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Country Economic Data Analysis

A sample of various economic indicators by country using python for data visualization

This project was built with Python

import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  

pd.set_option('Display.max_rows', 1000)
  pd.set_option('Display.max_columns', 100)
  

country_df = pd.read_excel(r'/Users/jasonlee_1_1/Documents/Datasets/country_data.xlsx')
  

Set multiple indexes (country & indicator)

country_df.set_index(['Country', 'IndicatorName'], inplace=True)
  

country_df.head(5)
  

		1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013
Country	IndicatorName
Afghanistan	Final consumption expenditure	4.564542e+09	4.378772e+09	4.267302e+09	4.718450e+09	5.047528e+09	5.190784e+09	5.233225e+09	5.663142e+09	5.822363e+09	5.681430e+09	5.496820e+09	5.594092e+09	5.713029e+09	5.997747e+09	6.103186e+09	6.121124e+09	6.304677e+09	5.656766e+09	5.189110e+09	4.822818e+09	4.672128e+09	4.338720e+09	4.588914e+09	3.578467e+09	2.886939e+09	4.486074e+09	4.398895e+09	4.314326e+09	4.253662e+09	4.175809e+09	4.067512e+09	3.858310e+09	5.894713e+09	7.284105e+09	7.169433e+09	8.328424e+09	8.806973e+09	9.857976e+09	1.008238e+10	1.213075e+10	1.292326e+10	1.342614e+10	1.547503e+10	1.587033e+10
	Household consumption expenditure (including Non-profit institutions serving households)	4.313212e+09	4.124317e+09	4.003631e+09	4.439145e+09	4.759225e+09	4.811703e+09	4.837928e+09	5.236723e+09	5.367908e+09	5.237976e+09	5.067775e+09	5.157455e+09	5.267108e+09	5.529603e+09	5.626812e+09	5.643353e+09	5.812574e+09	5.215234e+09	4.784092e+09	4.446371e+09	4.307449e+09	4.000102e+09	4.230674e+09	3.299166e+09	2.661701e+09	4.135583e+09	4.055768e+09	3.978123e+09	3.919935e+09	3.851662e+09	3.751466e+09	3.549475e+09	5.451838e+09	6.716488e+09	6.526145e+09	7.664069e+09	7.955015e+09	8.833153e+09	9.041919e+09	1.089687e+10	1.172417e+10	1.206670e+10	1.390981e+10	1.417584e+10
	General government final consumption expenditure	2.325123e+08	2.386311e+08	2.508686e+08	2.631060e+08	2.692248e+08	3.732435e+08	3.915997e+08	4.221934e+08	4.527871e+08	4.418271e+08	4.274706e+08	4.350354e+08	4.442841e+08	4.664263e+08	4.746267e+08	4.760186e+08	4.902971e+08	4.399118e+08	4.035311e+08	3.750670e+08	3.633414e+08	3.373694e+08	3.569356e+08	2.782746e+08	2.243951e+08	3.492638e+08	3.418299e+08	3.348773e+08	3.327952e+08	3.226533e+08	3.146419e+08	3.090114e+08	4.383134e+08	5.653761e+08	6.551988e+08	6.643553e+08	8.768912e+08	1.064611e+09	1.079870e+09	1.278297e+09	1.227158e+09	1.407557e+09	1.620417e+09	1.765987e+09
	Gross capital formation	6.897897e+08	7.357756e+08	6.438037e+08	6.438037e+08	8.277475e+08	9.565083e+08	1.425565e+09	1.342791e+09	1.554326e+09	1.453272e+09	1.387379e+09	1.445337e+09	1.459767e+09	1.531962e+09	1.565069e+09	1.565728e+09	1.613258e+09	1.448336e+09	1.327910e+09	1.234356e+09	1.195878e+09	1.056052e+09	1.063845e+09	7.913112e+08	6.097564e+08	9.061401e+08	8.507147e+08	7.997050e+08	7.564639e+08	7.131425e+08	6.676514e+08	7.013424e+08	9.612824e+08	1.080955e+09	1.329356e+09	1.444097e+09	1.442450e+09	1.520829e+09	1.502197e+09	1.577921e+09	1.371679e+09	1.391975e+09	1.281930e+09	1.246302e+09
	Gross fixed capital formation (including Acquisitions less disposals of valuables)	6.897897e+08	7.357756e+08	6.438037e+08	6.438037e+08	8.277475e+08	9.565083e+08	1.425565e+09	1.342791e+09	1.554326e+09	1.453272e+09	1.387379e+09	1.445337e+09	1.459767e+09	1.531962e+09	1.565069e+09	1.565728e+09	1.613258e+09	1.448336e+09	1.327910e+09	1.234356e+09	1.195878e+09	1.056052e+09	1.063845e+09	7.913112e+08	6.097564e+08	9.061401e+08	8.507147e+08	7.997050e+08	7.564639e+08	7.131425e+08	6.676514e+08	7.013424e+08	9.612824e+08	1.080955e+09	1.329356e+09	1.444097e+09	1.442450e+09	1.520829e+09	1.502197e+09	1.577921e+09	1.371679e+09	1.391975e+09	1.281930e+09	1.246302e+09

Get all indicators for a particular country for a particular year:

country_df.loc['United States', 1970]
  

IndicatorName
  Final consumption expenditure                                                               3.798640e+12
  Household consumption expenditure (including Non-profit institutions serving households)    2.708690e+12
  General government final consumption expenditure                                            1.073740e+12
  Gross capital formation                                                                     7.678880e+11
  Gross fixed capital formation (including Acquisitions less disposals of valuables)          7.797820e+11
  Changes in inventories                                                                     -1.234387e+10
  Exports of goods and services                                                               1.771680e+11
  Imports of goods and services                                                               2.378630e+11
  Gross Domestic Product (GDP)                                                                4.343660e+12
  Agriculture, hunting, forestry, fishing (ISIC A-B)                                          4.605207e+10
  Mining, Manufacturing, Utilities (ISIC C-E)                                                 9.947280e+11
  Manufacturing (ISIC D)                                                                      6.833920e+11
  Construction (ISIC F)                                                                       4.392630e+11
  Wholesale, retail trade, restaurants and hotels (ISIC G-H)                                  3.864870e+11
  Transport, storage and communication (ISIC I)                                               2.505570e+11
  Other Activities (ISIC J-P)                                                                 3.107960e+12
  Total Value Added                                                                           5.237800e+12
  Name: 1970, dtype: float64

Get all years for a particular country for a particular indicator:

country_df.loc['United States'].loc['Final consumption expenditure']
  

1970    3.798640e+12
  1971    3.901990e+12
  1972    4.077770e+12
  1973    4.216870e+12
  1974    4.216010e+12
  1975    4.315040e+12
  1976    4.491650e+12
  1977    4.653610e+12
  1978    4.828210e+12
  1979    4.924120e+12
  1980    4.929270e+12
  1981    5.001270e+12
  1982    5.077970e+12
  1983    5.329520e+12
  1984    5.556630e+12
  1985    5.843220e+12
  1986    6.091300e+12
  1987    6.274920e+12
  1988    6.501070e+12
  1989    6.689240e+12
  1990    6.831700e+12
  1991    6.868840e+12
  1992    7.070270e+12
  1993    7.255960e+12
  1994    7.481610e+12
  1995    7.660160e+12
  1996    7.876940e+12
  1997    8.141240e+12
  1998    8.516920e+12
  1999    8.922950e+12
  2000    9.311510e+12
  2001    9.568720e+12
  2002    9.837380e+12
  2003    1.011790e+13
  2004    1.046100e+13
  2005    1.077420e+13
  2006    1.106060e+13
  2007    1.129070e+13
  2008    1.131120e+13
  2009    1.124520e+13
  2010    1.142100e+13
  2011    1.156920e+13
  2012    1.172710e+13
  2013    1.192810e+13
  Name: Final consumption expenditure, dtype: float64

Calculate percent change for each year

country_df.pct_change(axis='columns')*100
  

		1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013
Country	IndicatorName
Afghanistan	Final consumption expenditure	NaN	-4.069850	-2.545692	10.572205	6.974283	2.838142	0.817624	8.215145	2.811530	-2.420551	-3.249349	1.769607	2.126104	4.983662	1.757989	0.293912	2.998667	-10.276672	-8.267186	-7.058870	-3.124511	-7.136119	5.766555	-22.019302	-19.324703	55.392067	-1.943340	-1.922490	-1.406115	-1.830257	-2.593438	-5.143247	52.779678	23.570136	-1.574281	16.165726	5.745974	11.933753	2.276367	20.316319	6.533096	3.891230	15.260500	2.554439
	Household consumption expenditure (including Non-profit institutions serving households)	NaN	-4.379446	-2.926209	10.877982	7.210410	1.102647	0.545038	8.243076	2.505113	-2.420550	-3.249350	1.769603	2.126114	4.983653	1.757979	0.293960	2.998601	-10.276686	-8.266982	-7.059241	-3.124384	-7.135252	5.764164	-22.017961	-19.322007	55.373664	-1.929943	-1.914439	-1.462701	-1.741695	-2.601369	-5.384310	53.595601	23.196771	-2.833961	17.436379	3.796239	11.038792	2.363445	20.515006	7.592088	2.921613	15.274280	1.912588
	General government final consumption expenditure	NaN	2.631579	5.128205	4.878049	2.325582	38.636363	4.918033	7.812500	7.246377	-2.420575	-3.249336	1.769653	2.125971	4.983791	1.758137	0.293251	2.999577	-10.276484	-8.270004	-7.053745	-3.126267	-7.148114	5.799633	-22.037850	-19.362003	55.646785	-2.128433	-2.033951	-0.621751	-3.047474	-2.482966	-1.789496	41.843755	28.989003	15.887240	1.397525	31.991286	21.407378	1.433321	18.375095	-4.000570	14.700564	15.122654	8.983471
	Gross capital formation	NaN	6.666667	-12.500000	0.000000	28.571414	15.555568	49.038461	-5.806452	15.753425	-6.501485	-4.534129	4.177514	0.998404	4.945699	2.161075	0.042107	3.035645	-10.222902	-8.314782	-7.045271	-3.117200	-11.692359	0.737971	-25.617823	-22.943549	48.606905	-6.116647	-5.996101	-5.407129	-5.726827	-6.378964	5.046190	37.063222	12.449261	22.979753	8.631342	-0.114073	5.433784	-1.225160	5.040882	-13.070446	1.479593	-7.905623	-2.779249
	Gross fixed capital formation (including Acquisitions less disposals of valuables)	NaN	6.666667	-12.500000	0.000000	28.571414	15.555568	49.038461	-5.806452	15.753425	-6.501485	-4.534129	4.177514	0.998404	4.945699	2.161075	0.042107	3.035645	-10.222902	-8.314782	-7.045271	-3.117200	-11.692359	0.737971	-25.617823	-22.943549	48.606905	-6.116647	-5.996101	-5.407129	-5.726827	-6.378964	5.046190	37.063222	12.449261	22.979753	8.631342	-0.114073	5.433784	-1.225160	5.040882	-13.070446	1.479593	-7.905623	-2.779249
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Zimbabwe	Construction (ISIC F)	NaN	10.619468	10.400000	15.942029	3.750000	-6.024097	-21.153846	-11.382114	-16.513761	-2.197803	2.247192	15.384615	-3.809524	-7.920792	-7.526882	-25.581395	17.060368	17.488789	23.282443	-9.597523	5.308219	0.650406	4.200323	-1.860465	0.315956	-23.937008	12.008282	16.635860	6.022187	-11.061285	-16.310002	-35.947172	-39.040061	-4.686725	-0.252800	-3.150054	-4.823966	-3.061097	2.883809	289.825270	14.099048	65.102170	23.497522	3.900000
	Wholesale, retail trade, restaurants and hotels (ISIC G-H)	NaN	8.710801	7.371795	5.373134	9.348442	-2.072539	-6.613757	-4.532578	-2.373887	3.039514	33.038348	1.108647	-1.096491	-13.082040	-6.632653	5.464481	5.436573	-1.562500	8.134921	2.788991	16.636915	6.764616	-6.307339	0.275398	6.927067	5.479452	6.764069	2.306133	0.767897	0.393314	-10.174779	-6.303659	-1.331089	-26.435754	-18.674151	-20.611923	-2.296885	2.435506	15.654308	97.304708	8.818526	4.299278	4.299278	3.931404
	Transport, storage and communication (ISIC I)	NaN	6.217617	6.341463	-3.211009	-0.947867	-6.220096	-3.571429	-12.698413	1.212121	3.592814	21.965318	4.739336	2.262443	-0.884956	0.892857	4.867257	7.677165	-4.113346	5.624404	-0.270758	7.239819	3.797468	8.455284	-4.572714	8.641005	23.355025	19.753810	0.930005	-11.057226	-3.598691	-7.742126	-5.359941	1.944270	-2.332142	0.316183	0.181146	-1.299938	-2.061769	18.506854	24.567237	4.738481	0.014369	6.692052	6.976744
	Other Activities (ISIC J-P)	NaN	5.184249	5.013575	5.500755	5.937823	8.730777	1.636673	3.036339	-0.877831	-2.648836	6.792349	14.897527	8.511765	2.813226	3.386395	3.694641	3.390535	4.582383	3.711121	2.413836	2.525829	4.346433	0.224079	2.012942	0.306409	-0.151990	2.671433	0.847288	3.459933	-1.773880	-2.906834	1.578049	7.006228	5.607765	0.299225	-5.631285	-4.162641	-3.592093	-2.734385	51.404568	16.480070	21.764815	22.113046	4.227449
	Total Value Added	NaN	10.820388	9.138664	0.280880	6.440233	-1.559388	-0.642714	-9.797729	0.385909	2.384946	12.411285	7.956105	-0.720049	-6.063219	2.621259	9.482821	1.206812	0.893824	4.902066	3.745765	7.639463	4.799277	-6.684205	3.361637	5.411342	-0.191321	11.147928	1.364874	-0.654050	-1.209010	-6.707025	-0.629341	-6.602774	-8.711097	-4.278721	-5.016554	-3.567039	-3.630125	-6.058535	49.472850	11.445011	12.007802	10.561460	3.833457

3685 rows × 44 columns

Compare GDP growth across countries

us_gdp = country_df.loc['United States'].loc['Gross Domestic Product (GDP)']
  

us_pct = us_gdp.pct_change()*100
  

spain_gdp = country_df.loc['Spain'].loc['Gross Domestic Product (GDP)']
  

spain_pct = spain_gdp.pct_change()*100
  

japan_gdp = country_df.loc['Japan'].loc['Gross Domestic Product (GDP)']
  

japan_pct = japan_gdp.pct_change()*100
  

china_gdp = country_df.loc['China'].loc['Gross Domestic Product (GDP)']
  

china_pct = china_gdp.pct_change()*100
  

plt.figure(figsize=(12, 6))
  plt.plot(us_pct, label='US')
  plt.plot(spain_pct, label='Spain')
  plt.plot(china_pct, label='China')
  plt.plot(japan_pct, label='Japan')
  plt.title('Annual GDP Growth by Country')
  plt.ylabel('% change')
  plt.legend()
  plt.show()
  

Visualize GDP correlation between countries

gdp = pd.DataFrame(columns=['US', 'Spain', 'China', 'Japan'])
  series = [us_gdp, spain_gdp, china_gdp, japan_gdp]
  for i in range(len(gdp.columns)):
      gdp[gdp.columns[i]] = series[i]
  

corrMatrix = gdp.corr()
  sns.heatmap(corrMatrix, annot=True)
  plt.show()
  

Consumption expenditure as a fraction of GDP across countries

us = country_df.loc['United States']
  

us_consumption = us.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  us_consumption_to_gdp = (us_consumption/us_gdp)*100
  

spain = country_df.loc['Spain']
  

spain_consumption = spain.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  spain_consumption_to_gdp = (spain_consumption/spain_gdp)*100
  

japan = country_df.loc['Japan']
  

japan_consumption = japan.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  japan_consumption_to_gdp = (japan_consumption/japan_gdp)*100
  

china = country_df.loc['China']
  

china_consumption = china.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  china_consumption_to_gdp = (china_consumption/china_gdp)*100
  

plt.figure(figsize=(12, 6))
  plt.plot(us_consumption_to_gdp, label='US')
  plt.plot(spain_consumption_to_gdp, label='Spain')
  plt.plot(japan_consumption_to_gdp, label='Japan')
  plt.plot(china_consumption_to_gdp, label='China')
  plt.title('Consumption to GDP')
  plt.ylabel('Consumption (% of GDP)')
  plt.legend()
  plt.show()
  

Components of GDP as a pie chart for each country

us_consumption = us.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  

us_investment = us.loc['Gross fixed capital formation (including Acquisitions less disposals of valuables)']
  

us_gvt_spending = us.loc['General government final consumption expenditure']
  

us_nx = us.loc['Exports of goods and services'] - us.loc['Imports of goods and services']
  

us_gdp_components = pd.concat([us_consumption, us_investment, us_gvt_spending, us_nx], axis=1)
  

spain_consumption = spain.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  

spain_investment = spain.loc['Gross fixed capital formation (including Acquisitions less disposals of valuables)']
  

spain_gvt_spending = spain.loc['General government final consumption expenditure']
  

spain_nx = spain.loc['Exports of goods and services'] - spain.loc['Imports of goods and services']
  

spain_gdp_components = pd.concat([spain_consumption, spain_investment, spain_gvt_spending, spain_nx], axis=1)
  

china_investment = china.loc['Gross fixed capital formation (including Acquisitions less disposals of valuables)']
  

china_gvt_spending = china.loc['General government final consumption expenditure']
  

china_nx = china.loc['Exports of goods and services'] - china.loc['Imports of goods and services']
  

china_gdp_components = pd.concat([china_consumption, china_investment, china_gvt_spending, china_nx], axis=1)
  

japan_consumption = japan.loc['Household consumption expenditure (including Non-profit institutions serving households)']
  

japan_investment = japan.loc['Gross fixed capital formation (including Acquisitions less disposals of valuables)']
  

japan_gvt_spending = japan.loc['General government final consumption expenditure']
  

japan_nx = japan.loc['Exports of goods and services'] - japan.loc['Imports of goods and services']
  

japan_gdp_components = pd.concat([japan_consumption, japan_investment, japan_gvt_spending, japan_nx], axis=1)
  

us_gdp_components
  

	Household consumption expenditure (including Non-profit institutions serving households)	Gross fixed capital formation (including Acquisitions less disposals of valuables)	General government final consumption expenditure	0
1970	2.708690e+12	7.797820e+11	1.073740e+12	-6.069500e+10
1971	2.811450e+12	8.022210e+11	1.075090e+12	-7.029900e+10
1972	2.984200e+12	8.649190e+11	1.079330e+12	-8.446800e+10
1973	3.133570e+12	9.257830e+11	1.070540e+12	-6.078600e+10
1974	3.107980e+12	8.899460e+11	1.094240e+12	-3.593100e+10
1975	3.179170e+12	8.244820e+11	1.121690e+12	-5.826000e+09
1976	3.356290e+12	8.901600e+11	1.122540e+12	-4.456500e+10
1977	3.498680e+12	9.819170e+11	1.142530e+12	-7.146300e+10
1978	3.652410e+12	1.086670e+12	1.163860e+12	-7.268500e+10
1979	3.738740e+12	1.148530e+12	1.173780e+12	-4.976100e+10
1980	3.724550e+12	1.099330e+12	1.192360e+12	9.518000e+09
1981	3.776880e+12	1.120100e+12	1.211760e+12	4.758000e+09
1982	3.823840e+12	1.059860e+12	1.240850e+12	-1.831100e+10
1983	4.037550e+12	1.137390e+12	1.279030e+12	-7.121500e+10
1984	4.245910e+12	1.308480e+12	1.298730e+12	-1.410570e+11
1985	4.468020e+12	1.400150e+12	1.362730e+12	-1.612520e+11
1986	4.651820e+12	1.443680e+12	1.426240e+12	-1.781300e+11
1987	4.807250e+12	1.469480e+12	1.454660e+12	-1.692580e+11
1988	5.003910e+12	1.504140e+12	1.484650e+12	-1.227300e+11
1989	5.145220e+12	1.548310e+12	1.530990e+12	-9.205700e+10
1990	5.244800e+12	1.546760e+12	1.573190e+12	-6.591400e+10
1991	5.252920e+12	1.482760e+12	1.601290e+12	-2.449500e+10
1992	5.444620e+12	1.544200e+12	1.612150e+12	-2.673300e+10
1993	5.633220e+12	1.623070e+12	1.610770e+12	-6.643400e+10
1994	5.851380e+12	1.722240e+12	1.619680e+12	-9.657800e+10
1995	6.026310e+12	1.811430e+12	1.624490e+12	-8.647700e+10
1996	6.236750e+12	1.950590e+12	1.632220e+12	-9.843500e+10
1997	6.472050e+12	2.094880e+12	1.662030e+12	-1.262300e+11
1998	6.817710e+12	2.281280e+12	1.693670e+12	-2.387300e+11
1999	7.178450e+12	2.471690e+12	1.740170e+12	-3.431000e+11
2000	7.542170e+12	2.627740e+12	1.766930e+12	-4.366700e+11
2001	7.736210e+12	2.613560e+12	1.829450e+12	-4.601100e+11
2002	7.933180e+12	2.567470e+12	1.900210e+12	-5.374600e+11
2003	8.180230e+12	2.668180e+12	1.934390e+12	-5.912500e+11
2004	8.494880e+12	2.823010e+12	1.964330e+12	-6.773200e+11
2005	8.794110e+12	2.981170e+12	1.980050e+12	-7.211800e+11
2006	9.060110e+12	3.046110e+12	2.001850e+12	-7.312600e+11
2007	9.262870e+12	3.008340e+12	2.029790e+12	-6.533900e+11
2008	9.229910e+12	2.862640e+12	2.081210e+12	-5.073200e+11
2009	9.082050e+12	2.488480e+12	2.159170e+12	-3.564000e+11
2010	9.256650e+12	2.516310e+12	2.161650e+12	-4.140600e+11
2011	9.465840e+12	2.609330e+12	2.104290e+12	-4.135200e+11
2012	9.638020e+12	2.748470e+12	2.091830e+12	-4.065200e+11
2013	9.868910e+12	2.823430e+12	2.064670e+12	-3.759000e+11

fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2,2, figsize=(12, 8))
  ax1.pie(abs(us_gdp_components.loc[2013]),
          colors=['lightskyblue', 'lightcoral', 'gold', 'wheat'],
          explode=[0,0,0.1,0], labels=['C', 'I', 'G', 'NX'],
          autopct='%.2f%%', shadow=True, startangle=90)
  ax1.set_title('US')
  ax2.pie(spain_gdp_components.loc[2013],
          colors=['lightskyblue', 'lightcoral', 'gold', 'wheat'],
          explode=[0,0,0.1,0], labels=['C', 'I', 'G', 'NX'],
          autopct='%.2f%%', shadow=True, startangle=90)
  ax2.set_title('Spain')
  ax3.pie(china_gdp_components.loc[2013],
          colors=['lightskyblue', 'lightcoral', 'gold', 'wheat'],
          explode=[0,0,0.1,0], labels=['C', 'I', 'G', 'NX'],
          autopct='%.2f%%', shadow=True, startangle=90)
  ax3.set_title('China')
  ax4.pie(japan_gdp_components.loc[2013],
          colors=['lightskyblue', 'lightcoral', 'gold', 'wheat'],
          explode=[0,0,0.1,0], labels=['C', 'I', 'G', 'NX'],
          autopct='%.2f%%', shadow=True, startangle=90)
  ax4.set_title('Japan')
  plt.show()
  

List all variables

whos
  

Variable                   Type         Data/Info
  -------------------------------------------------
  ax1                        Axes         Axes(0.18447,0.53;0.233333x0.35)
  ax2                        Axes         Axes(0.547727,0.53;0.352273x0.35)
  ax3                        Axes         Axes(0.125,0.11;0.352273x0.35)
  ax4                        Axes         Axes(0.547727,0.11;0.352273x0.35)
  china                      DataFrame                             <...>82940e+12  4.827000e+12
  china_consumption          Series       1970    6.922328e+10\n197<...>useholds), dtype: float64
  china_consumption_to_gdp   Series       1970    57.738034\n1971  <...>36.780653\ndtype: float64
  china_gdp                  Series       1970    1.198920e+11\n197<...>uct (GDP), dtype: float64
  china_gdp_components       DataFrame          Household consumpti<...>35640e+11  1.065800e+11
  china_gvt_spending         Series       1970    1.334328e+10\n197<...>penditure, dtype: float64
  china_investment           Series       1970    3.726599e+10\n197<...>aluables), dtype: float64
  china_nx                   Series       1970    4.630531e+07\n197<...>65800e+11\ndtype: float64
  china_pct                  Series       1970          NaN\n1971  <...>uct (GDP), dtype: float64
  corrMatrix                 DataFrame                 US     Spain<...>26890  0.760450  1.000000
  country_df                 DataFrame                             <...>n[3685 rows x 44 columns]
  fig                        Figure       Figure(1200x800)
  gdp                        DataFrame                    US       <...>.930690e+12  4.766600e+12
  i                          int          3
  japan                      DataFrame                             <...>98150e+12  4.680930e+12
  japan_consumption          Series       1970    9.342160e+11\n197<...>useholds), dtype: float64
  japan_consumption_to_gdp   Series       1970    59.048739\n1971  <...>59.678387\ndtype: float64
  japan_gdp                  Series       1970    1.582110e+12\n197<...>uct (GDP), dtype: float64
  japan_gdp_components       DataFrame          Household consumpti<...>71690e+11  7.232300e+10
  japan_gvt_spending         Series       1970    2.325390e+11\n197<...>penditure, dtype: float64
  japan_investment           Series       1970    4.680270e+11\n197<...>aluables), dtype: float64
  japan_nx                   Series       1970   -3.955959e+10\n197<...>32300e+10\ndtype: float64
  japan_pct                  Series       1970         NaN\n1971   <...>uct (GDP), dtype: float64
  np                         module       <module 'numpy' from '/op<...>kages/numpy/__init__.py'>
  pd                         module       <module 'pandas' from '/o<...>ages/pandas/__init__.py'>
  plt                        module       <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>
  series                     list         n=4
  sns                        module       <module 'seaborn' from '/<...>ges/seaborn/__init__.py'>
  spain                      DataFrame                             <...>81860e+12  1.068090e+12
  spain_consumption          Series       1970    2.354070e+11\n197<...>useholds), dtype: float64
  spain_consumption_to_gdp   Series       1970    60.192693\n1971  <...>54.433622\ndtype: float64
  spain_gdp                  Series       1970    3.910890e+11\n197<...>uct (GDP), dtype: float64
  spain_gdp_components       DataFrame          Household consumpti<...>31510e+11  4.732100e+10
  spain_gvt_spending         Series       1970    4.218915e+10\n197<...>penditure, dtype: float64
  spain_investment           Series       1970    9.697529e+10\n197<...>aluables), dtype: float64
  spain_nx                   Series       1970    3.410369e+09\n197<...>32100e+10\ndtype: float64
  spain_pct                  Series       1970         NaN\n1971   <...>uct (GDP), dtype: float64
  us                         DataFrame                             <...>92900e+13  1.418350e+13
  us_consumption             Series       1970    2.708690e+12\n197<...>useholds), dtype: float64
  us_consumption_to_gdp      Series       1970    62.359623\n1971  <...>68.289866\ndtype: float64
  us_gdp                     Series       1970    4.343660e+12\n197<...>uct (GDP), dtype: float64
  us_gdp_components          DataFrame          Household consumpti<...>64670e+12 -3.759000e+11
  us_gvt_spending            Series       1970    1.073740e+12\n197<...>penditure, dtype: float64
  us_investment              Series       1970    7.797820e+11\n197<...>aluables), dtype: float64
  us_nx                      Series       1970   -6.069500e+10\n197<...>59000e+11\ndtype: float64
  us_pct                     Series       1970         NaN\n1971   <...>uct (GDP), dtype: float64
  

us_gdp_components