r.fit(pred_train,tar_train) predictions_r=classifier_r.predict(pred_test)

Print the confusion matrix

In [11]:sklearn.metrics.confusion_matrix(tar_test,predictions_r)

Out[11]:array([[22, 5], [10, 24]])

Print the accuracy score

In [12]:sklearn.metrics.accuracy_score(tar_test, predictions_r)

Out[12]:0.75409836065573765

Fit an Extra Trees model to the data

In [13]:model_r = ExtraTreesClassifier() model_r.fit(pred_train,tar_train)

Out[13]:ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)

Display the Relative Importances of Each Attribute

In [15]:model_r.feature_importances_

Out[15]:array([ 0.44072852, 0.12553198, 0.1665162 , 0.2672233 ])

Run a different number of trees and see the effect of that on the accuracy of the prediction

In [16]:trees=range(50) accuracy=np.zeros(50) for idx in range(len(trees)): classifier_r=RandomForestClassifier(n_estimators=idx + 1) classifier_r=classifier_r.fit(pred_train,tar_train) predictions_r=classifier_r.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions_r) plt.cla() plt.plot(trees, accuracy) plt.ylabel('Accuracy Score') plt.xlabel('Number of Trees') plt.show()

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The confusion matrix and accuracy score are similar to that of my previous post (remember, a decision tree is pseudo-randomly created, so results will be similar, but not identical, when run with the same dataset). Examining the relative importance of each attribute is interesting here. As expected, income per person is the most highly correlated with internet use rate, at 54% of the model’s predictive capability. Employment rate (15%) and female employment rate (11%) are less correlated, again, as expected. But polity score, at 20% of the model’s predictive capability, stood out to me because none of the previous models I’ve examined with this dataset have had polity score even near the same level of importance as employment rates. Interesting. Finally, the graph shows that as the number of trees in the forest grows, the accuracy of the model does as well, but only up to about 20 trees. After that, the accuracy stops increasing and instead fluctuates with the random permutations of the subsets of data that were used to create the trees.

More from @chidujs

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machine learning week1

For the next few posts, I’ll be exploring machine learning techniques to help analyze the GapMinder data. To begin, I’ll create a classification tree to explore the relationship between my response variable, internet user rate, and my explanatory variables, income per person, employment rate, female employment rate, and polity score. The technique requires a binary, categorical response variable, so for the purpose of this demonstration I have binned internet use rate into two categories, High usage and Low usage, split by the median data point.

Load the data and convert the variables to numeric

In [1]:''' Code for Peer-graded Assignments: Running a Classification Tree Course: Data Management and Visualization Specialization: Data Analysis and Interpretation ''' import pandas as pd from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier import sklearn.metrics data = pd.read_csv('c:/users/greg/desktop/gapminder.csv', low_memory=False) data['internetuserate'] = pd.to_numeric(data['internetuserate'], errors='coerce') data['incomeperperson'] = pd.to_numeric(data['incomeperperson'], errors='coerce') data['employrate'] = pd.to_numeric(data['employrate'], errors='coerce') data['femaleemployrate'] = pd.to_numeric(data['femaleemployrate'], errors='coerce') data['polityscore'] = pd.to_numeric(data['polityscore'], errors='coerce')

Convert the response variable to binary

In [3]:binarydata = data.copy() def internetgrp (row): if row['internetuserate'] < data['internetuserate'].median(): return 0 else: return 1 binarydata['internetuserate'] = binarydata.apply (lambda row: internetgrp (row),axis=1)

Clean the data by discarding NA values

In [4]:binarydata_clean = binarydata.dropna() binarydata_clean.dtypes binarydata_clean.describe()

Out[4]:incomeperpersonfemaleemployrateinternetuseratepolityscoreemployratecount152.000000152.000000152.000000152.000000152.000000mean6706.55697848.0684210.4539473.86184259.212500std9823.59231514.8268570.4995216.24558110.363802min103.77585712.4000000.000000-10.00000034.90000225%560.79715839.5499990.000000-2.00000051.92499950%2225.93101948.5499990.0000007.00000058.90000275%6905.28766256.0500001.0000009.00000065.000000max39972.35276883.3000031.00000010.00000083.199997

Split into training and testing sets

In [7]:predictors = binarydata_clean[['incomeperperson','employrate','femaleemployrate','polityscore']] targets = binarydata_clean.internetuserate pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4) print ('Training sample') print (pred_train.shape) print ('') print ('Testing sample') print (pred_test.shape) print ('') print ('Training sample') print (tar_train.shape) print ('') print ('Testing sample') print (tar_test.shape) Training sample (91, 4) Testing sample (61, 4) Training sample (91,) Testing sample (61,)

Build model on the training data

In [8]:classifier=DecisionTreeClassifier() classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test)

Display the confusion matrix

In [10]:sklearn.metrics.confusion_matrix(tar_test,predictions)

Out[10]:array([[22, 9], [ 8, 22]])

Display the accuracy score

In [11]:sklearn.metrics.accuracy_score(tar_test, predictions)

Out[11]:0.72131147540983609

Display the decision tree

In [13]:from sklearn import tree from io import StringIO from IPython.display import Image out = StringIO() tree.export_graphviz(classifier, out_file=out) import pydotplus graph=pydotplus.graph_from_dot_data(out.getvalue()) Image(graph.create_png())

Out[13]:

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The decision tree analysis was performed to test non-linear relationships among the explanatory variables and a single binary, categorical response variable. The training sample has 91 rows of data and 4 explanatory variables; the testing sample has 61 rows of data, and the same 4 explanatory variables. The decision tree results in 27 true negatives and 16 true positives; and 11 false negatives and 7 false positives. The accuracy score is 70.5%, meaning that the model accurately predicted 70.5% of the internet use rates per country.

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THE GAPMINDER data

Sample

I am using the GapMinder dataset to investigate the relationship between internet usage in a country and that country’s GDP, overall employment rate, female employment rate, and its “polity score”, which is a measure of a country’s democratic and free nature. The sample contains data on a country-level for 215 regions (the 192 U.N. countries, with Serbia and Montenegro aggregated into one, as well as 24 other non-country regions, such as Monaco for instance). The study population is these 215 countries and regions and my sample data is the same; ie, the population is small enough that no sample is necessary to make the data collecting and processing more manageable.

Procedure

The data has been collected by the non-profit venture GapMinder from a handful of sources, including the Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank. In the case of each data collection organization, data was collected from detailed surveys of the country’s population (such as in a national census) and based mainly upon 2010 data. Employment rate data comes from 2007 and polity score from 2009. Polity score is calculated by subtracting the autocracy score from the democracy score from the Polity IV project’s research. GapMinder’s goal in collecting this data is to help world leaders and their citizens to better understand the forces shaping the geopolitical landscape around the globe.

Measures

My response variable is the internet use rate and my explanatory variables are income per person, employment rate, female employment rate, and polity score. Internet use rate, employment rate, and female employment rate are scaled as percentages of the country’s population. Income per person is simply Gross Domestic Product per capita (the country’s total, country-wide income divided by the population). Polity score is a single measure applied to the whole country. The internet use rate of a country was collected by the World Bank in their World Development Indicators. Income per person is simply the 2010 Gross Domestic Product per capita in constant 2000 USD. The inflation, but not the differences in the cost of living between countries, has been taken into account (this can lead to the seemingly odd case of a having negative income per person, when that country already has very low income relative to the United States plus high inflation, relative to the United States). Both employment rate and female employment rate have been provided by the International Labour Organization. Finally, the polity score has been calculated by the Polity IV project.

I have gone through the data and removed entries where data is missing, when necessary, and sometimes have aggregated data into bins, for histograms, for instance, but otherwise have not modified the data in any way. Deeper data management was unnecessary for the analysis.

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Logistic Regression Model

import numpy

import pandas

import statsmodels.api as sm

import seaborn

import statsmodels.formula.api as smf

# bug fix for display formats to avoid run time errorspandas.set_option('display.float_format', lambda x:'%.2f'%x)nesarc = pandas.read_csv ('nesarc_pds.csv' , low_memory=False)

#Set PANDAS to show all columns in DataFramepandas.set_option('display.max_columns', None)

#Set PANDAS to show all rows in DataFramepandas.set_option('display.max_rows', None) nesarc.columns = map(str.upper , nesarc.columns)

# Change my variables to numeric nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce')nesarc['MARP12ABDEP'] = pandas.to_numeric(nesarc['MARP12ABDEP'], errors='coerce') # Cannabis abuse/dependencenesarc['COCP12ABDEP'] = pandas.to_numeric(nesarc['COCP12ABDEP'], errors='coerce') # Cocaine abuse/dependencenesarc['ALCABDEPP12DX'] = pandas.to_numeric(nesarc['ALCABDEPP12DX'], errors='coerce') # Alcohol abuse/dependencenesarc['HERP12ABDEP'] = pandas.to_numeric(nesarc['HERP12ABDEP'], errors='coerce')

# Heroin abuse/dependencenesarc['MAJORDEP12'] = pandas.to_numeric(nesarc['MAJORDEP12'], errors='coerce')

# Major depression # Subset my sample: ages 18-30 sub1=nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30)] ############################################################################### LOGISTIC REGRESSION############################################################################## # Binary cannabis abuse/dependence prior to the last 12 months def CANDEPPR12 (x1): if x1['MARP12ABDEP']==1 or x1['MARP12ABDEP']==2 or x1['MARP12ABDEP']==3: return 1 else: return 0sub1['CANDEPPR12'] = sub1.apply (lambda x1: CANDEPPR12 (x1), axis=1)print (pandas.crosstab(sub1['MARP12ABDEP'], sub1['CANDEPPR12'])) ## Logistic regression with cannabis abuse/dependence (explanatory) - major depression (response) logreg1 = smf.logit(formula = 'MAJORDEP12 ~ CANDEPPR12', data = sub1).fit()print (logreg1.summary())# odds ratiosprint ("Odds Ratios")print (numpy.exp(logreg1.params))

# Odd ratios with 95% confidence intervals params = logreg1.paramsconf = logreg1.conf_int()conf['OR'] = paramsconf.columns = ['Lower CI', 'Upper CI', 'OR']print (numpy.exp(conf))

# Binary cocaine abuse/dependence prior to the last 12 months def COCDEPPR12 (x2): if x2['COCP12ABDEP']==1 or x2['COCP12ABDEP']==2 or x2['COCP12ABDEP']==3: return 1 else: return 0sub1['COCDEPPR12'] = sub1.apply (lambda x2: COCDEPPR12 (x2), axis=1)print (pandas.crosstab(sub1['COCP12ABDEP'], sub1['COCDEPPR12']))

## Logistic regression with cannabis and cocaine abuse/depndence (explanatory) - major depression (response) logreg2 = smf.logit(formula = 'MAJORDEP12 ~ CANDEPPR12 + COCDEPPR12', data = sub1).fit()print (logreg2.summary()) # Odd ratios with 95% confidence intervals params = logreg2.paramsconf = logreg2.conf_int()conf['OR'] = paramsconf.columns = ['Lower CI', 'Upper CI', 'OR']print (numpy.exp(conf))

# Binary alcohol abuse/dependence prior to the last 12 months def ALCDEPPR12 (x2): if x2['ALCABDEPP12DX']==1 or x2['ALCABDEPP12DX']==2 or x2['ALCABDEPP12DX']==3: return 1 else: return 0sub1['ALCDEPPR12'] = sub1.apply (lambda x2: ALCDEPPR12 (x2), axis=1)print (pandas.crosstab(sub1['ALCABDEPP12DX'], sub1['ALCDEPPR12']))

# Binary sedative abuse/dependence prior to the last 12 months def HERDEPPR12 (x3): if x3['HERP12ABDEP']==1 or x3['HERP12ABDEP']==2 or x3['HERP12ABDEP']==3: return 1 else: return 0sub1['HERDEPPR12'] = sub1.apply (lambda x3: HERDEPPR12 (x3), axis=1)print (pandas.crosstab(sub1['HERP12ABDEP'], sub1['HERDEPPR12']))

## Logistic regression with alcohol abuse/depndence (explanatory) - major depression (response) logreg3 = smf.logit(formula = 'MAJORDEP12 ~ HERDEPPR12', data = sub1).fit()print (logreg3.summary()) # Odd ratios with 95% confidence intervals print ("Odds Ratios")params = logreg3.paramsconf = logreg3.conf_int()conf['OR'] = paramsconf.columns = ['Lower CI', 'Upper CI', 'OR']print (numpy.exp(conf))

## Logistic regression with cannabis and alcohol abuse/depndence (explanatory) - major depression (response) logreg4 = smf.logit(formula = 'MAJORDEP12 ~ CANDEPPR12 + ALCDEPPR12 + COCDEPPR12', data = sub1).fit()print (logreg4.summary()) # Odd ratios with 95% confidence intervals print ("Odds Ratios")params = logreg4.paramsconf = logreg4.conf_int()conf['OR'] = paramsconf.columns = ['Lower CI', 'Upper CI', 'OR']print (numpy.exp(conf))

result:

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Multiple Regression Model

import numpy

import pandas

import statsmodels.api as sm

import seaborn

import statsmodels.formula.api as smf

import matplotlib.pyplot as plt

# bug fix for display formats to avoid run time errorspandas.set_option('display.float_format', lambda x:'%.2f'%x)nesarc = pandas.read_csv ('nesarc_pds.csv' , low_memory=False)

#Set PANDAS to show all columns in DataFramepandas.set_option('display.max_columns', None)#Set PANDAS to show all rows in DataFramepandas.set_option('display.max_rows', None) nesarc.columns = map(str.upper , nesarc.columns) # Change my variables to numeric nesarc['IDNUM'] =pandas.to_numeric(nesarc['IDNUM'], errors='coerce')nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce')nesarc['MAJORDEP12'] = pandas.to_numeric(nesarc['MAJORDEP12'], errors='coerce') # Major depressionnesarc['AGE'] =pandas.to_numeric(nesarc['AGE'], errors='coerce')nesarc['SEX'] = pandas.to_numeric(nesarc['SEX'], errors='coerce')nesarc['S3BD5Q2E'] = pandas.to_numeric(nesarc['S3BD5Q2E'], errors='coerce') # Cannabis use frequencynesarc['S3BQ4'] = pandas.to_numeric(nesarc['S3BQ4'], errors='coerce') # Quantity of joints per daynesarc['GENAXDX12'] = pandas.to_numeric(nesarc['GENAXDX12'], errors='coerce') # General anxietynesarc['S3BD5Q2F'] = pandas.to_numeric(nesarc['S3BD5Q2F'], errors='coerce') # Age when began using cannabis the mostnesarc['DYSDX12'] = pandas.to_numeric(nesarc['DYSDX12'], errors='coerce')

# Dysthymianesarc['SOCPDX12'] = pandas.to_numeric(nesarc['SOCPDX12'], errors='coerce') # Social phobianesarc['S3BD5Q2GR'] = pandas.to_numeric(nesarc['S3BD5Q2GR'], errors='coerce') # Cannabis use duration (weeks)nesarc['S3CD5Q15C'] = pandas.to_numeric(nesarc['S3CD5Q15C'], errors='coerce') # Cannabis dependencenesarc['S3CD5Q13B'] = pandas.to_numeric(nesarc['S3CD5Q13B'], errors='coerce')

# Cannabis abuse # Current cannabis abuse criterianesarc['S3CD5Q14C9'] = pandas.to_numeric(nesarc['S3CD5Q14C9'], errors='coerce')nesarc['S3CQ14A8'] = pandas.to_numeric(nesarc['S3CQ14A8'], errors='coerce') # Longer period cannabis abuse criterianesarc['S3CD5Q14C3'] = pandas.to_numeric(nesarc['S3CD5Q14C3'], errors='coerce') # Depressed because of cannabis effects wearing offnesarc['S3CD5Q14C6C'] = pandas.to_numeric(nesarc['S3CD5Q14C6C'], errors='coerce') # Sleep difficulties because of cannabis effects wearing offnesarc['S3CD5Q14C6R'] = pandas.to_numeric(nesarc['S3CD5Q14C6R'], errors='coerce')

# Eat more because of cannabis effects wearing offnesarc['S3CD5Q14C6H'] = pandas.to_numeric(nesarc['S3CD5Q14C6H'], errors='coerce') # Feel nervous or anxious because of cannabis effects wearing offnesarc['S3CD5Q14C6I'] = pandas.to_numeric(nesarc['S3CD5Q14C6I'], errors='coerce')

# Fast heart beat because of cannabis effects wearing offnesarc['S3CD5Q14C6D'] = pandas.to_numeric(nesarc['S3CD5Q14C6D'], errors='coerce') # Feel weak or tired because of cannabis effects wearing offnesarc['S3CD5Q14C6B'] = pandas.to_numeric(nesarc['S3CD5Q14C6B'], errors='coerce')

# Withdrawal symptomsnesarc['S3CD5Q14C6U'] = pandas.to_numeric(nesarc['S3CD5Q14C6U'], errors='coerce')

# Subset my sample: Cannabis users, ages 18-30 sub1=nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)] ###############

Cannabis abuse/dependence criteria in the last 12 months (response variable) ############### #

Current cannabis abuse/dependence criteria #1 DSM-IV def crit1 (row): if row['S3CD5Q14C9']==1 or row['S3CQ14A8'] == 1 : return 1 elif row['S3CD5Q14C9']==2 and row['S3CQ14A8']==2 : return 0sub1['crit1'] = sub1.apply (lambda row: crit1 (row),axis=1) # Current 6 cannabis abuse/dependence sub-symptoms criteria #2 DSM-IV # Recode for summing (from 1,2 to 0,1)recode1 = {1: 1, 2: 0}sub1['S3CD5Q14C6C']=sub1['S3CD5Q14C6C'].replace(9, numpy.nan)sub1['S3CD5Q14C6C']= sub1['S3CD5Q14C6C'].map(recode1)sub1['S3CD5Q14C6R']=sub1['S3CD5Q14C6R'].replace(9, numpy.nan)sub1['S3CD5Q14C6R']= sub1['S3CD5Q14C6R'].map(recode1)sub1['S3CD5Q14C6H']=sub1['S3CD5Q14C6H'].replace(9, numpy.nan)sub1['S3CD5Q14C6H']= sub1['S3CD5Q14C6H'].map(recode1)sub1['S3CD5Q14C6I']=sub1['S3CD5Q14C6I'].replace(9, numpy.nan)sub1['S3CD5Q14C6I']= sub1['S3CD5Q14C6I'].map(recode1)sub1['S3CD5Q14C6D']=sub1['S3CD5Q14C6D'].replace(9, numpy.nan)sub1['S3CD5Q14C6D']= sub1['S3CD5Q14C6D'].map(recode1)sub1['S3CD5Q14C6B']=sub1['S3CD5Q14C6B'].replace(9, numpy.nan)sub1['S3CD5Q14C6B']= sub1['S3CD5Q14C6B'].map(recode1)

# Sum symptomssub1['CWITHDR_COUNT'] = numpy.nansum([sub1['S3CD5Q14C6C'], sub1['S3CD5Q14C6R'], sub1['S3CD5Q14C6H'], sub1['S3CD5Q14C6I'], sub1['S3CD5Q14C6D'], sub1['S3CD5Q14C6B']], axis=0) # Sum code checkchksum=sub1[['IDNUM','S3CD5Q14C6C', 'S3CD5Q14C6R', 'S3CD5Q14C6H', 'S3CD5Q14C6I', 'S3CD5Q14C6D', 'S3CD5Q14C6B', 'CWITHDR_COUNT']]chksum.head(n=50)

# Withdrawal symptoms in the last 12 months (yes/no)def crit2 (row): if row['CWITHDR_COUNT']>=3 or row['S3CD5Q14C6U']==1: return 1 elif row['CWITHDR_COUNT']<3 and row['S3CD5Q14C6U']!=1: return 0sub1['crit2'] = sub1.apply (lambda row: crit2 (row),axis=1) # Longer period cannabis abuse/dependence criteria #3 DSM-IV sub1['S3CD5Q14C3']=sub1['S3CD5Q14C3'].replace(9, numpy.nan)sub1['S3CD5Q14C3']= sub1['S3CD5Q14C3'].map(recode1)

# Current cannabis use cut down criteria #4 DSM-IV sub1['S3CD5Q14C2'] = pandas.to_numeric(sub1['S3CD5Q14C2'], errors='coerce') # Without successsub1['S3CD5Q14C1'] = pandas.to_numeric(sub1['S3CD5Q14C1'], errors='coerce') # More than oncedef crit4 (row): if row['S3CD5Q14C2']==1 or row['S3CD5Q14C1'] == 1 : return 1 elif row['S3CD5Q14C2']==2 and row['S3CD5Q14C1']==2 : return 0sub1['crit4'] = sub1.apply (lambda row: crit4 (row),axis=1)chk1e = sub1['crit4'].value_counts(sort=False, dropna=False)

# Current reduce of important/pleasurable activities criteria #5 DSM-IV sub1['S3CD5Q14C10'] = pandas.to_numeric(sub1['S3CD5Q14C10'], errors='coerce')sub1['S3CD5Q14C11'] = pandas.to_numeric(sub1['S3CD5Q14C11'], errors='coerce')def crit5 (row): if row['S3CD5Q14C10']==1 or row['S3CD5Q14C11'] == 1 : return 1 elif row['S3CD5Q14C10']==2 and row['S3CD5Q14C11']==2 : return 0sub1['crit5'] = sub1.apply (lambda row: crit5 (row),axis=1)chk1g = sub1['crit5'].value_counts(sort=False, dropna=False) # Current cannbis use continuation despite knowledge of physical or psychological problem criteria

#6 DSM-IV sub1['S3CD5Q14C13'] = pandas.to_numeric(sub1['S3CD5Q14C13'], errors='coerce')sub1['S3CD5Q14C12'] = pandas.to_numeric(sub1['S3CD5Q14C12'], errors='coerce')def crit6 (row): if row['S3CD5Q14C13']==1 or row['S3CD5Q14C12'] == 1 : return 1 elif row['S3CD5Q14C13']==2 and row['S3CD5Q14C12']==2 : return 0sub1['crit6'] = sub1.apply (lambda row: crit6 (row),axis=1)chk1h = sub1['crit6'].value_counts(sort=False, dropna=False)

# Cannabis abuse/dependence symptoms sum sub1['CanDepSymptoms'] = numpy.nansum([sub1['crit1'], sub1['crit2'], sub1['S3CD5Q14C3'], sub1['crit4'], sub1['crit5'], sub1['crit6']], axis=0 )chk2 = sub1['CanDepSymptoms'].value_counts(sort=False, dropna=False) ############################################################################### MULTIPLE REGRESSION & CONFIDENCE INTERVALS

############################################################################## sub2 = sub1[['S3BQ4', 'S3BD5Q2F', 'DYSDX12', 'MAJORDEP12', 'CanDepSymptoms', 'SOCPDX12', 'GENAXDX12', 'S3BD5Q2GR']].dropna()

# Centre the quantity of joints smoked per day and age when they began using cannabis, quantitative variablessub1['numberjosmoked_c'] = (sub1['S3BQ4'] - sub1['S3BQ4'].mean())sub1['agebeganuse_c'] = (sub1['S3BD5Q2F'] - sub1['S3BD5Q2F'].mean())sub1['canuseduration_c'] = (sub1['S3BD5Q2GR'] - sub1['S3BD5Q2GR'].mean()) # Linear regression analysis print('OLS regression model for the association between major depression diagnosis and cannabis depndence symptoms')reg1 = smf.ols('CanDepSymptoms ~ MAJORDEP12', data=sub1).fit()print (reg1.summary()) print('OLS regression model for the association of majord depression diagnosis and smoking quantity with cannabis dependence symptoms')reg2 = smf.ols('CanDepSymptoms ~ MAJORDEP12 + DYSDX12', data=sub1).fit()print (reg2.summary()) reg3 = smf.ols('CanDepSymptoms ~ MAJORDEP12 + agebeganuse_c + numberjosmoked_c + canuseduration_c + GENAXDX12 + DYSDX12 + SOCPDX12', data=sub1).fit()print (reg3.summary())

##################################################################################### POLYNOMIAL REGRESSION

#################################################################################### #

First order (linear) scatterplotscat1 = seaborn.regplot(x="S3BQ4", y="CanDepSymptoms", scatter=True, data=sub1)plt.ylim(0, 6)plt.xlabel('Quantity of joints')plt.ylabel('Cannabis dependence symptoms') # Fit second order polynomialscat1 = seaborn.regplot(x="S3BQ4", y="CanDepSymptoms", scatter=True, order=2, data=sub1)plt.ylim(0, 6)plt.xlabel('Quantity of joints')plt.ylabel('Cannabis dependence symptoms') # Linear regression analysisreg4 = smf.ols('CanDepSymptoms ~ numberjosmoked_c', data=sub1).fit()print (reg4.summary()) reg5 = smf.ols('CanDepSymptoms ~ numberjosmoked_c + I(numberjosmoked_c**2)',

data=sub1).fit()print (reg5.summary()) ##################################################################################### EVALUATING MODEL FIT

####################################################################################

recode1 = {1: 10, 2: 9, 3: 8, 4: 7, 5: 6, 6: 5, 7: 4, 8: 3, 9: 2, 10: 1} # Dictionary with details of frequency variable reverse-recodesub1['CUFREQ'] = sub1['S3BD5Q2E'].map(recode1) # Change variable name from S3BD5Q2E to CUFREQ sub1['CUFREQ_c'] = (sub1['CUFREQ'] - sub1['CUFREQ'].mean()) # Adding frequency of cannabis usereg6 = smf.ols('CanDepSymptoms ~ numberjosmoked_c + I(numberjosmoked_c**2) + CUFREQ_c', data=sub1).fit()print (reg6.summary()) # Q-Q plot for normalityfig1=sm.qqplot(reg6.resid, line='r')print (fig1)

# Simple plot of residualsstdres=pandas.DataFrame(reg6.resid_pearson)fig2=plt.plot(stdres, 'o', ls='None')l = plt.axhline(y=0, color='r')plt.ylabel('Standardized Residual')plt.xlabel('Observation Number') # Additional regression diagnostic plotsfig3 = plt.figure(figsize=(12,8))fig3 = sm.graphics.plot_regress_exog(reg6, "CUFREQ_c", fig=fig3) # leverage plotfig4 = plt.figure(figsize=(36,24))fig4=sm.graphics.influence_plot(reg6, size=2)print(fig4)

OUTPUT:

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BASIC REGRESSION MODEL

import numpy

import pandas

import statsmodels.api as sm

import seaborn

import statsmodels.formula.api as smf

import matplotlib.pyplot as plt

# bug fix for display formats to avoid run time errorspandas.set_option('display.float_format', lambda x:'%.2f'%x) nesarc = pandas.read_csv ('nesarc_pds.csv' , low_memory=False) #Set PANDAS to show all columns in DataFramepandas.set_option('display.max_columns', None)#Set PANDAS to show all rows in DataFramepandas.set_option('display.max_rows', None) nesarc.columns = map(str.upper , nesarc.columns) # Change my variables to numeric nesarc['IDNUM'] =pandas.to_numeric(nesarc['IDNUM'], errors='coerce')nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce')nesarc['MAJORDEP12'] = pandas.to_numeric(nesarc['MAJORDEP12'], errors='coerce')nesarc['AGE'] =pandas.to_numeric(nesarc['AGE'], errors='coerce')nesarc['SEX'] = pandas.to_numeric(nesarc['SEX'], errors='coerce')nesarc['S3BD5Q2E'] = pandas.to_numeric(nesarc['S3BD5Q2E'], errors='coerce') # Current cannabis abuse criterianesarc['S3CD5Q14C9'] = pandas.to_numeric(nesarc['S3CD5Q14C9'], errors='coerce')nesarc['S3CQ14A8'] = pandas.to_numeric(nesarc['S3CQ14A8'], errors='coerce') # Longer period cannabis abuse criterianesarc['S3CD5Q14C3'] = pandas.to_numeric(nesarc['S3CD5Q14C3'], errors='coerce') # Depressed because of cannabis effects wearing offnesarc['S3CD5Q14C6C'] = pandas.to_numeric(nesarc['S3CD5Q14C6C'], errors='coerce') # Sleep difficulties because of cannabis effects wearing offnesarc['S3CD5Q14C6R'] = pandas.to_numeric(nesarc['S3CD5Q14C6R'], errors='coerce') # Eat more because of cannabis effects wearing offnesarc['S3CD5Q14C6H'] = pandas.to_numeric(nesarc['S3CD5Q14C6H'], errors='coerce') # Feel nervous or anxious because of cannabis effects wearing offnesarc['S3CD5Q14C6I'] = pandas.to_numeric(nesarc['S3CD5Q14C6I'], errors='coerce') # Fast heart beat because of cannabis effects wearing offnesarc['S3CD5Q14C6D'] = pandas.to_numeric(nesarc['S3CD5Q14C6D'], errors='coerce') # Feel weak or tired because of cannabis effects wearing offnesarc['S3CD5Q14C6B'] = pandas.to_numeric(nesarc['S3CD5Q14C6B'], errors='coerce') # Withdrawal symptomsnesarc['S3CD5Q14C6U'] = pandas.to_numeric(nesarc['S3CD5Q14C6U'], errors='coerce') # Subset my sample: Cannabis users, ages 18-30 sub1=nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)] (pandas.crosstab(sub1['S3CD5Q14C9'], sub1['S3CQ14A8'])) c1 = sub1['S3CD5Q14C6U'].value_counts(sort=False, dropna=False)print (c1) # Current 6 cannabis abuse/dependence sub-symptoms criteria #2 DSM-IV # Recode for summing (from 1,2 to 0,1)recode1 = {1: 1, 2: 0}sub1['S3CD5Q14C6C']=sub1['S3CD5Q14C6C'].replace(9, numpy.nan)sub1['S3CD5Q14C6C']= sub1['S3CD5Q14C6C'].map(recode1)sub1['S3CD5Q14C6R']=sub1['S3CD5Q14C6R'].replace(9, numpy.nan)sub1['S3CD5Q14C6R']= sub1['S3CD5Q14C6R'].map(recode1)sub1['S3CD5Q14C6H']=sub1['S3CD5Q14C6H'].replace(9, numpy.nan)sub1['S3CD5Q14C6H']= sub1['S3CD5Q14C6H'].map(recode1)sub1['S3CD5Q14C6I']=sub1['S3CD5Q14C6I'].replace(9, numpy.nan)sub1['S3CD5Q14C6I']= sub1['S3CD5Q14C6I'].map(recode1)sub1['S3CD5Q14C6D']=sub1['S3CD5Q14C6D'].replace(9, numpy.nan)sub1['S3CD5Q14C6D']= sub1['S3CD5Q14C6D'].map(recode1)sub1['S3CD5Q14C6B']=sub1['S3CD5Q14C6B'].replace(9, numpy.nan)sub1['S3CD5Q14C6B']= sub1['S3CD5Q14C6B'].map(recode1) # Check recodechk1c = sub1['S3CD5Q14C6U'].value_counts(sort=False, dropna=False)print (chk1c) # Sum symptomssub1['CWITHDR_COUNT'] = numpy.nansum([sub1['S3CD5Q14C6C'], sub1['S3CD5Q14C6R'], sub1['S3CD5Q14C6H'], sub1['S3CD5Q14C6I'], sub1['S3CD5Q14C6D'], sub1['S3CD5Q14C6B']], axis=0) # Sum code checkchksum=sub1[['IDNUM','S3CD5Q14C6C', 'S3CD5Q14C6R', 'S3CD5Q14C6H', 'S3CD5Q14C6I', 'S3CD5Q14C6D', 'S3CD5Q14C6B', 'CWITHDR_COUNT']]chksum.head(n=50) chk1d = sub1['CWITHDR_COUNT'].value_counts(sort=False, dropna=False)print (chk1d)

# Withdrawal symptoms in the last 12 months (yes/no)def crit2 (row): if row['CWITHDR_COUNT']>=3 or row['S3CD5Q14C6U']==1: return 1 elif row['CWITHDR_COUNT']<3 and row['S3CD5Q14C6U']!=1: return 0sub1['crit2'] = sub1.apply (lambda row: crit2 (row),axis=1)print (pandas.crosstab(sub1['CWITHDR_COUNT'], sub1['crit2'])) # Longer period cannabis abuse/dependence criteria #3 DSM-IV sub1['S3CD5Q14C3']=sub1['S3CD5Q14C3'].replace(9, numpy.nan)sub1['S3CD5Q14C3']= sub1['S3CD5Q14C3'].map(recode1) chk1d = sub1['S3CD5Q14C3'].value_counts(sort=False, dropna=False)print (chk1d) # Current cannabis use cut down criteria #4 DSM-IV sub1['S3CD5Q14C2'] = pandas.to_numeric(sub1['S3CD5Q14C2'], errors='coerce') # Without successsub1['S3CD5Q14C1'] = pandas.to_numeric(sub1['S3CD5Q14C1'], errors='coerce') # More than oncedef crit4 (row): if row['S3CD5Q14C2']==1 or row['S3CD5Q14C1'] == 1 : return 1 elif row['S3CD5Q14C2']==2 and row['S3CD5Q14C1']==2 : return 0sub1['crit4'] = sub1.apply (lambda row: crit4 (row),axis=1)chk1e = sub1['crit4'].value_counts(sort=False, dropna=False)print (chk1e) # Current reduce of important/pleasurable activities criteria

#5 DSM-IV sub1['S3CD5Q14C10'] = pandas.to_numeric(sub1['S3CD5Q14C10'], errors='coerce')sub1['S3CD5Q14C11'] = pandas.to_numeric(sub1['S3CD5Q14C11'], errors='coerce')def crit5 (row): if row['S3CD5Q14C10']==1 or row['S3CD5Q14C11'] == 1 : return 1 elif row['S3CD5Q14C10']==2 and row['S3CD5Q14C11']==2 : return 0sub1['crit5'] = sub1.apply (lambda row: crit5 (row),axis=1)chk1g = sub1['crit5'].value_counts(sort=False, dropna=False)print (chk1g) # Current cannbis use continuation despite knowledge of physical or psychological problem criteria #6 DSM-IV sub1['S3CD5Q14C13'] = pandas.to_numeric(sub1['S3CD5Q14C13'], errors='coerce')sub1['S3CD5Q14C12'] = pandas.to_numeric(sub1['S3CD5Q14C12'], errors='coerce')def crit6 (row): if row['S3CD5Q14C13']==1 or row['S3CD5Q14C12'] == 1 : return 1 elif row['S3CD5Q14C13']==2 and row['S3CD5Q14C12']==2 : return 0sub1['crit6'] = sub1.apply (lambda row: crit6 (row),axis=1)chk1h = sub1['crit6'].value_counts(sort=False, dropna=False)print (chk1h) # Cannabis abuse/dependence symptoms sum sub1['CanDepSymptoms'] = numpy.nansum([sub1['crit1'], sub1['crit2'], sub1['S3CD5Q14C3'], sub1['crit4'], sub1['crit5'], sub1['crit6']], axis=0 )

chk2 = sub1['CanDepSymptoms'].value_counts(sort=False, dropna=False)

print (chk2) c1 = sub1["MAJORDEP12"].value_counts(sort=False, dropna=False)print(c1)c2 = sub1["AGE"].value_counts(sort=False, dropna=False)print(c2)

############### Major depression diagnosis in the last 12 months (explanatory variable) ############### # Major depression diagnosis print('OLS regression model for the association between major depression diagnosis and cannabis depndence symptoms')reg1 = smf.ols('CanDepSymptoms ~ MAJORDEP12', data=sub1).fit()print (reg1.summary()) # Listwise deletion for calculating means for regression model observations sub1 = sub1[['CanDepSymptoms', 'MAJORDEP12']].dropna() # Group means & sd print ("Mean")ds1 = sub1.groupby('MAJORDEP12').mean()print (ds1)print ("Standard deviation")ds2 = sub1.groupby('MAJORDEP12').std()print (ds2) # Bivariate bar graph print('Bivariate bar graph for major depression diagnosis and cannabis depndence symptoms')

seaborn.factorplot(x="MAJORDEP12", y="CanDepSymptoms", data=sub1, kind="bar", ci=None)plt.xlabel('Major Depression Diagnosis')plt.ylabel('Mean Number of Cannabis Dependence Symptoms')

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writing about your data assignment

Sample

I am using the GapMinder dataset to investigate the relationship between internet usage in a country and that country’s GDP, overall employment rate, female employment rate, and its “polity score”, which is a measure of a country’s democratic and free nature. The sample contains data on a country-level for 215 regions (the 192 U.N. countries, with Serbia and Montenegro aggregated into one, as well as 24 other non-country regions, such as Monaco for instance). The study population is these 215 countries and regions and my sample data is the same; ie, the population is small enough that no sample is necessary to make the data collecting and processing more manageable.

Procedure

The data has been collected by the non-profit venture GapMinder from a handful of sources, including the Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank. In the case of each data collection organization, data was collected from detailed surveys of the country’s population (such as in a national census) and based mainly upon 2010 data. Employment rate data comes from 2007 and polity score from 2009. Polity score is calculated by subtracting the autocracy score from the democracy score from the Polity IV project’s research. GapMinder’s goal in collecting this data is to help world leaders and their citizens to better understand the forces shaping the geopolitical landscape around the globe.

Measures

My response variable is the internet use rate and my explanatory variables are income per person, employment rate, female employment rate, and polity score. Internet use rate, employment rate, and female employment rate are scaled as percentages of the country’s population. Income per person is simply Gross Domestic Product per capita (the country’s total, country-wide income divided by the population). Polity score is a single measure applied to the whole country. The internet use rate of a country was collected by the World Bank in their World Development Indicators. Income per person is simply the 2010 Gross Domestic Product per capita in constant 2000 USD. The inflation, but not the differences in the cost of living between countries, has been taken into account (this can lead to the seemingly odd case of a having negative income per person, when that country already has very low income relative to the United States plus high inflation, relative to the United States). Both employment rate and female employment rate have been provided by the International Labour Organization. Finally, the polity score has been calculated by the Polity IV project.

I have gone through the data and removed entries where data is missing, when necessary, and sometimes have aggregated data into bins, for histograms, for instance, but otherwise have not modified the data in any way.Deeper data management was unnecessary for the analysis.

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Assignment.

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-- coding: utf-8 --

""" Created on Sun Mar 17 18:11:22 2019 @author: Voltas """ import pandas import numpy import seaborn import scipy import matplotlib.pyplot as plt

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

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)

nesarc.columns = map(str.upper , nesarc.columns)

pandas.set_option('display.float_format' , lambda x:'%f'%x)

Change my variables to numeric

nesarc['AGE'] = nesarc['AGE'].convert_objects(convert_numeric=True) nesarc['MAJORDEP12'] = nesarc['MAJORDEP12'].convert_objects(convert_numeric=True) nesarc['S1Q231'] = nesarc['S1Q231'].convert_objects(convert_numeric=True) nesarc['S3BQ1A5'] = nesarc['S3BQ1A5'].convert_objects(convert_numeric=True) nesarc['S3BD5Q2E'] = nesarc['S3BD5Q2E'].convert_objects(convert_numeric=True)

Subset my sample

subset1 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & nesarc['S3BQ1A5']==1] # Ages 18-30, cannabis users subsetc1 = subset1.copy()

Setting missing data

subsetc1['S1Q231']=subsetc1['S1Q231'].replace(9, numpy.nan) subsetc1['S3BQ1A5']=subsetc1['S3BQ1A5'].replace(9, numpy.nan) subsetc1['S3BD5Q2E']=subsetc1['S3BD5Q2E'].replace(99, numpy.nan) subsetc1['S3BD5Q2E']=subsetc1['S3BD5Q2E'].replace('BL', numpy.nan) recode1 = {1: 9, 2: 8, 3: 7, 4: 6, 5: 5, 6: 4, 7: 3, 8: 2, 9: 1} # Frequency of cannabis use variable reverse-recode subsetc1['CUFREQ'] = subsetc1['S3BD5Q2E'].map(recode1) # Change the variable name from S3BD5Q2E to CUFREQ

subsetc1['CUFREQ'] = subsetc1['CUFREQ'].astype('category')

Raname graph labels for better interpetation

subsetc1['CUFREQ'] = subsetc1['CUFREQ'].cat.rename_categories(["2 times/year","3-6 times/year","7-11 times/year","Once a month","2-3 times/month","1-2 times/week","3-4 times/week","Nearly every day","Every day"])

Contingency table of observed counts of major depression diagnosis (response variable) within frequency of cannabis use groups (explanatory variable), in ages 18-30

contab1 = pandas.crosstab(subsetc1['MAJORDEP12'], subsetc1['CUFREQ']) print (contab1)

Column percentages

colsum=contab1.sum(axis=0) colpcontab=contab1/colsum print(colpcontab)

Chi-square calculations for major depression within frequency of cannabis use groups

print ('Chi-square value, p value, expected counts, for major depression within cannabis use status') chsq1= scipy.stats.chi2_contingency(contab1) print (chsq1)

Bivariate bar graph for major depression percentages with each cannabis smoking frequency group

plt.figure(figsize=(12,4)) # Change plot size ax1 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=subsetc1, kind="bar", ci=None) ax1.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.show()

recode2 = {1: 10, 2: 9, 3: 8, 4: 7, 5: 6, 6: 5, 7: 4, 8: 3, 9: 2, 10: 1} # Frequency of cannabis use variable reverse-recode subsetc1['CUFREQ2'] = subsetc1['S3BD5Q2E'].map(recode2) # Change the variable name from S3BD5Q2E to CUFREQ2

sub1=subsetc1[(subsetc1['S1Q231']== 1)] sub2=subsetc1[(subsetc1['S1Q231']== 2)]

print ('Association between cannabis use status and major depression for those who lost a family member or a close friend in the last 12 months') contab2=pandas.crosstab(sub1['MAJORDEP12'], sub1['CUFREQ2']) print (contab2)

Column percentages

colsum2=contab2.sum(axis=0) colpcontab2=contab2/colsum2 print(colpcontab2)

Chi-square

print ('Chi-square value, p value, expected counts') chsq2= scipy.stats.chi2_contingency(contab2) print (chsq2)

Line graph for major depression percentages within each frequency group, for those who lost a family member or a close friend

plt.figure(figsize=(12,4)) # Change plot size ax2 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=sub1, kind="point", ci=None) ax2.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.title('Association between cannabis use status and major depression for those who lost a family member or a close friend in the last 12 months') plt.show()

#

print ('Association between cannabis use status and major depression for those who did NOT lose a family member or a close friend in the last 12 months') contab3=pandas.crosstab(sub2['MAJORDEP12'], sub2['CUFREQ2']) print (contab3)

Column percentages

colsum3=contab3.sum(axis=0) colpcontab3=contab3/colsum3 print(colpcontab3)

Chi-square

print ('Chi-square value, p value, expected counts') chsq3= scipy.stats.chi2_contingency(contab3) print (chsq3)

Line graph for major depression percentages within each frequency group, for those who did NOT lose a family member or a close friend

plt.figure(figsize=(12,4)) # Change plot size ax3 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=sub2, kind="point", ci=None) ax3.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.title('Association between cannabis use status and major depression for those who did NOT lose a family member or a close friend in the last 12 months') plt.show()

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Assignment3

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-- coding: utf-8 --

""" Created on Thu Mar 7 15:00:39 2019 @author: Voltas """

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

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

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) nesarc.columns = map(str.upper , nesarc.columns)

pandas.set_option('display.float_format' , lambda x:'%f'%x)

Change my variables to numeric

nesarc['AGE'] = pandas.to_numeric(nesarc['AGE'], errors='coerce') nesarc['S3BQ4'] = pandas.to_numeric(nesarc['S3BQ4'], errors='coerce') nesarc['S4AQ6A'] = pandas.to_numeric(nesarc['S4AQ6A'], errors='coerce') nesarc['S3BD5Q2F'] = pandas.to_numeric(nesarc['S3BD5Q2F'], errors='coerce') nesarc['S9Q6A'] = pandas.to_numeric(nesarc['S9Q6A'], errors='coerce') nesarc['S4AQ7'] = pandas.to_numeric(nesarc['S4AQ7'], errors='coerce') nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce')

Subset my sample

subset1 = nesarc[(nesarc['S3BQ1A5']==1)] # Cannabis users subsetc1 = subset1.copy()

Setting missing data

subsetc1['S3BQ1A5']=subsetc1['S3BQ1A5'].replace(9, numpy.nan) subsetc1['S3BD5Q2F']=subsetc1['S3BD5Q2F'].replace('BL', numpy.nan) subsetc1['S3BD5Q2F']=subsetc1['S3BD5Q2F'].replace(99, numpy.nan) subsetc1['S4AQ6A']=subsetc1['S4AQ6A'].replace('BL', numpy.nan) subsetc1['S4AQ6A']=subsetc1['S4AQ6A'].replace(99, numpy.nan) subsetc1['S9Q6A']=subsetc1['S9Q6A'].replace('BL', numpy.nan) subsetc1['S9Q6A']=subsetc1['S9Q6A'].replace(99, numpy.nan)

Scatterplot for the age when began using cannabis the most and the age of first episode of major depression

plt.figure(figsize=(12,4)) # Change plot size scat1 = seaborn.regplot(x="S3BD5Q2F", y="S4AQ6A", fit_reg=True, data=subset1) plt.xlabel('Age when began using cannabis the most') plt.ylabel('Age when expirenced the first episode of major depression') plt.title('Scatterplot for the age when began using cannabis the most and the age of first the episode of major depression') plt.show()

data_clean=subset1.dropna()

Pearson correlation coefficient for the age when began using cannabis the most and the age of first the episode of major depression

print ('Association between the age when began using cannabis the most and the age of the first episode of major depression') print (scipy.stats.pearsonr(data_clean['S3BD5Q2F'], data_clean['S4AQ6A']))

Scatterplot for the age when began using cannabis the most and the age of the first episode of general anxiety

plt.figure(figsize=(12,4)) # Change plot size scat2 = seaborn.regplot(x="S3BD5Q2F", y="S9Q6A", fit_reg=True, data=subset1) plt.xlabel('Age when began using cannabis the most') plt.ylabel('Age when expirenced the first episode of general anxiety') plt.title('Scatterplot for the age when began using cannabis the most and the age of the first episode of general anxiety') plt.show()

Pearson correlation coefficient for the age when began using cannabis the most and the age of the first episode of general anxiety

print ('Association between the age when began using cannabis the most and the age of first the episode of general anxiety') print (scipy.stats.pearsonr(data_clean['S3BD5Q2F'], data_clean['S9Q6A']))

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ASSIGNMENT

-- coding: utf-8 --

""" Created on Fri Mar 1 17:20:15 2019 @author: Voltas """

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

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

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)

nesarc.columns = map(str.upper , nesarc.columns)

pandas.set_option('display.float_format' , lambda x:'%f'%x)

Change my variables to numeric

nesarc['AGE'] = pandas.to_numeric(nesarc['AGE'], errors='coerce') nesarc['S3BQ4'] = pandas.to_numeric(nesarc['S3BQ4'], errors='coerce') nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce') nesarc['S3BD5Q2B'] = pandas.to_numeric(nesarc['S3BD5Q2B'], errors='coerce') nesarc['S3BD5Q2E'] = pandas.to_numeric(nesarc['S3BD5Q2E'], errors='coerce') nesarc['MAJORDEP12'] = pandas.to_numeric(nesarc['MAJORDEP12'], errors='coerce') nesarc['GENAXDX12'] = pandas.to_numeric(nesarc['GENAXDX12'], errors='coerce')

Subset my sample

subset1 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30)] # Ages 18-30 subsetc1 = subset1.copy()

subset2 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)] # Cannabis users, ages 18-30 subsetc2 = subset2.copy()

Setting missing data for frequency and cannabis use, variables S3BD5Q2E, S3BQ1A5

subsetc1['S3BQ1A5']=subsetc1['S3BQ1A5'].replace(9, numpy.nan) subsetc2['S3BD5Q2E']=subsetc2['S3BD5Q2E'].replace('BL', numpy.nan) subsetc2['S3BD5Q2E']=subsetc2['S3BD5Q2E'].replace(99, numpy.nan)

Contingency table of observed counts of major depression diagnosis (response variable) within cannabis use (explanatory variable), in ages 18-30

contab1=pandas.crosstab(subsetc1['MAJORDEP12'], subsetc1['S3BQ1A5']) print (contab1)

Column percentages

colsum=contab1.sum(axis=0) colpcontab=contab1/colsum print(colpcontab)

Chi-square calculations for major depression within cannabis use status

print ('Chi-square value, p value, expected counts, for major depression within cannabis use status') chsq1= scipy.stats.chi2_contingency(contab1) print (chsq1)

Contingency table of observed counts of geberal anxiety diagnosis (response variable) within cannabis use (explanatory variable), in ages 18-30

contab2=pandas.crosstab(subsetc1['GENAXDX12'], subsetc1['S3BQ1A5']) print (contab2)

Column percentages

colsum2=contab2.sum(axis=0) colpcontab2=contab2/colsum2 print(colpcontab2)

Chi-square calculations for general anxiety within cannabis use status

print ('Chi-square value, p value, expected counts, for general anxiety within cannabis use status') chsq2= scipy.stats.chi2_contingency(contab2) print (chsq2)

#

Contingency table of observed counts of major depression diagnosis (response variable) within frequency of cannabis use (10 level explanatory variable), in ages 18-30

contab3=pandas.crosstab(subset2['MAJORDEP12'], subset2['S3BD5Q2E']) print (contab3)

Column percentages

colsum3=contab3.sum(axis=0) colpcontab3=contab3/colsum3 print(colpcontab3)

Chi-square calculations for mahor depression within frequency of cannabis use groups

print ('Chi-square value, p value, expected counts for major depression associated frequency of cannabis use') chsq3= scipy.stats.chi2_contingency(contab3) print (chsq3)

recode1 = {1: 9, 2: 8, 3: 7, 4: 6, 5: 5, 6: 4, 7: 3, 8: 2, 9: 1} # Dictionary with details of frequency variable reverse-recode subsetc2['CUFREQ'] = subsetc2['S3BD5Q2E'].map(recode1) # Change variable name from S3BD5Q2E to CUFREQ

subsetc2["CUFREQ"] = subsetc2["CUFREQ"].astype('category')

Rename graph labels for better interpretation

subsetc2['CUFREQ'] = subsetc2['CUFREQ'].cat.rename_categories(["2 times/year","3-6 times/year","7-11 times/years","Once a month","2-3 times/month","1-2 times/week","3-4 times/week","Nearly every day","Every day"])

Graph percentages of major depression within each cannabis smoking frequency group

plt.figure(figsize=(12,4)) # Change plot size ax1 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=subsetc2, kind="bar", ci=None) ax1.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.show()

Post hoc test, pair comparison of frequency groups 1 and 9, 'Every day' and '2 times a year'

recode2 = {1: 1, 9: 9} subsetc2['COMP1v9']= subsetc2['S3BD5Q2E'].map(recode2)

Contingency table of observed counts

ct4=pandas.crosstab(subsetc2['MAJORDEP12'], subsetc2['COMP1v9']) print (ct4)

Column percentages

colsum4=ct4.sum(axis=0) colpcontab4=ct4/colsum4 print(colpcontab4)

Chi-square calculations for pair comparison of frequency groups 1 and 9, 'Every day' and '2 times a year'

print ('Chi-square value, p value, expected counts, for pair comparison of frequency groups -Every day- and -2 times a year-') cs4= scipy.stats.chi2_contingency(ct4) print (cs4)

Post hoc test, pair comparison of frequency groups 2 and 6, 'Nearly every day' and 'Once a month'

recode3 = {2: 2, 6: 6} subsetc2['COMP2v6']= subsetc2['S3BD5Q2E'].map(recode3)

Contingency table of observed counts

ct5=pandas.crosstab(subsetc2['MAJORDEP12'], subsetc2['COMP2v6']) print (ct5)

Column percentages

colsum5=ct5.sum(axis=0) colpcontab5=ct5/colsum5 print(colpcontab5)

Chi-square calculations for pair comparison of frequency groups 2 and 6, 'Nearly every day' and 'Once a month'

print ('Chi-square value, p value, expected counts for pair comparison of frequency groups -Nearly every day- and -Once a month-') cs5= scipy.stats.chi2_contingency(ct5) print (cs5)

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assignment

@@ -0,0 +1,118 @@

-- coding: utf-8 --

""" Created on Thu Feb 7 00:30:58 2019 @author: Voltas """

import pandas import numpy import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi nesarc = pandas.read_csv ('nesarc_pds.csv' , low_memory=False) # load NESARC dataset

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)

nesarc.columns = map(str.upper , nesarc.columns)

pandas.set_option('display.float_format' , lambda x:'%f'%x)

Change my variables to numeric

nesarc['AGE'] = nesarc['AGE'].convert_objects(convert_numeric=True) nesarc['S3BQ4'] = nesarc['S3BQ4'].convert_objects(convert_numeric=True) nesarc['S3BQ1A5'] = nesarc['S3BQ1A5'].convert_objects(convert_numeric=True) nesarc['S3BD5Q2B'] = nesarc['S3BD5Q2B'].convert_objects(convert_numeric=True) nesarc['S3BD5Q2E'] = nesarc['S3BD5Q2E'].convert_objects(convert_numeric=True) nesarc['MAJORDEP12'] = nesarc['MAJORDEP12'].convert_objects(convert_numeric=True) nesarc['GENAXDX12'] = nesarc['GENAXDX12'].convert_objects(convert_numeric=True)

Subset my sample

subset5 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)] # Cannabis users, ages 18-30 subsetc5 = subset5.copy()

Setting missing data for quantity of cannabis (measured in joints), variable S3BQ4

subsetc5['S3BQ4']=subsetc5['S3BQ4'].replace(99, numpy.nan) subsetc5['S3BQ4']=subsetc5['S3BQ4'].replace('BL', numpy.nan)

sub1 = subsetc5[['S3BQ4', 'MAJORDEP12']].dropna()

Using ols function for calculating the F-statistic and the associated p value

Depression (categorical, explanatory variable) and joints quantity (quantitative, response variable) correlation

model1 = smf.ols(formula='S3BQ4 ~ C(MAJORDEP12)', data=sub1) results1 = model1.fit() print (results1.summary())

Measure mean and spread for categorical variable MAJORDEP12, major depression

print ('Means for joints quantity by major depression status') m1= sub1.groupby('MAJORDEP12').mean() print (m1)

print ('Standard deviations for joints quantity by major depression status') sd1 = sub1.groupby('MAJORDEP12').std() print (sd1)

sub2 = subsetc5[['S3BQ4', 'GENAXDX12']].dropna()

Using ols function for calculating the F-statistic and the associated p value

Anxiety (categorical, explanatory variable) and joints quantity (quantitative, response variable) correlation

model2 = smf.ols(formula='S3BQ4 ~ C(GENAXDX12)', data=sub2) results2 = model2.fit() print (results2.summary())

Measure mean and spread for categorical variable GENAXDX12, general anxiety

print ('Means for joints quantity by major general anxiety status') m2= sub2.groupby('GENAXDX12').mean() print (m2)

print ('Standard deviations for joints quantity by general anxiety status') sd2 = sub2.groupby('GENAXDX12').std() print (sd2)

#

Setting missing data for frequency of cannabis use, variable S3BD5Q2E

subsetc5['S3BD5Q2E']=subsetc5['S3BD5Q2E'].replace(99, numpy.nan) subsetc5['S3BD5Q2E']=subsetc5['S3BD5Q2E'].replace('BL', numpy.nan)

sub3 = subsetc5[['S3BQ4', 'S3BD5Q2E']].dropna()

Using ols function for calculating the F-statistic and associated p value

Frequency of cannabis use (10 level categorical, explanatory variable) and joints quantity (quantitative, response variable) correlation

model3 = smf.ols(formula='S3BQ4 ~ C(S3BD5Q2E)', data=sub3).fit() print (model3.summary())

Measure mean and spread for categorical variable S3BD5Q2E, frequency of cannabis use

print ('Means for joints quantity by frequency of cannabis use status') mc2= sub3.groupby('S3BD5Q2E').mean() print (mc2)

print ('Standard deviations for joints quantity by frequency of cannabis use status') sdc2 = sub3.groupby('S3BD5Q2E').std() print (sdc2)

Run a post hoc test (paired comparisons), using Tukey HSDT

mc1 = multi.MultiComparison(sub3['S3BQ4'], sub3['S3BD5Q2E']) res1 = mc1.tukeyhsd() print(res1.summary())

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graph assignment

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.

Python Program

""" Created on Sun Oct 25 2015

@author: violetgirl """ 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')

END

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assignment3

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.

Python Program

""" Created on Sun Oct 18 2015

@author: violetgirl """ import pandas as pd

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,[0bc_max,0.25bc_max,0.5bc_max,0.75bc_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))

END

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GRAPHS

import pandas as pdimport numpy as npimport osimport matplotlib.pyplot as plt import seaborn

read pickled data

In [9]: data = pd.read_pickle('cleaned_data2.pickle')

In [10]: data.shape

Out[10]: (43093, 12)

In [11]: data.dtypes

Out[11]: marital objectage_1st_mar objectage int64hispanich int64indian int64asian int64black int64HAWAIIAN int64WHITE int64how_mar_ended objectedu objectETHNICITY objectdtype: object

In [12]: data.head()

Out[12]:

marital

age_1st_mar

age

hispanich

indian

asian

black

HAWAIIAN

WHITE

how_mar_ended

edu

ETHNICITY

0

Never Married

23

1

2

1

Completed high school

hispanich

1

Married

23

28

1

2

1

Completed high school

hispanich

2

Widowed

35

81

1

2

1

2

8

hispanich

3

Never Married

18

1

2

1

Completed high school

hispanich

4

Married

22

36

2

1

2

bachelor's

black

In [6]:%matplotlib inline

barplot (count plot) for the marital status

In [7]:# univariate bar graph for categorical variables# First hange format from numeric to categoricalplt.figure(figsize=(15,5))data["marital"] = data["marital"].astype('category') seaborn.countplot(x="marital", data=data)plt.xlabel('marital ')

Out[7]:

barplot (count plot) for the education level .

In [8]: plt.figure(figsize=(18,8))data["edu"] = data["edu"].astype('category') seaborn.countplot(x="edu", data=data)plt.xlabel('education ')

Out[8]:

barplot (count plot) for the ETHNICITY .

In [9]: plt.figure(figsize=(10,5))data["ETHNICITY"] = data["ETHNICITY"].astype('category') seaborn.countplot(x="ETHNICITY", data=data)plt.xlabel('ETHNICITY ')

Out[9]:

the distribution od the ages in the sample

In [13]: plt.figure(figsize=(18,8))seaborn.distplot(data["age"].dropna(), kde=False);plt.xlabel('Age')

Out[13]:

In [16]:# plt.figure(figsize=(18,8))# seaborn.distplot(data["age_1st_mar"], kde=False);# plt.xlabel('age_1st_mar')

In [17]: data.marital.describe()

Out[17]: count 43093unique 6top Marriedfreq 20769Name: marital, dtype: object

In [18]: data['age_1st_mar'].describe()

Out[18]: count 43093unique 59top freq 10756Name: age_1st_mar, dtype: object

In [19]: data.age.describe()

Out[19]: count 43093.000000mean 46.400808std 18.178612min 18.00000025% 32.00000050% 44.00000075% 59.000000max 98.000000Name: age, dtype: float64

In [20]: data.how_mar_ended.describe()

Out[20]: count 43093unique 5top freq 27966Name: how_mar_ended, dtype: object

renaming the education to be numeric and Representative for the estimate of years of studying .

In [13]: edu_remap_dict = { 'No formal schooling':0, 'K, 1 or 2':1.5, '3 or 4':3.5, '5 or 6':5.5, '7':7, '8':8, '(grades 9-11)':10, 'Completed high school':12, ' degree':14, 'Some college (no degree)':14, 'technical 2-year degree':14, 'bachelor\'s':16, 'master\'s':18 }

In [ ]:

In [15]: data['edu'] = data['edu'].map(edu_remap_dict)

In [27]: plt.figure(figsize=(12,8))seaborn.factorplot(x="edu", y="age", data=data) plt.xlabel('education')plt.ylabel('age at the first marriage')plt.title('the relationship between education and age at the first marriage ')

Out[27]:

In [16]: data.to_pickle('data.pickle')

note there is two contentious numerical variables in the variables i chose that's why i didn't use scatter plots.

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Creating graphs for your data

print("ahmed") ahmed

In [2]:

ahmed = [1,2,3,4,5,6,7,8,9]

print (ahmed[4]) 5

In [ ]:

ahmed hindi

In [2]:

import pandas as pd import numpy as np import os import matplotlib.pyplot as plt import seaborn read pickled data

In [9]:

data = pd.read_pickle('cleaned_data2.pickle')

In [10]:

data.shape

Out[10]:

(43093, 12)

In [11]:

data.dtypes

Out[11]:

marital object age_1st_mar object age int64 hispanich int64 indian int64 asian int64 black int64 HAWAIIAN int64 WHITE int64 how_mar_ended object edu object ETHNICITY object dtype: object

In [12]:

data.head()

Out[12]:

marita l age_1st_ mar ag e hispani ch indi an asia n bla ck HAWAII AN WHI TE how_mar_e nded edu ETHNIC ITY

0 Never Marrie d

23 1 2 2 2 2 1

Comple ted high school hispani ch

1 Marrie d 23 28 1 2 2 2 2 1

Comple ted high school hispani ch

2 Widow ed 35 81 1 2 2 2 2 1 2 8 hispani ch

3 Never Marrie d

18 1 2 2 2 2 1

Comple ted high school hispani ch

4 Marrie d 22 36 2 2 2 1 2 2 bachelo r's black In [6]:

%matplotlib inline barplot (count plot) for the marital status

In [7]:

# univariate bar graph for categorical variables # First hange format from numeric to categorical plt.figure(figsize=(15,5)) data["marital"] = data["marital"].astype('category') seaborn.countplot(x="marital", data=data) plt.xlabel('marital ')

Out[7]:

barplot (count plot) for the education level .

In [8]:

plt.figure(figsize=(18,8)) data["edu"] = data["edu"].astype('category') seaborn.countplot(x="edu", data=data) plt.xlabel('education ')

Out[8]:

barplot (count plot) for the ETHNICITY .

In [9]:

plt.figure(figsize=(10,5)) data["ETHNICITY"] = data["ETHNICITY"].astype('category') seaborn.countplot(x="ETHNICITY", data=data) plt.xlabel('ETHNICITY ')

Out[9]:

the distribution od the ages in the sample

In [13]:

plt.figure(figsize=(18,8)) seaborn.distplot(data["age"].dropna(), kde=False); plt.xlabel('Age')

Out[13]:

In [16]:

# plt.figure(figsize=(18,8)) # seaborn.distplot(data["age_1st_mar"], kde=False); # plt.xlabel('age_1st_mar')

In [17]:

data.marital.describe()

Out[17]:

count 43093 unique 6 top Married freq 20769 Name: marital, dtype: object

In [18]:

data['age_1st_mar'].describe()

Out[18]:

count 43093 unique 59 top freq 10756 Name: age_1st_mar, dtype: object

In [19]:

data.age.describe()

Out[19]:

count 43093.000000 mean 46.400808 std 18.178612 min 18.000000 25% 32.000000 50% 44.000000 75% 59.000000 max 98.000000 Name: age, dtype: float64

In [20]:

data.how_mar_ended.describe()

Out[20]:

count 43093 unique 5 top freq 27966 Name: how_mar_ended, dtype: object renaming the education to be numeric and Representative for the estimate of years of studying .

In [13]:

edu_remap_dict = { 'No formal schooling':0, 'K, 1 or 2':1.5, '3 or 4':3.5, '5 or 6':5.5, '7':7, '8':8, '(grades 9-11)':10, 'Completed high school':12, ' degree':14, 'Some college (no degree)':14, 'technical 2-year degree':14, 'bachelor\'s':16, 'master\'s':18 }

In [ ]: In [15]:

data['edu'] = data['edu'].map(edu_remap_dict)

In [27]:

plt.figure(figsize=(12,8)) seaborn.factorplot(x="edu", y="age", data=data) plt.xlabel('education') plt.ylabel('age at the first marriage') plt.title('the relationship between education and age at the first marriage ')

Out[27]:

In [16]:

data.to_pickle('data.pickle') note there is two contentious numerical variables in the variables i chose that's why i didn't use scatter plots.

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Making Data Management Decisions

import pandas as pd import numpy as np import os import matplotlib.pyplot as plt import seaborn read data and pickle it all

In [2]:

#this function reads data from csv file def read_data(): data = pd.read_csv('/home/data- sci/Desktop/analysis/course/nesarc_pds.csv',low_memory=False) return data

In [3]: #this function saves the data in a pickle "binary" file so it's faster to deal with it next time we run the script def pickle_data(data): data.to_pickle('cleaned_data.pickle') #this function reads data from the binary .pickle file

def get_pickle(): return pd.read_pickle('cleaned_data.pickle')

In [4]:

def the_data(): """this function will check and read the data from the pickle file if not fond it will read the csv file then pickle it""" if os.path.isfile('cleaned_data.pickle'): data = get_pickle() else: data = read_data() pickle_data(data) return data

In [20]:

data = the_data()

In [21]:

data.shape

Out[21]:

(43093, 3008)

In [22]:

data.head()

Out[22]:

ET H R AC E2 A ET O TL C A 2 I D N U M P S U ST R A T U M W EI G HT C D A Y C M O N C Y E A R R E G I O N . . . SO L1 2A BD EP SO LP 12 AB DE P HA L1 2A BD EP HA LP 12 AB DE P M AR 12 AB DE P MA RP 12 AB DE P HE R1 2A BD EP HE RP 12 AB DE P OT HB 12 AB DE P OT HB P12 AB DE P

0 5 1 4 0 0 7 4 0 3 39 28 .6 13 50 5 1 4 8 2 0 0 1 4 . . . 0 0 0 0 0 0 0 0 0 0

1 5 0. 0 0 1 4 2 6 0 4 5 6 0 4 36 38 .6 91 84 5 1 2 1 2 0 0 2 4 . . . 0 0 0 0 0 0 0 0 0 0

2 5 3 1 2 1 2 57 79 2 3 1 1 2 0 3 . . 0 0 0 0 0 0 0 0 0 0

0 4 2 1 8 .0 32 02 5

0 1 .

3 5 4 1 7 0 9 9 1 7 0 4 10 71 .7 54 30 3 9 9 2 0 0 1 2 . . . 0 0 0 0 0 0 0 0 0 0

4 2 5 1 7 0 9 9 1 7 0 4 49 86 .9 52 37 7 1 8 1 0 2 0 0 1 2 . . . 0 0 0 0 0 0 0 0 0 0

5 rows × 3008 columns

In [102]:

data2 = data[['MARITAL','S1Q4A','AGE','S1Q4B','S1Q6A']] data2 = data2.rename(columns={'MARITAL':'marital','S1Q4A':'age_1st_mar', 'AGE':'age','S1Q4B':'how_mar_ended','S1Q6A':'edu'}) In [103]:

#selecting the wanted range of values #THE RANGE OF WANTED AGES data2['age'] = data2[data2['age'] < 30] #THE RANGE OF WANTED AGES OF FISRT MARRIEGE #convert to numeric so we can subset the values < 25 data2['age_1st_mar'] = pd.to_numeric(data2['age_1st_mar'], errors='ignor') In [105]:

data2 = data2[data2['age_1st_mar'] < 25 ] data2.age_1st_mar.value_counts()

Out[105]:

21.0 3473 19.0 2999 18.0 2944 20.0 2889 22.0 2652 23.0 2427 24.0 2071 17.0 1249 16.0 758 15.0 304 14.0 150 Name: age_1st_mar, dtype: int64

for simplisity will remap the variable edu to have just 4 levels below high school education == 0 high school == 1 collage == 2 higher == 3

In [106]: edu_remap ={1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:1,9:1,10:1,11:1,12:2,13:2,14:3} data2['edu'] = data2['edu'].map(edu_remap) print the frquancy of the values

In [107]:

def distribution(var_data): """this function will print out the frequency distribution for every variable in the data-frame """ #var_data = pd.to_numeric(var_data, errors='ignore') print("the count of the values in {}".format(var_data.name)) print(var_data.value_counts()) print("the % of every value in the {} variable ".format(var_data.name)) print(var_data.value_counts(normalize=True)) print("-----------------------------------")

def print_dist(): # this function loops though the variables and print them out for i in data2.columns: print(distribution(data2[i]))

print_dist() the count of the values in marital 1 13611 4 3793 3 3183 5 977 2 352 Name: marital, dtype: int64 the % of every value in the marital variable 1 0.621053 4 0.173070 3 0.145236 5 0.044579 2 0.016061 Name: marital, dtype: float64 ----------------------------------- None the count of the values in age_1st_mar 21.0 3473 19.0 2999 18.0 2944 20.0 2889

22.0 2652 23.0 2427 24.0 2071 17.0 1249 16.0 758 15.0 304 14.0 150 Name: age_1st_mar, dtype: int64 the % of every value in the age_1st_mar variable 21.0 0.158469 19.0 0.136841 18.0 0.134331 20.0 0.131822 22.0 0.121007 23.0 0.110741 24.0 0.094497 17.0 0.056990 16.0 0.034587 15.0 0.013871 14.0 0.006844 Name: age_1st_mar, dtype: float64 ----------------------------------- None the count of the values in age 1.0 1957 4.0 207 5.0 153 2.0 40 3.0 7 Name: age, dtype: int64 the % of every value in the age variable 1.0 0.827834 4.0 0.087563 5.0 0.064721 2.0 0.016920 3.0 0.002961 Name: age, dtype: float64 ----------------------------------- None the count of the values in how_mar_ended 10459 2 8361 1 2933 3 154 9 9 Name: how_mar_ended, dtype: int64 the % of every value in the how_mar_ended variable 0.477231 2 0.381502 1 0.133829 3 0.007027 9 0.000411 Name: how_mar_ended, dtype: float64

----------------------------------- None the count of the values in edu 1 13491 0 4527 2 2688 3 1210 Name: edu, dtype: int64 the % of every value in the edu variable 1 0.615578 0 0.206561 2 0.122650 3 0.055211 Name: edu, dtype: float64 ----------------------------------- None summery

In [1]:

# ##### marital status # Married 0.48 % | # Living with someone 0.22 % | # Widowed 0.12 % | # Divorced 0.1 % | # Separated 0.03 % | # Never Married 0.03 % | # | # -------------------------------------| # -------------------------------------| # | # ##### AGE AT FIRST MARRIAGE FOR THOSE # WHO MARRY UNDER THE AGE OF 25 | # AGE % | # 21 0.15 % | # 19 0.13 % | # 18 0.13 % | # 20 0.13 % | # 22 0.12 % | # 23 0.11 % | # 24 0.09 % | # 17 0.05 % | # 16 0.03 % | # 15 0.01 % | # 14 0.00 % | # | # -------------------------------------| # -------------------------------------| # | # ##### HOW FIRST MARRIAGE ENDED # Widowed 0.65 % | # Divorced 0.25 % | # Other 0.09 % | # Unknown 0.004% |

# Na 0.002% | # | # -------------------------------------| # -------------------------------------| # | # ##### education # high school 0.58 % | # lower than high school 0.18 % | # collage 0.15 % | # ms and higher 0.07 % | # | 1- recoding unknown values from the variable "how_mar_ended" HOW FIRST MARRIAGE ENDED will code the 9 value from Unknown to NaN

In [13]:

data2['how_mar_ended'] = data2['how_mar_ended'].replace(9, np.nan) data2['age_1st_mar'] = data2['age_1st_mar'].replace(99, np.nan)

In [14]:

data2['how_mar_ended'].value_counts(sort=False, dropna=False)

Out[14]:

1 4025 9 98 3 201 2 10803 27966 Name: how_mar_ended, dtype: int64

In [23]:

#pickle the data tp binary .pickle file pickle_data(data2) Week 4 { "cells": [], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

chidujsFollow

Making Data Management Decisions

import pandas as pd import numpy as np import os import matplotlib.pyplot as plt import seaborn read data and pickle it all

In [2]:

#this function reads data from csv file def read_data(): data = pd.read_csv('/home/data- sci/Desktop/analysis/course/nesarc_pds.csv',low_memory=False) return data

In [3]: #this function saves the data in a pickle "binary" file so it's faster to deal with it next time we run the script def pickle_data(data): data.to_pickle('cleaned_data.pickle') #this function reads data from the binary .pickle file

def get_pickle(): return pd.read_pickle('cleaned_data.pickle')

In [4]:

def the_data(): """this function will check and read the data from the pickle file if not fond it will read the csv file then pickle it""" if os.path.isfile('cleaned_data.pickle'): data = get_pickle() else: data = read_data() pickle_data(data) return data

In [20]:

data = the_data()

In [21]:

data.shape

Out[21]:

(43093, 3008)

In [22]:

data.head()

Out[22]:

ET H R AC E2 A ET O TL C A 2 I D N U M P S U ST R A T U M W EI G HT C D A Y C M O N C Y E A R R E G I O N . . . SO L1 2A BD EP SO LP 12 AB DE P HA L1 2A BD EP HA LP 12 AB DE P M AR 12 AB DE P MA RP 12 AB DE P HE R1 2A BD EP HE RP 12 AB DE P OT HB 12 AB DE P OT HB P12 AB DE P

0 5 1 4 0 0 7 4 0 3 39 28 .6 13 50 5 1 4 8 2 0 0 1 4 . . . 0 0 0 0 0 0 0 0 0 0

1 5 0. 0 0 1 4 2 6 0 4 5 6 0 4 36 38 .6 91 84 5 1 2 1 2 0 0 2 4 . . . 0 0 0 0 0 0 0 0 0 0

2 5 3 1 2 1 2 57 79 2 3 1 1 2 0 3 . . 0 0 0 0 0 0 0 0 0 0

0 4 2 1 8 .0 32 02 5

0 1 .

3 5 4 1 7 0 9 9 1 7 0 4 10 71 .7 54 30 3 9 9 2 0 0 1 2 . . . 0 0 0 0 0 0 0 0 0 0

4 2 5 1 7 0 9 9 1 7 0 4 49 86 .9 52 37 7 1 8 1 0 2 0 0 1 2 . . . 0 0 0 0 0 0 0 0 0 0

5 rows × 3008 columns

In [102]:

data2 = data[['MARITAL','S1Q4A','AGE','S1Q4B','S1Q6A']] data2 = data2.rename(columns={'MARITAL':'marital','S1Q4A':'age_1st_mar', 'AGE':'age','S1Q4B':'how_mar_ended','S1Q6A':'edu'}) In [103]:

#selecting the wanted range of values #THE RANGE OF WANTED AGES data2['age'] = data2[data2['age'] < 30] #THE RANGE OF WANTED AGES OF FISRT MARRIEGE #convert to numeric so we can subset the values < 25 data2['age_1st_mar'] = pd.to_numeric(data2['age_1st_mar'], errors='ignor') In [105]:

data2 = data2[data2['age_1st_mar'] < 25 ] data2.age_1st_mar.value_counts()

Out[105]:

21.0 3473 19.0 2999 18.0 2944 20.0 2889 22.0 2652 23.0 2427 24.0 2071 17.0 1249 16.0 758 15.0 304 14.0 150 Name: age_1st_mar, dtype: int64

for simplisity will remap the variable edu to have just 4 levels below high school education == 0 high school == 1 collage == 2 higher == 3

In [106]: edu_remap ={1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:1,9:1,10:1,11:1,12:2,13:2,14:3} data2['edu'] = data2['edu'].map(edu_remap) print the frquancy of the values

In [107]:

def distribution(var_data): """this function will print out the frequency distribution for every variable in the data-frame """ #var_data = pd.to_numeric(var_data, errors='ignore') print("the count of the values in {}".format(var_data.name)) print(var_data.value_counts()) print("the % of every value in the {} variable ".format(var_data.name)) print(var_data.value_counts(normalize=True)) print("-----------------------------------")

def print_dist(): # this function loops though the variables and print them out for i in data2.columns: print(distribution(data2[i]))

print_dist() the count of the values in marital 1 13611 4 3793 3 3183 5 977 2 352 Name: marital, dtype: int64 the % of every value in the marital variable 1 0.621053 4 0.173070 3 0.145236 5 0.044579 2 0.016061 Name: marital, dtype: float64 ----------------------------------- None the count of the values in age_1st_mar 21.0 3473 19.0 2999 18.0 2944 20.0 2889

22.0 2652 23.0 2427 24.0 2071 17.0 1249 16.0 758 15.0 304 14.0 150 Name: age_1st_mar, dtype: int64 the % of every value in the age_1st_mar variable 21.0 0.158469 19.0 0.136841 18.0 0.134331 20.0 0.131822 22.0 0.121007 23.0 0.110741 24.0 0.094497 17.0 0.056990 16.0 0.034587 15.0 0.013871 14.0 0.006844 Name: age_1st_mar, dtype: float64 ----------------------------------- None the count of the values in age 1.0 1957 4.0 207 5.0 153 2.0 40 3.0 7 Name: age, dtype: int64 the % of every value in the age variable 1.0 0.827834 4.0 0.087563 5.0 0.064721 2.0 0.016920 3.0 0.002961 Name: age, dtype: float64 ----------------------------------- None the count of the values in how_mar_ended 10459 2 8361 1 2933 3 154 9 9 Name: how_mar_ended, dtype: int64 the % of every value in the how_mar_ended variable 0.477231 2 0.381502 1 0.133829 3 0.007027 9 0.000411 Name: how_mar_ended, dtype: float64

----------------------------------- None the count of the values in edu 1 13491 0 4527 2 2688 3 1210 Name: edu, dtype: int64 the % of every value in the edu variable 1 0.615578 0 0.206561 2 0.122650 3 0.055211 Name: edu, dtype: float64 ----------------------------------- None summery

In [1]:

# ##### marital status # Married 0.48 % | # Living with someone 0.22 % | # Widowed 0.12 % | # Divorced 0.1 % | # Separated 0.03 % | # Never Married 0.03 % | # | # -------------------------------------| # -------------------------------------| # | # ##### AGE AT FIRST MARRIAGE FOR THOSE # WHO MARRY UNDER THE AGE OF 25 | # AGE % | # 21 0.15 % | # 19 0.13 % | # 18 0.13 % | # 20 0.13 % | # 22 0.12 % | # 23 0.11 % | # 24 0.09 % | # 17 0.05 % | # 16 0.03 % | # 15 0.01 % | # 14 0.00 % | # | # -------------------------------------| # -------------------------------------| # | # ##### HOW FIRST MARRIAGE ENDED # Widowed 0.65 % | # Divorced 0.25 % | # Other 0.09 % | # Unknown 0.004% |

# Na 0.002% | # | # -------------------------------------| # -------------------------------------| # | # ##### education # high school 0.58 % | # lower than high school 0.18 % | # collage 0.15 % | # ms and higher 0.07 % | # | 1- recoding unknown values from the variable "how_mar_ended" HOW FIRST MARRIAGE ENDED will code the 9 value from Unknown to NaN

In [13]:

data2['how_mar_ended'] = data2['how_mar_ended'].replace(9, np.nan) data2['age_1st_mar'] = data2['age_1st_mar'].replace(99, np.nan)

In [14]:

data2['how_mar_ended'].value_counts(sort=False, dropna=False)

Out[14]:

1 4025 9 98 3 201 2 10803 27966 Name: how_mar_ended, dtype: int64

In [23]:

#pickle the data tp binary .pickle file pickle_data(data2) Week 4 { "cells": [], "metadata": {}, "nbformat": 4, "nbformat_minor": 0 }

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Assignment 2

PYTHON PROGRAM:

import pandas as pd import numpy as np

data = pd.read_csv('gapminder.csv',low_memory=False)

data.columns = map(str.lower, data.columns) pd.set_option('display.float_format', lambda x:'%f'%x)

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)

print("Statistics for a Suicide Rate") print(data['suicideper100th'].describe())

sub = data[(data['suicideper100th']>12)]

sub_copy = sub.copy()

bc = sub_copy['breastcancerper100th'].value_counts(sort=False,bins=10)

pbc = sub_copy['breastcancerper100th'].value_counts(sort=False,bins=10,normalize=True)*100

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 = '%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'))

hc = sub_copy['hivrate'].value_counts(sort=False,bins=7) phc = sub_copy['hivrate'].value_counts(sort=False,bins=7,normalize=True)100 hc1=[] # Cumulative Frequency phc1=[] # Cumulative Percentage cf=0 cp=0 for freq in bc: cf=cf+freq hc1.append(cf) pf=cf100/len(sub_copy) phc1.append(pf)

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'))

ec = sub_copy['employrate'].value_counts(sort=False,bins=10)

pec = sub_copy['employrate'].value_counts(sort=False,bins=10,normalize=True)100 ec1=[] # Cumulative Frequency pec1=[] # Cumulative Percentage cf=0 cp=0 for freq in bc: cf=cf+freq ec1.append(cf) pf=cf100/len(sub_copy) pec1.append(pf)

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:

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

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?

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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Assignment 1

Data set: GapMinder Data. Research question: Is a fertility rate associated with a number of breast cancer cases? Items included in the CodeBook: for fertility rate: Children per woman (total fertility) Children per woman (total fertility), with projections for breast cancer: Breast cancer, deaths per 100,000 women Breast cancer, new cases per 100,000 women Breast cancer, number of female deaths Breast cancer, number of new female cases Literature Review: From original source: http://ww5.komen.org/KomenPerspectives/Does-pregnancy-affect-breast-cancer-risk-and-survival-.html The more children a woman has given birth to, the lower her risk of breast cancer tends to be. Women who have never given birth have a slightly higher risk of breast cancer compared to women who have had more than one child. The hypothesis to explore using GapMinder data set: the higher fertility rate, the lower risk of breast cancer.