def main(fips, county):
#county = 'Lubbock'
#fips = '303'
figures = []
#Fulfills the 'Number' Column of the Table
county_data_2010_number = api.api_request(
p.frankenstein(p.table_7_call_2010_number, fips))
county_data_2000_number = api.api_request(
p.frankenstein(p.table_7_call_2000_number, fips))
#Fulfills the 'Avg. HH Size' Column of the Table
county_data_2010 = api.api_request(
p.frankenstein(p.table_7_call_2010, fips))
county_data_2000 = api.api_request(
p.frankenstein(p.table_7_call_2000, fips))
#All necessary data prior to 2000
county_data_1990 = get_excel_data(p.table7_excel_1990, county, 1990)
county_data_1980 = get_excel_data(p.table7_excel_1980, county, 1980)
county_data_2010 = table7_string_converter(pop.splitter(county_data_2010))
county_data_2000 = table7_string_converter(pop.splitter(county_data_2000))
county_data_2010_number = pop.string_converter(
"county", pop.splitter(county_data_2010_number))
county_data_2000_number = pop.string_converter(
"county", pop.splitter(county_data_2000_number))
table7 = make_table7(county, county_data_2010, county_data_2000,
county_data_2010_number, county_data_2000_number,
county_data_1990, county_data_1980)
figures.append(table7)
figure4 = make_figure4(county, county_data_2010, county_data_2000,
county_data_1990, county_data_1980)
figures.append(figure4)
plt.close()
plt.cla()
"""
#Gets written to Excel
writer = pd.ExcelWriter('output.xlsx', engine='xlsxwriter')
workbook = writer.book
worksheet = workbook.add_worksheet('Table 7')
writer.sheets['Table 7'] = worksheet
worksheet.write_string(0, 0, table7.name)
table7.to_excel(writer, sheet_name='Table 7', startrow=1, startcol=0)
"""
return figures
def get_nominal(county, base_year, fips):
#Read file for data prior to 2000
income_data = pd.read_excel("Historical_Median_Income_TXcounties1969-89.xlsx")
income_data = income_data.loc[income_data['Name'] == county]
#Only a figure for the base year is needed.
#Source of this data is determined by these conditional statements looking
#For both County Specified and Base Year
if(base_year == float(2000)):
if(county == "State of Texas"):
nominal = api.api_request("https://api.census.gov/data/2000/sf3?get=HCT012001,NAME&for=state:48&key=" + p.census_api_key)
nominal = pop.string_converter("state", pop.splitter(nominal))
else:
nominal = api.api_request(p.frankenstein(p.nominal2000, fips))
nominal = pop.string_converter("county", pop.splitter(nominal))
nominal = float(nominal["HCT012001"])
elif(base_year < float(2000)):
nominal = income_data[base_year].values[0]
else:
if(county == "State of Texas"):
nominal = api.api_request("https://api.census.gov/data/"+ str(base_year) +"/acs/acs1?get=NAME,B19013_001E&for=state:48&key=" + p.census_api_key)
nominal = pop.string_converter("state", pop.splitter(nominal))
else:
nominal = api.api_request(p.acs_frankenstein(str(base_year), "B19013_001E", fips, "1"))
if(nominal is None):
return None
else:
nominal = popt.table3_string_converter(pop.splitter(nominal))
nominal = float(nominal["B19013_001E"])
return nominal
def base_year_data(fips):
called_data = api_request(p.frankenstein(p.table1_call_2010, fips))
called_data = pop.string_converter('county', pop.splitter(called_data))
#print(called_data)
converted_data = float(called_data['P001001'])
return converted_data
def main(fips, county):
figures = []
data_TDC = pd.read_csv(p.table3_excel, sep=r'\,|\t', engine='python')
#Make call to API for data
population_ACS = api_request(p.frankenstein(p.table_3_call, fips))
data_ACS = table3_string_converter(pop.splitter(population_ACS))
table3 = make_table3(data_ACS, data_TDC, 2012, fips)
figures.append(table3)
plt.close()
table4 = make_table4(data_TDC, fips, county)
figures.append(table4)
"""
print(table3)
print(table4)
"""
figure3 = make_figure3(data_TDC, fips, county)
figures.append(figure3)
return figures
def make_table5(fips, county):
group_quarter_data = group_quarters_array(fips)
column_2 = table_5_other_columns(fips, "2011")
column_3 = table_5_other_columns(fips, "2012")
data_2010 = api.api_request(p.frankenstein(p.table1_call_2010, fips))
data_2010 = pop.string_converter("county", pop.splitter(data_2010))
total_population_2010 = float(data_2010["P001001"])
percent_of_population_2010 = "{0:.2%}".format(float(group_quarter_data[0])/total_population_2010)
group_quarter_data.append(percent_of_population_2010)
table5 = pd.DataFrame({"2010": group_quarter_data,
"2011": column_2,
"2012": column_3})
table5 = table5.rename(index={0: "Total",
1: "Correctional Institutions",
2: "Juvenile Institutions",
3: "Nursing Homes",
4: "Other Institutions",
5: "Total Institutional",
6: "College Dorms",
7: "Military Quarters",
8: "Other Non-Institutional",
9: "Total Non-Institutional",
10: "Percent of Total Population"})
table5.name = "Recent Trends in Group Quarters Population for " + county + " County"
return table5
def group_quarters_array(fips):
#Make calls to the API
#2010
total_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_total, fips))
correctional_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_correctional, fips))
juvenile_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_juvenile, fips))
nursing_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_nursing, fips))
other_institutional_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_otherinstitutional, fips))
total_institutional_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_totalinstitutional, fips))
dorms_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_dorms, fips))
military_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_military, fips))
other_noninstitutional_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_othernoninstitutional, fips))
total_noninstitutional_2010 = api.api_request(p.frankenstein(p.table_5_call_2010_totalnoninstitutional, fips))
#Turn these requests into something readable
total_2010 = pop.string_converter("county", pop.splitter(total_2010))
correctional_2010 = pop.string_converter("county", pop.splitter(correctional_2010))
juvenile_2010 = pop.string_converter("county", pop.splitter(juvenile_2010))
nursing_2010 = pop.string_converter("county", pop.splitter(nursing_2010))
other_institutional_2010 = pop.string_converter("county", pop.splitter(other_institutional_2010))
total_institutional_2010 = pop.string_converter("county", pop.splitter(total_institutional_2010))
dorms_2010 = pop.string_converter("county", pop.splitter(dorms_2010))
military_2010 = pop.string_converter("county", pop.splitter(military_2010))
other_noninstitutional_2010 = pop.string_converter("county", pop.splitter(other_noninstitutional_2010))
total_noninstitutional_2010 = pop.string_converter("county", pop.splitter(total_noninstitutional_2010))
#Make an array
group_quarter_data = [total_2010["P029026"], correctional_2010["PCT020003"],
juvenile_2010["PCT020010"], nursing_2010["P042005"],
other_institutional_2010["PCT020015"],
total_institutional_2010["P042002"],
dorms_2010["P042008"], military_2010["P042009"],
other_noninstitutional_2010["PCT020026"],
total_noninstitutional_2010["P029028"]]
return group_quarter_data
def main(fips):
figures = []
#variables containing state data
state_data_2010 = api_request(p.table1_call_state_2010)
print("this is the data after api requst: ", type(state_data_2010))
state_data_2010 = string_converter('state', splitter(state_data_2010))
state_population_2010 = float(state_data_2010['P001001'])
state_data_2000 = api_request(p.table1_call_state_2000)
print("TEST TEST TEST:", state_data_2000)
state_data_2000 = string_converter('state', splitter(state_data_2000))
state_population_2000 = float(state_data_2000['P001001'])
state_population_1970 = find_population("000", 48, 1970, data)
state_population_1980 = find_population("000", 48, 1980, data)
state_population_1990 = find_population("000", 48, 1990, data)
#variables containing census data
data_2010 = api_request(p.frankenstein(p.table1_call_2010, fips))
data_2010 = string_converter('county', splitter(data_2010))
population_2010 = float(data_2010['P001001'])
data_2000 = api_request(p.frankenstein(p.table1_call_2000, fips))
data_2000 = string_converter('county', splitter(data_2000))
population_2000 = float(data_2000['P001001'])
population_1970 = find_population(data_2010['county'], data_2010['state'],
1970, data)
population_1980 = find_population(data_2010['county'], data_2010['state'],
1980, data)
population_1990 = find_population(data_2010['county'], data_2010['state'],
1990, data)
figure1 = make_figure1(population_1970, population_1980, population_1990,
population_2000, population_2010, data_2010)
figures.append(figure1)
plt.close()
table1 = make_table1(state_population_2010, state_population_2000,
state_population_1990, state_population_1980,
state_population_1970, population_2010,
population_2000, population_1990, population_1980,
population_1970, data_2010)
figures.append(table1)
figure2 = make_figure2(state_population_2010, state_population_2000,
state_population_1990, state_population_1980,
state_population_1970, population_2010,
population_2000, population_1990, population_1980,
population_1970, data_2010)
plt.close()
figures.append(figure2)
table2 = make_table2(state_population_2010, state_population_2000,
state_population_1990, state_population_1980,
state_population_1970, population_2010,
population_2000, population_1990, population_1980,
population_1970, data_2010)
figures.append(table2)
"""
#Prints to Console
figure1.show()
print(table1)
figure2.show()
print(table2)
#Gets written to Excel
writer = pd.ExcelWriter('output.xlsx', engine='xlsxwriter')
workbook = writer.book
worksheet = workbook.add_worksheet('Results')
writer.sheets['Results'] = worksheet
worksheet.write_string(0, 0, table1.name)
table1.to_excel(writer, sheet_name='Results', startrow=1, startcol=0)
worksheet.write_string(table1.shape[0] + 4, 0, table2.name)
table2.to_excel(writer,sheet_name='Results', startrow=table1.shape[0]+5, startcol=0)
#figure1.to_excel('output.xlsx', sheet_name='figures', engine='xlsxwriter')
writer.save()
"""
return figures