def bus_pul():
logging.info('%s Business Pulse Started', datetime.datetime.now())
bus_pul = business_pulse(
math.floor(
(datetime.datetime.now() -
datetime.datetime.strptime('2020-08-13', '%Y-%m-%d')).days / 7))
qa = {
'20-6': 'Business is closed',
'19-7': 'Expect to close in the next 6 months',
'6-1': 'Increased number of employees last week',
'6-2': 'Decreased number of employees last week',
'8-1': 'Increased hours paid last week',
'8-2': 'Decreased hours paid last week',
}
locations = ['Sacramento-Roseville-Folsom, CA MSA', 'CA', 'National']
current_status = compare_questions_locations(
bus_pul[bus_pul.date == bus_pul.date.max()], qa, locations)
sacramento_status = qa_for_loc(bus_pul,
qa,
locations[0],
title='Sacramento Responses')
decreased_employment = qa_by_loc(bus_pul,
'6-2',
locations,
title='Decreased Employment')
decreased_hours = qa_by_loc(bus_pul,
'8-2',
locations,
title='Decreased Hours')
change_in_employment = qa_diff_by_loc(
bus_pul,
'6-1',
'6-2',
locations,
title=
"Businesses increasing employment less businesses decreasing employment"
)
change_in_hours = qa_diff_by_loc(
bus_pul,
'8-1',
'8-2',
locations,
title="Businesses increasing hours less businesses decreasing hours")
closures = stacked_by_loc(bus_pul,
dict(list(qa.items())[:2]),
locations,
title='Business Closures')
business_pulse_panel = Panel(child=layout(
[[current_status, sacramento_status,
padding()], [change_in_employment, change_in_hours,
padding()],
[decreased_employment, decreased_hours,
padding()], [closures, padding()]],
sizing_mode='stretch_width'),
title='Small Business Pulse')
logging.info('%s Business Pulse Completed', datetime.datetime.now())
return business_pulse_panel
def retail_sales():
logging.info('%s Retail Sales Started', datetime.datetime.now())
retail_sales = [
'RSHPCS', 'RSGASS', 'RSCCAS', 'RSSGHBMS', 'RSGMS', 'RSMSR', 'RSNSR',
'RSFSDP', 'RSMVPD', 'RSFHFS', 'RSEAS', 'RSBMGESD', 'RSDBS'
]
retail_sales_chart = fred_chart(retail_sales,
'2019-01-01',
transformation='index',
transform_date=rs,
title="Change in retail sales")
for i in retail_sales_chart.legend[0]._property_values['items']:
i._property_values['label']['value'] = i._property_values['label'][
'value'][22:]
retail_sales = [
'RSHPCS', 'RSGASS', 'RSCCAS', 'RSSGHBMS', 'RSGMS', 'RSMSR', 'RSNSR',
'RSFSDP', 'RSMVPD', 'RSFHFS', 'RSEAS', 'RSBMGESD', 'RSDBS'
]
compare_industries = category_compare(retail_sales,
'Retail Sales',
nameoffset=22)
retail_bar = bar_chart(retail_sales,
cy,
title='Retail Sales by Sector',
lstrip=22,
rstrip=0,
legend=False)
retail_bar_difference = bar_chart(retail_sales,
cy,
transformation='difference',
transform_date=rs,
title='Change in Retail Sales',
lstrip=22,
rstrip=0,
legend=False,
net=True)
retail_sales = Panel(child=layout(
[[retail_sales_chart, compare_industries,
padding()], [retail_bar, retail_bar_difference,
padding()]],
sizing_mode='stretch_width'),
title='Retail Sales')
logging.info('%s Retail Sales Completed', datetime.datetime.now())
return retail_sales
def miscellaneous():
logging.info('%s Misc Started', datetime.datetime.now())
business_applications = fred_chart([
'BUSAPPWNSAUSYY', 'BUSAPPWNSACAYY', 'WBUSAPPWNSACAYY',
'HBUSAPPWNSACAYY'
],
'2020-01-01',
title="Weekly Business Applications",
legend="top_left")
url = 'https://www.tsa.gov/coronavirus/passenger-throughput'
tsa = pd.read_html(requests.get(url).text, header=0)[0]
tsa['Date'] = pd.to_datetime(tsa['Date'], format='%m/%d/%Y')
#tsa['combined']=tsa['2021'].fillna(tsa['2020'])
years = ['2020', '2021', '2022']
for year in years:
tsa[year] = tsa[year] / tsa['2019']
melted = pd.melt(tsa,
id_vars=['Date'],
value_vars=['2020', '2021', '2022'])
melted['month'] = pd.DatetimeIndex(melted.Date).month
melted['day'] = pd.DatetimeIndex(melted.Date).day
melted['year'] = melted.variable
melted['date'] = pd.to_datetime(melted[['year', 'month', 'day']])
melted.sort_values(by='date', inplace=True)
#tsa['YoY']=tsa['combined']/tsa['2019']
tsa_chart = chart(melted[['date', 'value']],
date='date',
title='TSA Travelers vs 2019 levels',
units='percent')
housing_starts = fred_chart(['PERMIT', 'HOUST', 'SACR906BPPRIVSA'],
'1990-01-01',
title='Housing Starts')
miscellaneous = Panel(child=layout([
[business_applications, tsa_chart,
padding()],
[housing_starts, padding()],
],
sizing_mode='stretch_width'),
title='Misc')
logging.info('%s Misc Completed', datetime.datetime.now())
return miscellaneous
def weekly_claims():
logging.info('%s Weekly Claims Started', datetime.datetime.now())
recessions = 5
national_initial_claims = adder_chart(
'ICNSA',
pua_data.groupby(by='Rptdate').sum()[[
'PUA IC'
]].rename(columns={'PUA IC': 'value'}),
recessions,
years=3)
ca_initial_claims = adder_chart('CAICLAIMS',
pua_data[pua_data.State == 'CA'][[
'Rptdate', 'PUA IC'
]].rename(columns={'PUA IC': 'value'}),
recessions,
years=3)
national_continuing_claims = adder_chart(
'CCNSA',
pua_data.groupby(by='Rptdate').sum(min_count=1)[[
'PUA CC'
]].rename(columns={'PUA CC': 'value'}),
recessions,
years=3)
ca_continuing_claims = adder_chart('CACCLAIMS',
pua_data[pua_data.State == 'CA'][[
'Rptdate', 'PUA CC'
]].rename(columns={'PUA CC': 'value'}),
recessions,
years=3)
weekly_claims = Panel(child=layout(
[[national_initial_claims, ca_initial_claims,
padding()],
[national_continuing_claims, ca_continuing_claims,
padding()]],
sizing_mode='stretch_width'),
title="Weekly Claims")
logging.info('%s Weekly Claims Completed', datetime.datetime.now())
return weekly_claims
def overall_trends():
logging.info('%s Overall Trends Started', datetime.datetime.now())
series = ['RSAFS', 'IPMAN', 'PAYEMS', 'DGORDER']
national_trends = fred_chart(series,
'2019-01-01',
transformation='index',
transform_date=rs,
title='Employment, Manufacturing, and Sales')
employment = fred_chart(['PAYEMS'], '2000-01-01', title='Employment')
employment.legend.location = 'top_left'
unemployment = fred_chart(['UNRATENSA', 'CAURN', 'CASACR5URN'],
'2000-01-01',
title='Unemployment')
unemployment.legend.location = 'top_left'
overall_trends = Panel(child=layout(
[[national_trends, employment, padding()], [unemployment,
padding()]],
sizing_mode='stretch_width'),
title='Overall Trends')
logging.info('%s Overall Trends Completed', datetime.datetime.now())
return overall_trends
def personal_income():
logging.info('%s Personal Income Started', datetime.datetime.now())
income = ['W209RC1', 'PCTR', 'PIROA', 'A048RC1', 'A041RC1']
income_change = bar_chart(income,
cy,
transformation='difference',
transform_date=rs,
title='Change in Income',
net=True,
legend='top_left')
income_change.legend[0].items[3].label['value'] = 'Rental income'
income_change.legend[0].items[4].label['value'] = '''Proprietors' income'''
#contra_income=['A061RC1','W055RC1']
expenses = ['PCEDG', 'PCEND', 'PCES', 'B069RC1', 'W211RC1']
expenses_change = bar_chart(expenses,
cy,
transformation='difference',
transform_date=rs,
title='Change in Expenses',
net=True)
for i in range(0, 3):
expenses_change.legend[0].items[i].label[
'value'] = expenses_change.legend[0].items[i].label['value'][35:]
for i in range(3, 5):
expenses_change.legend[0].items[i].label[
'value'] = expenses_change.legend[0].items[i].label['value'][
9:].title()
high_level = ['W209RC1', 'DSPI', 'A068RC1', 'PMSAVE']
high_level_chart = fred_chart(high_level,
cy,
title='Income, Expenditure, and Saving')
personal_income_charts = Panel(child=layout(
[[income_change, expenses_change,
padding()], [high_level_chart, padding()]],
sizing_mode='stretch_width'),
title='Personal Income and Expenses')
logging.info('%s Personal Income Completed', datetime.datetime.now())
return personal_income_charts
def jobs_report():
logging.info('%s Jobs Report Started', datetime.datetime.now())
recessions = 5
national_jobs = historical_comparison(
historical_data('UNRATENSA', recessions))
state_jobs = historical_comparison(historical_data('CAURN', recessions))
local_jobs = historical_comparison(
historical_data('CASACR5URN', recessions))
national_jobs.y_range = state_jobs.y_range
local_jobs.y_range = state_jobs.y_range
permanent_unemployment = historical_comparison(
historical_data('LNU03025699', recessions))
mid_term_unemployment = historical_comparison(
historical_data('UEMP15T26', recessions))
mid_term_unemployment.y_range = permanent_unemployment.y_range
long_term_unemployment = historical_comparison(
historical_data('UEMP27OV', recessions))
long_term_unemployment.y_range = permanent_unemployment.y_range
occupations = [
'LNU02032201', 'LNU02032204', 'LNU02032205', 'LNU02032208',
'LNU02032212'
]
employment_by_occupation = category_compare(
occupations, 'Change in Employment by Occupation', nameoffset=19)
employment_by_occupation.legend.location = 'bottom_left'
industries = [
'LNU02034560', 'LNU02034561', 'LNU02034562', 'LNU02034563',
'LNU02034566', 'LNU02034570', 'LNU02034571', 'LNU02034572',
'LNU02034573', 'LNU02034574', 'LNU02034575', 'LNU02034576',
'LNU02034579'
]
industry_names = [
'Agriculture, forestry, fishing, and hunting',
'Mining, quarrying, and oil and gas extraction', 'Construction',
'Manufacturing', 'Wholesale and Retail Trade',
'Transportation and Utilities', 'Information', 'Financial Services',
'Professional and Business Services', 'Education and health services',
'Leisure and hospitality', 'Other services', 'Public administration'
]
employment_by_industry = bls_compare(industries, industry_names,
'Change in Employment by Industry')
employment_by_industry.legend.location = 'bottom_right'
labor_force_categories = ['CLF16OV', 'LNS11000002', 'LNS11000001']
labor_force = fred_chart(labor_force_categories,
cy,
transformation='index',
transform_date=rs,
title='Labor Force Level')
industries = [
'CEU1000000001', 'CEU2000000001', 'CEU3000000001', 'CEU4000000001',
'CEU5000000001', 'CEU5500000001', 'CEU6000000001', 'CEU6500000001',
'CEU7000000001', 'CEU8000000001', 'CEU9091000001', 'CEU9092000001',
'CEU9093000001'
]
industry_compare = fred_chart(industries,
'2019-01-01',
transformation='index',
transform_date=rs,
title="Change in Employment",
lstrip=14)
industry_bar_difference = bar_chart(industries,
cy,
transformation='difference',
transform_date=rs,
title='Change in Employment',
lstrip=14,
rstrip=0,
legend=False,
net=True)
national_change = historical_comparison(historical_data('PAYEMS',
recessions,
transform='index'),
title='Change in Total Employment')
state_change = historical_comparison(
historical_data('CANA', recessions, transform='index'),
title='Change in California Employment')
state_change.y_range = national_change.y_range
local_change = historical_comparison(
historical_data('SACR906NA', recessions, transform='index'),
title='Change in Sac Metro Employment')
local_change.y_range = national_change.y_range
jobs_report = Panel(child=layout(
[[national_change, state_change, local_change,
padding()], [national_jobs, state_jobs, local_jobs,
padding()],
[
permanent_unemployment, mid_term_unemployment,
long_term_unemployment,
padding()
],
[
employment_by_occupation, employment_by_industry, labor_force,
padding()
], [industry_compare, industry_bar_difference,
padding()]],
sizing_mode='stretch_width'),
title='Jobs Report')
logging.info('%s Jobs Report Completed', datetime.datetime.now())
return jobs_report
def adp_charts():
logging.info('%s ADP Charts Started', datetime.datetime.now())
adp_sectors = ['NPPSPT', 'NPPGPT']
adp_sectors = ['NPPMNF', 'NPPCON', 'NPPTTU', 'NPPBUS', 'NPPFIN']
adp_sizes = ['NPPTS1', 'NPPTS2', 'NPPTM', 'NPPTL1', 'NPPTL2']
sector_index = fred_chart(
adp_sectors,
cy,
transformation='index',
transform_date=rs,
title='National Nonfarm Private Payroll for Select Sectors (ADP)',
lstrip=16,
rstrip=19)
sector_index_bar = bar_chart(
adp_sectors,
cy,
title='National Nonfarm Private Payroll for Select Sectors (ADP)',
lstrip=16,
rstrip=19)
sector_difference_bar = bar_chart(
adp_sectors,
cy,
transformation='difference',
transform_date=rs,
title='Change in Payroll for Select Sectors (ADP)',
lstrip=16,
rstrip=19)
size_index = fred_chart(
adp_sizes,
cy,
transformation='index',
transform_date=rs,
title='National Nonfarm Private Payroll by Business Size (ADP)',
lstrip=16)
size_index_bar = bar_chart(
adp_sizes,
cy,
title='National Nonfarm Private Payroll by Business Size (ADP)',
lstrip=16)
size_difference_bar = bar_chart(
adp_sizes,
cy,
transformation='difference',
transform_date=rs,
title='Change in Payroll by Business Size (ADP)',
lstrip=16)
copyright_note = Div(
text=
"<p>Copyright Automatic Data Processing, Inc. Retrieved from FRED, Federal Reserve Bank of St. Louis</p>"
)
adp_charts = Panel(child=layout(
[[sector_index, size_index, padding()],
[sector_index_bar, size_index_bar,
padding()], [sector_difference_bar, size_difference_bar,
padding()], [copyright_note]],
sizing_mode='stretch_width'),
title='ADP Monthly Data')
logging.info('%s ADP Charts Completed', datetime.datetime.now())
return adp_charts