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import pandas as pd
import numpy as np
from scipy.stats import ttest_ind


# Assignment 4 - Hypothesis Testing¶

This assignment requires more individual learning than previous assignments - you are encouraged to check out the pandas documentation to find functions or methods you might not have used yet, or ask questions on Stack Overflow and tag them as pandas and python related. And of course, the discussion forums are open for interaction with your peers and the course staff.

Definitions:

• A quarter is a specific three month period, Q1 is January through March, Q2 is April through June, Q3 is July through September, Q4 is October through December.
• A recession is defined as starting with two consecutive quarters of GDP decline, and ending with two consecutive quarters of GDP growth.
• A recession bottom is the quarter within a recession which had the lowest GDP.
• A university town is a city which has a high percentage of university students compared to the total population of the city.

Hypothesis: University towns have their mean housing prices less effected by recessions. Run a t-test to compare the ratio of the mean price of houses in university towns the quarter before the recession starts compared to the recession bottom. (price_ratio=quarter_before_recession/recession_bottom)

The following data files are available for this assignment:

• From the Zillow research data site there is housing data for the United States. In particular the datafile for all homes at a city level, City_Zhvi_AllHomes.csv, has median home sale prices at a fine grained level.
• From the Wikipedia page on college towns is a list of university towns in the United States which has been copy and pasted into the file university_towns.txt.
• From Bureau of Economic Analysis, US Department of Commerce, the GDP over time of the United States in current dollars (use the chained value in 2009 dollars), in quarterly intervals, in the file gdplev.xls. For this assignment, only look at GDP data from the first quarter of 2000 onward.

Each function in this assignment below is worth 10%, with the exception of run_ttest(), which is worth 50%.

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# Use this dictionary to map state names to two letter acronyms
states = {'OH': 'Ohio', 'KY': 'Kentucky', 'AS': 'American Samoa', 'NV': 'Nevada', 'WY': 'Wyoming', 'NA': 'National', 'AL': 'Alabama', 'MD': 'Maryland', 'AK': 'Alaska', 'UT': 'Utah', 'OR': 'Oregon', 'MT': 'Montana', 'IL': 'Illinois', 'TN': 'Tennessee', 'DC': 'District of Columbia', 'VT': 'Vermont', 'ID': 'Idaho', 'AR': 'Arkansas', 'ME': 'Maine', 'WA': 'Washington', 'HI': 'Hawaii', 'WI': 'Wisconsin', 'MI': 'Michigan', 'IN': 'Indiana', 'NJ': 'New Jersey', 'AZ': 'Arizona', 'GU': 'Guam', 'MS': 'Mississippi', 'PR': 'Puerto Rico', 'NC': 'North Carolina', 'TX': 'Texas', 'SD': 'South Dakota', 'MP': 'Northern Mariana Islands', 'IA': 'Iowa', 'MO': 'Missouri', 'CT': 'Connecticut', 'WV': 'West Virginia', 'SC': 'South Carolina', 'LA': 'Louisiana', 'KS': 'Kansas', 'NY': 'New York', 'NE': 'Nebraska', 'OK': 'Oklahoma', 'FL': 'Florida', 'CA': 'California', 'CO': 'Colorado', 'PA': 'Pennsylvania', 'DE': 'Delaware', 'NM': 'New Mexico', 'RI': 'Rhode Island', 'MN': 'Minnesota', 'VI': 'Virgin Islands', 'NH': 'New Hampshire', 'MA': 'Massachusetts', 'GA': 'Georgia', 'ND': 'North Dakota', 'VA': 'Virginia'}

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import pandas as pd
import numpy as np
import re
from scipy import stats

def get_list_of_university_towns():
'''Returns a DataFrame of towns and the states they are in from the
university_towns.txt list. The format of the DataFrame is:
DataFrame( [ ["Michigan","Ann Arbor"], ["Michigan", "Yipsilanti"] ],
columns=["State","RegionName"]  )'''

states = [line.split('') for line in f if '' in line] # each state name ends in 

town_groups = re.split(r'[A-Z]{1}\w*\s?\w*$edit$', f2)[1:]# split on state names to get list of towns in all states

school = []

for state in range(len(states)):
for town in town_groups[state].split('\n'):
if (town != '') and (town != '\n'):
temp = {"RegionName" : town.split(' ('), "State" : states[state]}
school.append(temp)

col_order = ['State', 'RegionName']
university_towns = pd.DataFrame(school)
university_towns = university_towns.sort_values(by=['State']).reset_index()
university_towns = university_towns[col_order]

return university_towns

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gdp = pd.read_excel('gdplev.xls', skiprows=5)
gdp = gdp.drop(gdp.index[0:2])
gdp = gdp.reset_index()
gdp = gdp.drop(['index', 'Unnamed: 3', 'Unnamed: 7'], axis=1)
gdp = gdp.rename(columns={'Unnamed: 0' : 'Year', 'Unnamed: 4' : 'Quarterly'})

gdp = gdp.drop(gdp.index[0:212])
gdp = gdp.reset_index()

col_to_keep = ['Quarterly', 'GDP in billions of current dollars.1', 'GDP in billions of chained 2009 dollars.1']
gdp = gdp[col_to_keep]

gdp['deltaGdp'] = gdp['GDP in billions of chained 2009 dollars.1'].diff()

def get_recession_start():
'''Returns the year and quarter of the recession start time as a
string value in a format such as 2005q3'''

rec_years = []
for i in range(len(gdp)):

if (gdp.loc[i, 'deltaGdp'] < 0) and (gdp.loc[i+1, 'deltaGdp'] < 0):
if gdp.loc[i, 'Quarterly'] not in rec_years:
rec_years.append(gdp.loc[i, 'Quarterly'])
rec_years.append(gdp.loc[i+1, 'Quarterly'])

return rec_years

get_recession_start()

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gdp = pd.read_excel('gdplev.xls', skiprows=5)
gdp = gdp.drop(gdp.index[0:2])
gdp = gdp.reset_index()
gdp = gdp.drop(['index', 'Unnamed: 3', 'Unnamed: 7'], axis=1)
gdp = gdp.rename(columns={'Unnamed: 0' : 'Year', 'Unnamed: 4' : 'Quarterly'})

gdp = gdp.drop(gdp.index[0:212])
gdp = gdp.reset_index()

col_to_keep = ['Quarterly', 'GDP in billions of current dollars.1', 'GDP in billions of chained 2009 dollars.1']
gdp = gdp[col_to_keep]

gdp['deltaGdp'] = gdp['GDP in billions of chained 2009 dollars.1'].diff()


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def get_recession_end():
'''Returns the year and quarter of the recession end time as a
string value in a format such as 2005q3'''

rec_years = []
for i in range(len(gdp)):

if (gdp.loc[i, 'deltaGdp'] < 0) and (gdp.loc[i+1, 'deltaGdp'] < 0):
if gdp.loc[i, 'Quarterly'] not in rec_years:
rec_years.append(gdp.loc[i, 'Quarterly'])
rec_years.append(i)
rec_years.append(gdp.loc[i+1, 'Quarterly'])
rec_years.append(i+1)

return gdp.loc[rec_years[-1]+2, 'Quarterly']

#return rec_years

get_recession_end()

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def get_recession_bottom():
'''Returns the year and quarter of the recession bottom time as a
string value in a format such as 2005q3'''

rec_years = []
for i in range(len(gdp)):

if (gdp.loc[i, 'deltaGdp'] < 0) and (gdp.loc[i+1, 'deltaGdp'] < 0):
if gdp.loc[i, 'Quarterly'] not in rec_years:
rec_years.append(gdp.loc[i, 'Quarterly'])
rec_years.append(i)
rec_years.append(gdp.loc[i+1, 'Quarterly'])
rec_years.append(i+1)

rec_start = rec_years
rec_end = rec_years[-1]

rec_bot = gdp.loc[rec_start : rec_end, ['GDP in billions of chained 2009 dollars.1', 'Quarterly']]
mask = min(rec_bot['GDP in billions of chained 2009 dollars.1'])

k = rec_bot.where(rec_bot['GDP in billions of chained 2009 dollars.1'] == mask).dropna().reset_index()

return k.loc[0, 'Quarterly']

get_recession_bottom()

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def quarter_rows(row):
for i in range(0, len(row), 3):
row.replace(row[i], np.mean(row[i:i+3]), inplace=True)
return row

def convert_housing_data_to_quarters():
'''Converts the housing data to quarters and returns it as mean
values in a dataframe. This dataframe should be a dataframe with
columns for 2000q1 through 2016q3, and should have a multi-index
in the shape of ["State","RegionName"].

Note: Quarters are defined in the assignment description, they are
not arbitrary three month periods.

The resulting dataframe should have 67 columns, and 10,730 rows.
'''

housing3 = housing.set_index(["State","RegionName"]).ix[:, '2000-01' : ]

'''
def quarter_rows(row):
for i in range(0, len(row), 3):
row.replace(row[i], np.mean(row[i:i+3]), inplace=True)
return row
housing = housing.apply(quarter_rows, axis=1)
This for loop accomplished the same purpose as datetime and resampling below, but much slower
'''

housing3.columns = pd.to_datetime(housing3.columns).to_period('M')
housing3 = housing3.resample('q', axis=1).mean()

states = {'OH': 'Ohio', 'KY': 'Kentucky', 'AS': 'American Samoa', 'NV': 'Nevada', \
'WY': 'Wyoming', 'NA': 'National', 'AL': 'Alabama', 'MD': 'Maryland', \
'AK': 'Alaska', 'UT': 'Utah', 'OR': 'Oregon', 'MT': 'Montana', 'IL': 'Illinois', \
'TN': 'Tennessee', 'DC': 'District of Columbia', 'VT': 'Vermont', 'ID': 'Idaho', \
'AR': 'Arkansas', 'ME': 'Maine', 'WA': 'Washington', 'HI': 'Hawaii', 'WI': 'Wisconsin', \
'MI': 'Michigan', 'IN': 'Indiana', 'NJ': 'New Jersey', 'AZ': 'Arizona', 'GU': 'Guam', \
'MS': 'Mississippi', 'PR': 'Puerto Rico', 'NC': 'North Carolina', 'TX': 'Texas', \
'SD': 'South Dakota', 'MP': 'Northern Mariana Islands', 'IA': 'Iowa', 'MO': 'Missouri', \
'CT': 'Connecticut', 'WV': 'West Virginia', 'SC': 'South Carolina', 'LA': 'Louisiana', \
'KS': 'Kansas', 'NY': 'New York', 'NE': 'Nebraska', 'OK': 'Oklahoma', 'FL': 'Florida', \
'CA': 'California', 'CO': 'Colorado', 'PA': 'Pennsylvania', 'DE': 'Delaware', 'NM': 'New Mexico', \
'RI': 'Rhode Island', 'MN': 'Minnesota', 'VI': 'Virgin Islands', 'NH': 'New Hampshire', \
'MA': 'Massachusetts', 'GA': 'Georgia', 'ND': 'North Dakota', 'VA': 'Virginia'}

housing3 = housing3.rename(index=states)
return housing3


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def run_ttest():
'''First creates new data showing the decline or growth of housing prices
between the recession start and the recession bottom. Then runs a ttest
comparing the university town values to the non-university towns values,
return whether the alternative hypothesis (that the two groups are the same)
is true or not as well as the p-value of the confidence.

Return the tuple (different, p, better) where different=True if the t-test is
True at a p<0.01 (we reject the null hypothesis), or different=False if
otherwise (we cannot reject the null hypothesis). The variable p should
be equal to the exact p value returned from scipy.stats.ttest_ind(). The
value for better should be either "university town" or "non-university town"
depending on which has a lower mean price ratio (which is equivilent to a
reduced market loss).'''

better = ''

univ_towns = get_list_of_university_towns()
univ_towns['univ_town'] = 'university town' # create a new column to mark university towns
hous_data = convert_housing_data_to_quarters()
hous_data.columns = hous_data.columns.map(str) # convert the column headings from PeriodIndex to string

hous_data['change'] = hous_data['2008Q1'] - hous_data['2009Q2'] # compute changes in housing price
hous_data['mean_pr'] = hous_data['2008Q1'].div(hous_data['2009Q2'])

all_towns = pd.merge(hous_data, univ_towns, how='outer', right_on=['State', 'RegionName'], left_index=True)
all_towns = all_towns.set_index(['State', 'RegionName'])

all_towns.ix[all_towns.univ_town != 'university town', 'univ_town'] = 'non-university town'

uni = all_towns[all_towns['univ_town'] == 'university town'].dropna()
non_uni = all_towns[all_towns['univ_town'] == 'non-university town'].dropna()

m, p = stats.ttest_ind(non_uni['mean_pr'], uni['mean_pr'])

if m > 0:
better = 'university town'

return True, p, better

run_ttest()

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