import pandas as pd
import matplotlib.pyplot as plt
import datetime
nipa = pd.read_csv('download.csv', skiprows=4, index_col=0, header = [0,1])
gdp = nipa.copy()
gdp.columns = gdp.columns.map(''.join).str.strip('')

rename = {'Q1': '', 'Q2': '.25', 'Q3': '.5', 'Q4': '.75'}
gdp = gdp.rename(columns=lambda x : x.replace('Q1', ''))
gdp = gdp.rename(columns=lambda x : x.replace('Q2', '.25'))
gdp = gdp.rename(columns=lambda x : x.replace('Q3', '.5'))
gdp = gdp.rename(columns=lambda x : x.replace('Q4', '.75'))
gdp = gdp.transpose()
gdp = gdp.rename(columns=gdp.iloc[0]).drop(gdp.index[0])
gdp
Gross domestic product Personal consumption expenditures Goods Durable goods Nondurable goods Services Gross private domestic investment Fixed investment Nonresidential Structures ... Imports Goods Services Government consumption expenditures and gross investment Federal National defense Nondefense State and local Addendum: Gross national product
1959 16.336 16.055 26.335 56.963 17.792 11.342 24.417 23.285 29.987 8.768 ... 16.529 17.173 14.627 10.982 13.581 13.059 14.575 9.125 NaN 16.303
1959.25 16.361 16.120 26.367 57.179 17.796 11.427 24.348 23.385 30.175 8.817 ... 16.608 17.328 14.588 10.971 13.557 13.053 14.461 9.128 NaN 16.329
1959.5 16.424 16.218 26.449 57.169 17.873 11.536 24.347 23.458 30.323 8.851 ... 16.687 17.459 14.586 10.966 13.551 13.080 14.283 9.123 NaN 16.392
1959.75 16.489 16.306 26.519 57.145 17.939 11.638 24.403 23.501 30.395 8.859 ... 16.849 17.693 14.612 10.975 13.560 13.093 14.268 9.132 NaN 16.456
1960 16.555 16.328 26.482 57.062 17.914 11.691 24.724 23.534 30.413 8.890 ... 16.801 17.640 14.567 11.009 13.545 13.075 14.256 9.222 NaN 16.523
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020.25 112.859 110.491 93.230 84.783 97.712 119.709 109.134 110.694 104.867 120.620 ... 86.349 82.180 108.574 114.253 111.443 109.298 114.765 116.093 NaN 112.846
2020.5 113.888 111.490 94.345 86.344 98.544 120.620 110.022 111.307 104.899 120.931 ... 88.008 83.900 109.755 114.922 112.267 110.259 115.398 116.659 NaN 113.873
2020.75 114.439 111.910 94.418 86.386 98.636 121.263 110.143 111.840 105.009 121.086 ... 88.489 84.261 111.061 115.769 112.957 110.933 116.111 117.612 NaN 114.432
2021 115.652 112.970 95.771 86.879 100.547 122.105 110.919 112.855 105.207 122.253 ... 91.286 87.241 112.329 117.294 114.063 112.146 117.063 119.417 NaN 115.632
2021.25 117.410 114.756 97.927 90.320 101.790 123.595 111.958 114.090 105.434 124.877 ... 94.207 90.210 114.684 118.997 115.188 113.290 118.164 121.510 NaN 117.389

250 rows × 28 columns


gdp_def = gdp[['        Gross domestic product']]
gdp_def = gdp_def.rename(columns={'        Gross domestic product': 'GDP'})
gdp_def['GDP']=gdp_def['GDP'].astype(float)
gdp_def['Rate']=gdp_def['GDP'].pct_change(periods=4)
gdp_def['Rate'] = gdp_def['Rate'].apply(lambda a:a * 100)
gdp_def = gdp_def.drop(columns=['date'])
gdp_def = gdp_def.set_index(pd.DatetimeIndex(gdp_def['date']))
gdp_def = gdp_def.iloc[4: , :]
gdp_def
GDP Rate
1960 16.555 1.340597
1960.25 16.597 1.442455
1960.5 16.654 1.400390
1960.75 16.703 1.297835
1961 16.739 1.111447
... ... ...
2020.25 112.859 0.630394
2020.5 113.888 1.218483
2020.75 114.439 1.293172
2021 115.652 2.034478
2021.25 117.410 4.032465

246 rows × 2 columns

goog = gdp_def['Rate']
CB91_Blue = '#2CBDFE'
CB91_Green = '#47DBCD'
CB91_Pink = '#F3A0F2'
CB91_Purple = '#9D2EC5'
CB91_Violet = '#661D98'
CB91_Amber = '#F5B14C'
#color_list = [CB91_Blue, CB91_Pink, CB91_Green, CB91_Amber,
#              CB91_Purple, CB91_Violet]
color_list = ['#e3abe8','#E8ABB0', '#ABE8E3']
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=color_list)
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
sns.set(font='Helvetica',
        rc={
 'axes.axisbelow': False,
 'axes.edgecolor': 'lightgrey',
 'axes.facecolor': '#DDEAE3',
 'axes.grid': True,
 #'axes.gridcolor': 'CB91_Green',
 'axes.labelcolor': 'dimgrey',
 'axes.spines.right': False,
 'axes.spines.bottom': False,
 'axes.spines.left': False,
 'axes.spines.top': False,
 'figure.facecolor': 'white',
 'lines.solid_capstyle': 'round',
 'patch.edgecolor': 'w',
 'patch.force_edgecolor': True,
 'text.color': 'dimgrey',
 'xtick.bottom': True,
 'xtick.color': 'dimgrey',
 'xtick.direction': 'inout',
 'xtick.top': False,
 'ytick.color': 'dimgrey',
 'ytick.direction': 'out',
 'ytick.left': True,
 'ytick.right': False})
sns.set_context("notebook", rc={"font.size":12,
                                "axes.titlesize":12,
                                "axes.labelsize":12})
fig, ax = plt.subplots(figsize=(12,8), dpi=300)
ax.patch.set_alpha(.55)
ax.plot(gdp_def['Rate'], color='#e3abe8', alpha=1)
#ax.axvspan(1990, 2000, color=CB91_Violet, alpha=0.25)
#ax.set_xticks(ax.get_xticks()[::65])
ax.tick_params(axis='both', direction='inout', length=6, grid_alpha=.5)
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_formatter('{x:.0f}')
ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.yaxis.set_major_locator(MultipleLocator(2))
ax.yaxis.set_major_formatter('{x:.0f}')
ax.yaxis.set_minor_locator(MultipleLocator(1))
ax.set_xlim(1960, 2022)
#ax.set_ylim(0)
ax.set(title='Inflation Rate (year over year)',
ylabel='Percent Change',
xlabel='Year')
plt.show()

gdp_def.index = gdp_def.index.astype('float64')
nipa_gdp = pd.read_csv('gdp.csv', skiprows=4, index_col=0, header = [0,1])
ngdp = nipa_gdp.copy()
ngdp.columns = ngdp.columns.map(''.join).str.strip('')
ngdp = ngdp.rename(columns=lambda x : x.replace('Q1', ''))
ngdp = ngdp.rename(columns=lambda x : x.replace('Q2', '.25'))
ngdp = ngdp.rename(columns=lambda x : x.replace('Q3', '.5'))
ngdp = ngdp.rename(columns=lambda x : x.replace('Q4', '.75'))
ngdp = ngdp.transpose()
ngdp = ngdp.rename(columns=ngdp.iloc[0]).drop(ngdp.index[0])
ngdp
Gross domestic product Personal consumption expenditures Goods Durable goods Nondurable goods Services Gross private domestic investment Fixed investment Nonresidential Structures ... Goods Services Imports Goods Services Government consumption expenditures and gross investment Federal National defense Nondefense State and local
1959 510.3 309.4 169.7 43.6 126.1 139.7 83.2 79.3 50.9 17.4 ... 15.6 6.4 21.4 14.5 6.9 117.2 72.4 61.1 11.2 44.8
1959.25 522.7 315.5 172.6 45.5 127.2 142.9 89.4 82.1 52.7 18.0 ... 15.9 5.9 22.5 15.5 7.0 118.5 73.6 60.3 13.3 45.0
1959.5 525.0 320.7 174.5 46.3 128.2 146.2 83.6 83.2 54.4 18.6 ... 17.5 6.6 22.9 15.8 7.1 119.5 74.5 61.4 13.1 45.0
1959.75 528.6 322.8 173.6 44.1 129.5 149.3 86.5 82.4 54.4 18.5 ... 16.9 6.3 22.5 15.4 7.1 118.6 73.8 61.0 12.9 44.8
1960 542.6 326.4 175.1 45.5 129.6 151.3 96.5 85.3 56.3 19.4 ... 19.5 6.6 23.3 15.7 7.6 117.0 71.0 60.0 11.0 46.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020.25 19477.4 12989.7 4349.9 1468.3 2881.7 8639.8 3167.0 3456.9 2659.1 585.9 ... 1146.3 661.5 2346.7 1942.1 404.6 3859.6 1525.0 872.4 652.6 2334.6
2020.5 21138.6 14293.8 4867.2 1753.3 3113.9 9426.6 3708.8 3693.8 2776.6 563.5 ... 1413.0 666.6 2805.3 2368.8 436.5 3861.7 1515.1 883.9 631.3 2346.5
2020.75 21477.6 14467.6 4867.3 1759.2 3108.1 9600.4 3923.2 3865.9 2862.7 552.3 ... 1520.8 699.9 3019.1 2540.8 478.3 3885.3 1512.3 900.8 611.5 2373.0
2021 22038.2 15005.4 5245.0 1957.8 3287.2 9760.4 3928.0 4022.2 2956.7 565.0 ... 1607.5 704.4 3184.5 2698.1 486.4 3977.3 1568.6 897.1 671.6 2408.7
2021.25 22731.4 15675.9 5527.4 2090.7 3436.7 10148.5 3924.2 4100.2 3029.5 569.1 ... 1726.0 729.7 3340.3 2818.0 522.3 4016.0 1563.3 904.1 659.2 2452.7

250 rows × 26 columns

nomgdp = ngdp[['        Gross domestic product']]
nomgdp = nomgdp.rename(columns={'        Gross domestic product': 'GDP'})
nomgdp['GDP']=nomgdp['GDP'].astype(float)
nomgdp.index = nomgdp.index.astype('float64')
nomgdp
GDP
1959.00 510.3
1959.25 522.7
1959.50 525.0
1959.75 528.6
1960.00 542.6
... ...
2020.25 19477.4
2020.50 21138.6
2020.75 21477.6
2021.00 22038.2
2021.25 22731.4

250 rows × 1 columns

nomgdp['Real GDP (2012 Dollars)'] = (nomgdp / (gdp_def/100))
nomgdp = nomgdp.rename(columns={'GDP': 'Nominal GDP'})
gdp_def
GDP
1959.00 16.336
1959.25 16.361
1959.50 16.424
1959.75 16.489
1960.00 16.555
... ...
2020.25 112.859
2020.50 113.888
2020.75 114.439
2021.00 115.652
2021.25 117.410

250 rows × 1 columns

realgdp
GDP Real GDP
1959.00 83.362608 NaN
1959.25 85.518947 NaN
1959.50 86.226000 NaN
1959.75 87.160854 NaN
1960.00 89.827430 NaN
... ... ...
2020.25 21981.998866 NaN
2020.50 24074.328768 NaN
2020.75 24578.750664 NaN
2021.00 25487.619064 NaN
2021.25 26688.936740 NaN

250 rows × 2 columns

nomgdp
Nominal GDP Real GDP (2012 Dollars) Annualized Rate
1959.00 510.3 3123.775710 NaN
1959.25 522.7 3194.792494 9.408546
1959.50 525.0 3196.541646 0.219180
1959.75 528.6 3205.773546 1.160250
1960.00 542.6 3277.559650 9.262480
... ... ... ...
2020.25 19477.4 17258.171701 -31.237191
2020.50 21138.6 18560.866817 33.786988
2020.75 21477.6 18767.727785 4.533084
2021.00 22038.2 19055.615121 6.278424
2021.25 22731.4 19360.701814 6.559579

250 rows × 3 columns

fig, ax = plt.subplots(figsize=(12,8), dpi=300)
ax.patch.set_alpha(.4)
ax.plot(nomgdp, alpha=1)
#ax.set_color_cycle(['#e3abe8', '#ABE8E3'])
#ax.cycler(color=['#e3abe8', '#ABE8E3'])
#ax2.plot(nomgdp['Real GDP'], color='#ABE8E3', alpha=1)
#ax.axvspan(2019.75, 2020.25, color='#EAE3DD', alpha=0.6)
#ax.axvspan(2019.75, 2020.25, color='#EAE3DD', alpha=0.6)
oof = 0
peaks = [2019.75, 2007.75, 2001.00, 1990.5, 1981.5, 1980.00, 1973.75, 1969.75, 1960.25]
ends = [2020.5, 2009.25, 2001.75, 1991.00, 1982.75, 1980.5, 1975.00, 1970.75, 1961.00]
while oof <= 8:
    #ax.axvspan(peaks[oof], ends[oof], color='#DDE7EA', alpha=0.5)
    oof += 1
    pass

#ax.set_xticks(ax.get_xticks()[::10])
ax.tick_params(axis='both', direction='inout', length=6, grid_alpha=.5)
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_formatter('{x:.0f}')
ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.yaxis.set_major_locator(MultipleLocator(5000))
ax.yaxis.set_major_formatter('{x:.0f}')
ax.yaxis.set_minor_locator(MultipleLocator(2500))
ax.set_xlim(1960, 2022)
#ax.set_ylim(0)
ax.set(title='Nominal and Real GDP from Q1 1960 to Q2 2021',
ylabel='Billions of Dollars',
xlabel='Year')
# twin1 = ax.twinx()
# twin1.plot(realgdp)
ax.legend(nomgdp.columns.values,loc='upper left', frameon=True)
plt.show()

#gdp_def['Rate']=gdp_def['GDP'].pct_change(periods=4)
nomgdp['Annualized Rate'] = nomgdp['Real GDP (2012 Dollars)'].pct_change(periods=1)
nomgdp
Nominal GDP Real GDP (2012 Dollars) Annualized Rate
1959.00 510.3 3123.775710 NaN
1959.25 522.7 3194.792494 9.408546
1959.50 525.0 3196.541646 0.219180
1959.75 528.6 3205.773546 1.160250
1960.00 542.6 3277.559650 9.262480
... ... ... ...
2020.25 19477.4 17258.171701 -31.237191
2020.50 21138.6 18560.866817 33.786988
2020.75 21477.6 18767.727785 4.533084
2021.00 22038.2 19055.615121 6.278424
2021.25 22731.4 19360.701814 6.559579

250 rows × 3 columns

nomgdp['Annualized Rate'] = nomgdp['Annualized Rate'].apply(lambda a: ((((a+1)**4)-1)*100))
fig, ax = plt.subplots(figsize=(12,8), dpi=300)
ax.patch.set_alpha(.3)
ax.plot(nomgdp['Annualized Rate'], color='#E8ABB0', alpha=1)
#ax.set_color_cycle(['#e3abe8', '#ABE8E3'])
#ax.cycler(color=['#e3abe8', '#ABE8E3'])
#ax2.plot(nomgdp['Real GDP'], color='#ABE8E3', alpha=1)
#ax.axvspan(2019.75, 2020.25, color='#EAE3DD', alpha=0.6)
#ax.axvspan(2019.75, 2020.25, color='#EAE3DD', alpha=0.6)
oof = 0
peaks = [2019.75, 2007.75, 2001.00, 1990.5, 1981.5, 1980.00, 1973.75, 1969.75, 1960.25]
ends = [2020.5, 2009.25, 2001.75, 1991.00, 1982.75, 1980.5, 1975.00, 1970.75, 1961.00]
while oof <= 8:
    ax.axvspan(peaks[oof], ends[oof], color='#DDE7EA', alpha=0.5)
    oof += 1
    pass

#ax.set_xticks(ax.get_xticks()[::10])
ax.tick_params(axis='both', direction='inout', length=6, grid_alpha=.5)
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_formatter('{x:.0f}')
ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.yaxis.set_major_locator(MultipleLocator(10))
ax.yaxis.set_major_formatter('{x:.0f}')
ax.yaxis.set_minor_locator(MultipleLocator(2.5))
ax.set_xlim(1960, 2022)
ax.axhline(0, color='darkgrey')
#ax.set_ylim(0)
ax.set(title='Annualized growth of Real GDP, by quarter',
ylabel='Percent change',
xlabel='Year')
# twin1 = ax.twinx()
# twin1.plot(realgdp)
#ax.legend(nomgdp.columns.values,loc='upper left', frameon=True)
plt.show()

u36 = pd.read_csv('u36.csv')
u36
UNRATE U6RATE
DATE
1994-01-01 6.6 11.7
1994-02-01 6.6 11.4
1994-03-01 6.5 11.5
1994-04-01 6.4 11.3
1994-05-01 6.1 10.9
... ... ...
2021-02-01 6.2 11.1
2021-03-01 6.0 10.7
2021-04-01 6.1 10.4
2021-05-01 5.8 10.2
2021-06-01 5.9 9.8

330 rows × 2 columns

u36 = u36.set_index(pd.DatetimeIndex(u36['DATE'])).drop(columns=['DATE'])
u36 = u36.rename(columns={'UNRATE': 'U3 Unemployment Rate', 'U6RATE':'U6 Unemployment Rate'})
xig, ax = plt.subplots(figsize=(12,8), dpi=300)
import matplotlib.dates as mdates

ax.patch.set_alpha(.4)
ax.plot(u36, alpha=1)
#ax.set_color_cycle(['#e3abe8', '#ABE8E3'])
#ax.cycler(color=['#e3abe8', '#ABE8E3'])
#ax2.plot(nomgdp['Real GDP'], color='#ABE8E3', alpha=1)
#ax.axvspan(2019.75, 2020.25, color='#EAE3DD', alpha=0.6)
#ax.axvspan(2019.75, 2020.25, color='#EAE3DD', alpha=0.6)
oof = 0
#peaks = [2019.75, 2007.75, 2001.00]
#ends = [2020.5, 2009.25, 2001.75]
# while oof <= 8:
#     ax.axvspan(peaks[oof], ends[oof], color='#DDE7EA', alpha=0.5)
#     oof += 1
#     pass
#ax.axvspan((2020, 2, 1), (2020, 4, 1), color='#DDE7EA', alpha=0.5)
ax.axvspan(*mdates.datestr2num(['2/01/2020', '4/01/2020']), color='#DDE7EA', alpha=0.5)
ax.axvspan(*mdates.datestr2num(['12/01/2007', '6/01/2009']), color='#DDE7EA', alpha=0.5)
ax.axvspan(*mdates.datestr2num(['3/01/2001', '11/01/2001']), color='#DDE7EA', alpha=0.5)

#ax.set_xticks(ax.get_xticks()[::10])
ax.tick_params(axis='both', direction='inout', length=6, grid_alpha=.5)
# ax.xaxis.set_major_locator(MultipleLocator(10))
# ax.xaxis.set_major_formatter('{x:.0f}')
# ax.xaxis.set_minor_locator(MultipleLocator(5))
ax.yaxis.set_major_locator(MultipleLocator(5))
ax.yaxis.set_major_formatter('{x:.0f}')
ax.yaxis.set_minor_locator(MultipleLocator(2.5))
ax.set_xlim(*mdates.datestr2num(['1/01/1994', '6/01/2021']))
#ax.axhline(0, color='darkgrey')
ax.set_ylim(0)
ax.set(title='U3 and U6 Unemployment Rate, by month',
ylabel='Unemployment (%)',
xlabel='Date')
# twin1 = ax.twinx()
# twin1.plot(realgdp)
ax.legend(u36.columns.values,loc='upper left', frameon=True)
plt.show()