Economy, Calculations, Data
Potus, Incumbent Elec. College Percentage Prediction
Time for Change model, (my post)
GDP is taken as annualized quarterly growth rate, quarter growth compared to previous quarter, annualized.
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1945, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
df = data.DataReader(['GDPC1'], 'fred', start, end)
df['growann'] = ( ( (1+df.pct_change())**4 )-1.0 )*100.0
#print (df[pd.DatetimeIndex(df.index).year == 1984]['growann'])
print (df['growann'].tail(5))
# look at Q2, 04-01 date
DATE
2019-10-01 2.365628
2020-01-01 -4.955763
2020-04-01 -31.383181
2020-07-01 33.441306
2020-10-01 4.091780
Name: growann, dtype: float64
from io import StringIO
import statsmodels.formula.api as smf
import pandas as pd
s="""year,gdp_growth,net_approval,two_terms,incumbent_vote
2012,1.3,-0.8,0,52
2008,1.3,-37,1,46.3
2004,2.6,-0.5,0,51.2
2000,8,19.5,1,50.3
1996,7.1,15.5,0,54.7
1992,4.3,-18,1,46.5
1988,5.2,10,1,53.9
1984,7.1,20,0,59.2
1980,-7.9,-21.7,0,44.7
1976,3,5,1,48.9
1972,9.8,26,0,61.8
1968,7,-5,1,49.6
1964,4.7,60.3,0,61.3
1960,-1.9,37,1,49.9
1956,3.2,53.5,0,57.8
1952,0.4,-27,1,44.5
1948,7.5,-6,1,52.4
"""
df = pd.read_csv(StringIO(s))
regr = 'incumbent_vote ~ gdp_growth + net_approval + two_terms'
results = smf.ols(regr, data=df).fit()
print ('R^2', results.rsquared)
conf = results.conf_int()
net_approv = -10.0; gdp_growth = 0.0
pred = [1., gdp_growth, net_approv, 0]
print (np.dot(pred, conf), np.dot(pred, results.params))
R^2 0.9011858911763367
[49.14454875 51.75431018] 50.4494294659622
The Cycle
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1970, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
fig, axs = plt.subplots(2)
df = data.DataReader(['GDPC1'], 'fred', start, end)
df['gdpyoy'] = (df.GDPC1 - df.GDPC1.shift(4)) / df.GDPC1.shift(4) * 100.0
df['gdpyoy'].plot(ax=axs[0],title="GDP and Inflation YoY")
axs[0].axvspan('01-11-1973', '01-03-1975', color='y', alpha=0.5, lw=0)
axs[0].axvspan('01-07-1981', '01-11-1982', color='y', alpha=0.5, lw=0)
axs[0].axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
axs[0].axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
axs[0].axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
print (df[['gdpyoy']].tail(6))
df = data.DataReader(['CPIAUCNS'], 'fred', start, end)
df['infyoy'] = (df.CPIAUCNS - df.CPIAUCNS.shift(12)) / df.CPIAUCNS.shift(12) * 100.0
df['infyoy'].plot(ax=axs[1])
axs[1].axvspan('01-11-1973', '01-03-1975', color='y', alpha=0.5, lw=0)
axs[1].axvspan('01-07-1981', '01-11-1982', color='y', alpha=0.5, lw=0)
axs[1].axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
axs[1].axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
axs[1].axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
print (df[['infyoy']].tail(6))
plt.savefig('cycle.png')
gdpyoy
DATE
2019-10-01 2.338885
2020-01-01 0.319261
2020-04-01 -9.032775
2020-07-01 -2.848345
2020-10-01 -2.386694
2021-01-01 0.397284
infyoy
DATE
2020-12-01 1.362005
2021-01-01 1.399770
2021-02-01 1.676215
2021-03-01 2.619763
2021-04-01 4.159695
2021-05-01 4.992707
Inflation Expection
Data comes from the University of Michigan survey.
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('http://www.sca.isr.umich.edu/files/tbcpx1px5.csv',skiprows=4,header=None)
df1 = df[[0,1,3,5]]
df1 = df1.dropna()
df1.columns = ['Mon','Year','Next Year','Next 5 Years']
df1['sdate'] = df1.apply(lambda x: x.Mon + "-" + str(int(x['Year'])),axis=1)
df1['date'] = pd.to_datetime(df1.sdate)
df1 = df1.set_index('date')
df1[['Next Year','Next 5 Years']].plot()
plt.savefig('infexp.png')
Wages and Unemployment
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1986, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['PAYEMS']
df = data.DataReader(cols, 'fred', start, end)
df['nfpyoy'] = (df.PAYEMS - df.PAYEMS.shift(12)) / df.PAYEMS.shift(12) * 100.0
print (df.tail(7))
df.nfpyoy.plot()
plt.grid(True)
plt.axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.title('Non-Farm Payroll YoY Change %')
plt.savefig('nfp.png')
PAYEMS nfpyoy
DATE
2020-12-01 142503 -6.198040
2021-01-01 142736 -6.239079
2021-02-01 143272 -6.065315
2021-03-01 144057 -4.496818
2021-04-01 144326 10.882676
2021-05-01 144909 8.959051
2021-06-01 145759 5.745067
import pandas as pd, datetime
from pandas_datareader import data
start=datetime.datetime(1950, 1, 1)
end=datetime.datetime(2019, 11, 1)
cols = ['ECIWAG']
df3 = data.DataReader(cols, 'fred', start, end)
df3 = df3.dropna()
df3['ECIWAG2'] = df3.shift(4).ECIWAG
df3['wagegrowth'] = (df3.ECIWAG-df3.ECIWAG2) / df3.ECIWAG2 * 100.
print (df3['wagegrowth'].tail(4))
df3['wagegrowth'].plot(title='Wage Growth')
plt.savefig('wages.png')
DATE
2019-01-01 2.956785
2019-04-01 3.012048
2019-07-01 2.910448
2019-10-01 2.965159
Name: wagegrowth, dtype: float64
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1995, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['ICSA']
df = data.DataReader(cols, 'fred', start, end)
df.ICSA.plot()
print (df.tail(4))
plt.title("Initial Unemployment Claims")
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('icsa.png')
ICSA
DATE
2021-06-12 418000
2021-06-19 416000
2021-06-26 371000
2021-07-03 373000
Calculation is based on [2]
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1986, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['LNS12032194','UNEMPLOY','NILFWJN','LNS12600000','CLF16OV','UNRATE','U6RATE']
df = data.DataReader(cols, 'fred', start, end)
df['REAL_UNEMP_LEVEL'] = df.LNS12032194*0.5 + df.UNEMPLOY + df.NILFWJN
df['REAL_UNRATE'] = (df.REAL_UNEMP_LEVEL / df.CLF16OV) * 100.0
pd.set_option('display.max_columns', None)
df1 = df.loc[df.index > '2005-01-01']
df1[['UNRATE','U6RATE','REAL_UNRATE']].plot()
plt.title('Unemployment Rate')
print (df1[['UNRATE','U6RATE','REAL_UNRATE','REAL_UNEMP_LEVEL']].tail(5))
plt.savefig('unemploy.png')
UNRATE U6RATE REAL_UNRATE REAL_UNEMP_LEVEL
DATE
2021-02-01 6.2 11.1 12.451704 19949.0
2021-03-01 6.0 10.7 12.128327 19473.0
2021-04-01 6.1 10.4 11.852126 19080.5
2021-05-01 5.8 10.2 11.527325 18551.5
2021-06-01 5.9 9.8 11.314143 18225.5
PMI
import quandl, os, datetime
from datetime import timedelta
today = datetime.datetime.now()
start=datetime.datetime(1985, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
today = datetime.datetime.now()
df = quandl.get("ISM/MAN_PMI-PMI-Composite-Index",
returns="pandas",
start_date=start.strftime('%Y-%m-%d'),
end_date=today.strftime('%Y-%m-%d'),
authtoken=open(".quandl").read())
print (df['PMI'].tail(4))
df['PMI'].plot()
plt.axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('pmi.png')
Date
2021-03-01 64.7
2021-04-01 60.7
2021-05-01 61.2
2021-06-01 60.6
Name: PMI, dtype: float64
GDP vs ISM
import pandas as pd, datetime
from pandas_datareader import data
import quandl
today = datetime.datetime.now()
start=datetime.datetime(1992, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['GDPC1']
df = data.DataReader(cols, 'fred', start, end)
df['gdpyoy'] = (df.GDPC1 - df.GDPC1.shift(4)) / df.GDPC1.shift(4) * 100.0
df2 = quandl.get("ISM/MAN_PMI-PMI-Composite-Index",
returns="pandas",
start_date=start.strftime('%Y-%m-%d'),
end_date=end.strftime('%Y-%m-%d'),
authtoken=open(".quandl").read())
plt.figure(figsize=(12,5))
ax1 = df2.PMI.plot(color='blue', grid=True, label='ISM')
ax2 = df.gdpyoy.plot(color='red', grid=True, label='GDP',secondary_y=True)
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
plt.legend(h1+h2, l1+l2, loc=2)
plt.savefig('gdp-ism.png')
Profits YoY
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(2000, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['CPROFIT']
df = data.DataReader(cols, 'fred', start, end)
df['cpyoy'] = (df.CPROFIT - df.CPROFIT.shift(4)) / df.CPROFIT.shift(4) * 100.0
print (df.tail(4))
df.cpyoy.plot()
plt.grid(True)
plt.savefig('profit.png')
CPROFIT cpyoy
DATE
2020-04-01 1826.137 -19.309944
2020-07-01 2325.730 3.528294
2020-10-01 2294.300 -0.734314
2021-01-01 2294.052 12.728166
Dollar
import pandas as pd, datetime, time as timelib
import urllib.request as urllib2, io
end = datetime.datetime.now()
start = datetime.datetime(1980, 1, 1)
start = int(timelib.mktime(start.timetuple()))
end = int(timelib.mktime(end.timetuple()))
base_fin_url = "https://query1.finance.yahoo.com/v7/finance/download"
url = base_fin_url + "/DX-Y.NYB?period1=" + str(start) + "&period2=" + str(end) + "&interval=1d&events=history&includeAdjustedClose=true"
r = urllib2.urlopen(url).read()
file = io.BytesIO(r)
df = pd.read_csv(file,index_col='Date',parse_dates=True)['Adj Close']
print (df.tail(4))
m,s = df.mean(),df.std()
print (np.array([m-s,m+s]).T)
df.tail(1000).plot()
plt.grid(True)
plt.savefig('dollar.png')
Date
2021-07-06 92.550003
2021-07-07 92.639999
2021-07-08 92.360001
2021-07-09 92.129997
Name: Adj Close, dtype: float64
[ 80.67397112 111.34537021]
Difference Between Wage Growth YoY and Payrolls (Hiring)
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1986, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['PAYEMS','AHETPI']
df = data.DataReader(cols, 'fred', start, end)
df['nfpyoy'] = (df.PAYEMS - df.PAYEMS.shift(12)) / df.PAYEMS.shift(12) * 100.0
df['wageyoy'] = (df.AHETPI - df.AHETPI.shift(12)) / df.AHETPI.shift(12) * 100.0
df[['wageyoy','nfpyoy']].plot()
plt.axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
print (df['wageyoy'].tail(5))
print (df['nfpyoy'].tail(5))
plt.savefig('pay-wage.png')
DATE
2021-02-01 5.217028
2021-03-01 4.637681
2021-04-01 1.152623
2021-05-01 2.279088
2021-06-01 3.673799
Name: wageyoy, dtype: float64
DATE
2021-02-01 -6.065315
2021-03-01 -4.496818
2021-04-01 10.882676
2021-05-01 8.959051
2021-06-01 5.745067
Name: nfpyoy, dtype: float64
Total Market Cap / GDP
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1995, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['WILL5000IND']
df = data.DataReader(cols, 'fred', start, end)
df.plot()
print (df.tail(4))
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('wilshire.png')
WILL5000IND
DATE
2021-06-28 213.97
2021-06-29 213.94
2021-06-30 214.11
2021-07-01 215.21
Junk Bond Yields
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1980, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['BAMLH0A2HYBEY']
df = data.DataReader(cols, 'fred', start, end)
print (df.tail(6))
df.plot()
plt.plot(df.tail(1).index, df.tail(1),'ro')
plt.axvspan('2001-03-03', '2001-10-27', color='y', alpha=0.5, lw=0)
plt.axvspan('2007-12-22', '2009-05-09', color='y', alpha=0.5, lw=0)
plt.savefig('junkbond.png')
BAMLH0A2HYBEY
DATE
2021-05-10 4.49
2021-05-11 4.64
2021-05-12 4.69
2021-05-13 4.70
2021-05-14 4.64
2021-05-17 4.66
Yield Curve, Rates
10 Year Treasury Yield - 3 Month Bills
import pandas as pd, datetime
from pandas_datareader import data
pd.set_option('display.max_columns', 10)
today = datetime.datetime.now()
start=datetime.datetime(1980, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['DGS10','DGS3MO']
df = data.DataReader(cols, 'fred', start, end)
df['Yield Curve'] = df.DGS10 - df.DGS3MO
print (df.tail(6))
plt.plot(df.tail(1).index, df.tail(1)['Yield Curve'],'ro')
df['Yield Curve'].plot()
plt.axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('yield-curve.png')
DGS10 DGS3MO Yield Curve
DATE
2021-06-24 1.49 0.05 1.44
2021-06-25 1.54 0.06 1.48
2021-06-28 1.49 0.05 1.44
2021-06-29 1.49 0.04 1.45
2021-06-30 1.45 0.05 1.40
2021-07-01 1.48 0.05 1.43
3-Month Treasury Constant Maturity Minus Federal Funds Rate
import pandas as pd, datetime
from pandas_datareader import data
pd.set_option('display.max_columns', 10)
today = datetime.datetime.now()
start=datetime.datetime(1999, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['DGS3MO','FEDFUNDS','T3MFF']
df = data.DataReader(cols, 'fred', start, end)
df = df.interpolate()
print (df.tail(6))
df.plot()
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('t3mff.png')
DGS3MO FEDFUNDS T3MFF
DATE
2021-06-24 0.05 0.08 -0.05
2021-06-25 0.06 0.08 -0.04
2021-06-28 0.05 0.08 -0.05
2021-06-29 0.04 0.08 -0.06
2021-06-30 0.05 0.08 -0.03
2021-07-01 0.05 0.08 -0.05
Gold and 10 Year Treasuries
from pandas_datareader import data
import datetime
today = datetime.datetime.now()
start=datetime.datetime(2000, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
df = data.DataReader(['DGS10', 'GOLDAMGBD228NLBM'], 'fred', start, end)
print (df.tail(5))
ax1 = df.DGS10.plot(color='blue', grid=True, label='10Y')
ax2 = df.GOLDAMGBD228NLBM.plot(color='red', grid=True, label='GOLD',secondary_y=True)
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
plt.legend(h1+h2, l1+l2, loc=2)
plt.savefig('10yrgld.png')
DGS10 GOLDAMGBD228NLBM
DATE
2021-06-28 1.49 1774.25
2021-06-29 1.49 1769.60
2021-06-30 1.45 1757.80
2021-07-01 1.48 1774.00
2021-07-02 NaN 1783.50
VIX
import pandas as pd, datetime, time as timelib
import urllib.request as urllib2, io
end = datetime.datetime.now()
start=datetime.datetime(2000, 1, 1)
start = int(timelib.mktime(start.timetuple()))
end = int(timelib.mktime(end.timetuple()))
base_fin_url = "https://query1.finance.yahoo.com/v7/finance/download"
url = base_fin_url + "/^VIX?period1=" + str(start) + "&period2=" + str(end) + "&interval=1d&events=history&includeAdjustedClose=true"
r = urllib2.urlopen(url).read()
file = io.BytesIO(r)
df = pd.read_csv(file,index_col='Date',parse_dates=True)['Adj Close']
df.plot()
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
print (df.tail(7))
plt.plot(df.tail(1).index, df.tail(1),'ro')
plt.savefig('vix.png')
Date
2021-06-30 15.830000
2021-07-01 15.480000
2021-07-02 15.070000
2021-07-06 16.440001
2021-07-07 16.200001
2021-07-08 19.000000
2021-07-09 16.180000
Name: Adj Close, dtype: float64
Oil
Futures, Continuous Contract, Front Month
import pandas as pd, datetime, time as timelib
import urllib.request as urllib2, io
end = datetime.datetime.now()
start = datetime.datetime(1980, 1, 1)
start = int(timelib.mktime(start.timetuple()))
end = int(timelib.mktime(end.timetuple()))
base_fin_url = "https://query1.finance.yahoo.com/v7/finance/download"
url = base_fin_url + "/CL=F?period1=" + str(start) + "&period2=" + str(end) + "&interval=1d&events=history&includeAdjustedClose=true"
r = urllib2.urlopen(url).read()
file = io.BytesIO(r)
df = pd.read_csv(file,index_col='Date',parse_dates=True)['Close']
print (df.tail(5))
plt.plot(df.tail(1).index, df.tail(1),'ro')
df.plot()
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('oil.png')
Date
2021-07-02 75.160004
2021-07-06 73.370003
2021-07-07 72.199997
2021-07-08 72.940002
2021-07-09 74.559998
Name: Close, dtype: float64
Oil Production
import pandas as pd
df = pd.read_csv('world-crude.csv',sep='\s', comment='#',index_col=0)
df.columns = ['Production (mil barrels per day)']
df.plot()
plt.savefig('crude-production.png')
Private Debt to GDP Ratio
import pandas as pd, datetime
from pandas_datareader import data
today = datetime.datetime.now()
start=datetime.datetime(1960, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
df = data.DataReader(['GDPC1','QUSPAMUSDA'], 'fred', start, end)
df = df.interpolate()
df['Credit to GDP'] = (df.QUSPAMUSDA / df.GDPC1)*100.0
df['Credit to GDP'].plot()
plt.axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('creditgdp.png')
print (df['Credit to GDP'].tail(4))
DATE
2020-04-01 195.193495
2020-07-01 182.902748
2020-10-01 182.816315
2021-01-01 180.019920
Freq: QS-OCT, Name: Credit to GDP, dtype: float64
Total Consumer Credit Outstanding as % of GDP
import pandas as pd, datetime
from pandas_datareader import data
pd.set_option('display.max_columns', 10)
today = datetime.datetime.now()
start=datetime.datetime(1980, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['TOTALSL','GDP']
df = data.DataReader(cols, 'fred', start, end)
df = df.interpolate(method='linear')
df['debt'] = df.TOTALSL / df.GDP * 100.0
print (df.debt.tail(4))
df.debt.plot()
plt.axvspan('01-09-1990', '01-07-1991', color='y', alpha=0.5, lw=0)
plt.axvspan('01-03-2001', '27-10-2001', color='y', alpha=0.5, lw=0)
plt.axvspan('22-12-2007', '09-05-2009', color='y', alpha=0.5, lw=0)
plt.savefig('debt.png')
DATE
2021-01-01 18.956443
2021-02-01 19.039059
2021-03-01 19.123295
2021-04-01 19.207661
Freq: MS, Name: debt, dtype: float64
Wealth Inequality - GINI Index
Code taken from [3]
import pandas as pd, datetime
from pandas_datareader import data
def gini(pop,val):
pop = list(pop); pop.insert(0,0.0)
val = list(val); val.insert(0,0.0)
poparg = np.array(pop)
valarg = np.array(val)
z = valarg * poparg;
ord = np.argsort(val)
poparg = poparg[ord]
z = z[ord]
poparg = np.cumsum(poparg)
z = np.cumsum(z)
relpop = poparg/poparg[-1]
relz = z/z[-1]
g = 1 - np.sum((relz[0:-1]+relz[1:]) * np.diff(relpop))
return np.round(g,3)
today = datetime.datetime.now()
start=datetime.datetime(1989, 1, 1)
end=datetime.datetime(today.year, today.month, today.day)
cols = ['WFRBLT01026', 'WFRBLN09053','WFRBLN40080','WFRBLB50107']
df = data.DataReader(cols, 'fred', start, end)
p = [0.01, 0.09, 0.40, 0.50]
g = df.apply(lambda x: gini(p,x),axis=1)
print (g.tail(4))
g.plot()
plt.xlim('1990-01-01','2023-01-01')
plt.axvspan('1993-01-01','1993-01-01',color='y')
plt.axvspan('2001-01-01','2001-01-01',color='y')
plt.axvspan('2009-01-01','2009-01-01',color='y')
plt.axvspan('2017-01-01','2017-01-01',color='y')
plt.axvspan('2020-12-01','2020-12-01',color='y')
plt.text('1990-01-01',0.44,'HW')
plt.text('1994-01-01',0.46,'Clinton')
plt.text('2003-01-01',0.47,'Bush')
plt.text('2011-01-01',0.44,'Obama')
plt.text('2018-01-01',0.42,'DJT')
plt.text('2020-03-01',0.48,'Biden')
plt.savefig('gini.png')
DATE
2020-04-01 0.466
2020-07-01 0.464
2020-10-01 0.463
2021-01-01 0.459
dtype: float64
References, Notes
[1] Note: for Quandl retrieval get the API key from Quandl, and place the
key in a .quandl file in the same directory as this file.
[2] Komlos
[3] Mathworks