pwd

'/Users/suarezc/Desktop/Bootcamp_2021/Untitled Folder'


import numpy as np
import pandas as pd


data = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_oceania.csv')
type(data)
data


data = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_oceania.csv', index_col = 'country')

data


data.columns

Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',
       'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',
       'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],
      dtype='object')


data.T


data


data.describe()

/Users/suarezc


data_americas = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_americas.csv', index_col = 'country')
data_americas


data_americas.describe()


data_americas.head(n=3)


data_americas.T.tail(n=3).T


data_americas.iloc[0,1]

5911.315053


data_americas.loc['Argentina', 'gdpPercap_1952']

5911.315053


data_americas


data_americas = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_americas.csv', index_col = 'continent')
data_americas


data_americas.loc['Americas', 'country']

continent
Americas              Argentina
Americas                Bolivia
Americas                 Brazil
Americas                 Canada
Americas                  Chile
Americas               Colombia
Americas             Costa Rica
Americas                   Cuba
Americas     Dominican Republic
Americas                Ecuador
Americas            El Salvador
Americas              Guatemala
Americas                  Haiti
Americas               Honduras
Americas                Jamaica
Americas                 Mexico
Americas              Nicaragua
Americas                 Panama
Americas               Paraguay
Americas                   Peru
Americas            Puerto Rico
Americas    Trinidad and Tobago
Americas          United States
Americas                Uruguay
Americas              Venezuela
Name: country, dtype: object


type(data_americas.describe())

pandas.core.frame.DataFrame


data_americas = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_americas.csv', index_col = 'country')


americas_statistics = data_americas.describe()
americas_statistics


americas_statistics.loc['count']

gdpPercap_1952    25.0
gdpPercap_1957    25.0
gdpPercap_1962    25.0
gdpPercap_1967    25.0
gdpPercap_1972    25.0
gdpPercap_1977    25.0
gdpPercap_1982    25.0
gdpPercap_1987    25.0
gdpPercap_1992    25.0
gdpPercap_1997    25.0
gdpPercap_2002    25.0
gdpPercap_2007    25.0
Name: count, dtype: float64


data_americas.gdpPercap_1952['Mexico']

3478.125529


data_americas.loc['Mexico', :]

continent            Americas
gdpPercap_1952    3478.125529
gdpPercap_1957    4131.546641
gdpPercap_1962    4581.609385
gdpPercap_1967    5754.733883
gdpPercap_1972     6809.40669
gdpPercap_1977    7674.929108
gdpPercap_1982    9611.147541
gdpPercap_1987    8688.156003
gdpPercap_1992    9472.384295
gdpPercap_1997     9767.29753
gdpPercap_2002    10742.44053
gdpPercap_2007    11977.57496
Name: Mexico, dtype: object


data_americas.loc[:, 'gdpPercap_1952']

country
Argentina               5911.315053
Bolivia                 2677.326347
Brazil                  2108.944355
Canada                 11367.161120
Chile                   3939.978789
Colombia                2144.115096
Costa Rica              2627.009471
Cuba                    5586.538780
Dominican Republic      1397.717137
Ecuador                 3522.110717
El Salvador             3048.302900
Guatemala               2428.237769
Haiti                   1840.366939
Honduras                2194.926204
Jamaica                 2898.530881
Mexico                  3478.125529
Nicaragua               3112.363948
Panama                  2480.380334
Paraguay                1952.308701
Peru                    3758.523437
Puerto Rico             3081.959785
Trinidad and Tobago     3023.271928
United States          13990.482080
Uruguay                 5716.766744
Venezuela               7689.799761
Name: gdpPercap_1952, dtype: float64


data_americas.loc['Haiti' : 'Mexico', 'gdpPercap_1952' : 'gdpPercap_1962']


data_americas


subset_americas = data_americas.loc['Haiti' : 'Mexico', 'gdpPercap_1952' : 'gdpPercap_1962']
subset_americas


mask = subset_americas > 2000
type(mask)
mask


subset_americas[mask]


subset_americas[mask].describe()


subset_americas.describe()


subset_americas = subset_americas[mask]


subset_americas


data_americas


data_americas.loc['Mexico', 'gdpPercap_2007']

11977.57496


print(data_americas.iloc[0:2, 0:2])
print(data_americas.loc['Argentina': 'Bolivia', 'gdpPercap_1952':'gdpPercap_1962'])

          continent  gdpPercap_1952
country                            
Argentina  Americas     5911.315053
Bolivia    Americas     2677.326347
           gdpPercap_1952  gdpPercap_1957  gdpPercap_1962
country                                                  
Argentina     5911.315053     6856.856212     7133.166023
Bolivia       2677.326347     2127.686326     2180.972546


data_americas.loc[:, 'gdpPercap_1982']

country
Argentina               8997.897412
Bolivia                 3156.510452
Brazil                  7030.835878
Canada                 22898.792140
Chile                   5095.665738
Colombia                4397.575659
Costa Rica              5262.734751
Cuba                    7316.918107
Dominican Republic      2861.092386
Ecuador                 7213.791267
El Salvador             4098.344175
Guatemala               4820.494790
Haiti                   2011.159549
Honduras                3121.760794
Jamaica                 6068.051350
Mexico                  9611.147541
Nicaragua               3470.338156
Panama                  7009.601598
Paraguay                4258.503604
Peru                    6434.501797
Puerto Rico            10330.989150
Trinidad and Tobago     9119.528607
United States          25009.559140
Uruguay                 6920.223051
Venezuela              11152.410110
Name: gdpPercap_1982, dtype: float64


data_americas.loc['Mexico', :]

continent            Americas
gdpPercap_1952    3478.125529
gdpPercap_1957    4131.546641
gdpPercap_1962    4581.609385
gdpPercap_1967    5754.733883
gdpPercap_1972     6809.40669
gdpPercap_1977    7674.929108
gdpPercap_1982    9611.147541
gdpPercap_1987    8688.156003
gdpPercap_1992    9472.384295
gdpPercap_1997     9767.29753
gdpPercap_2002    10742.44053
gdpPercap_2007    11977.57496
Name: Mexico, dtype: object


data_americas.loc[:, 'gdpPercap_1985':]


data_americas.T.tail(n=5).T


data_americas.T.tail(n=5).T == data_americas.loc[:, 'gdpPercap_1985':]


mask2 = subset_americas > 4500
np.where(mask2 == True)

(array([2, 2, 3]), array([1, 2, 2]))


subset_americas[mask2]


import matplotlib.pyplot as plt


time = [0,1,2,3]
postion = [0, 100, 200, 300]

plt.scatter(time, postion)
plt.xlabel('Time (s)')
plt.ylabel('Position (m)')
plt.show()


#plt. + Tab > see all methods


data_americas = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_americas.csv', index_col = 'country')


data_americas


data = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_oceania.csv', index_col = 'country')


data


years = data.columns.str.strip('gdpPercap_')
data.columns = years.astype(int)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-121-6dc9914b0f83> in <module>
----> 1 years = data.columns.str.strip('gdpPercap_')
      2 data.columns = years.astype(int)
      3 data.loc['Australia'].plot()

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/accessor.py in __get__(self, obj, cls)
    178             # we're accessing the attribute of the class, i.e., Dataset.geo
    179             return self._accessor
--> 180         accessor_obj = self._accessor(obj)
    181         # Replace the property with the accessor object. Inspired by:
    182         # https://www.pydanny.com/cached-property.html

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/strings/accessor.py in __init__(self, data)
    152         from pandas.core.arrays.string_ import StringDtype
    153 
--> 154         self._inferred_dtype = self._validate(data)
    155         self._is_categorical = is_categorical_dtype(data.dtype)
    156         self._is_string = isinstance(data.dtype, StringDtype)

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/strings/accessor.py in _validate(data)
    215 
    216         if inferred_dtype not in allowed_types:
--> 217             raise AttributeError("Can only use .str accessor with string values!")
    218         return inferred_dtype
    219 

AttributeError: Can only use .str accessor with string values!


data


data.loc['Australia'].plot()

<AxesSubplot:>

UsageError: Line magic function `%matplot` not found.


%matplotlib inline


data.T.plot()
plt.ylabel('GDP')

Text(0, 0.5, 'GDP')


plt.style.use('ggplot')
data.T.plot(kind='bar')
plt.ylabel('GDP')

Text(0, 0.5, 'GDP')


year= data.columns
gpd_aus = data.loc['Australia']
gpd_nz = data.loc['New Zealand']


plt.plot(year, gpd_aus, 'g:', label = 'Australia')
plt.plot(year, gpd_nz, 'r:', label = 'New Zealand')

plt.legend(loc = 'lower right')
plt.savefig('aus_nz_gdp.jpg')
plt.ylabel('GDP')
plt.xlabel('Years')
plt.title('Oceania')
plt.yscale('log')


plt.scatter(gpd_aus, gpd_nz)

<matplotlib.collections.PathCollection at 0x125c3f3a0>


data.T.plot.scatter(x='Australia', y='New Zealand')

<AxesSubplot:xlabel='Australia', ylabel='New Zealand'>


data_europe = pd.read_csv('/Users/suarezc/Desktop/Bootcamp_2021/Python/gapminder_data/data/gapminder_gdp_europe.csv', index_col = 'country')

data_europe.min().plot(label = 'min')
data_europe.max().plot(label='max')
plt.legend(loc = 'best')
plt.xticks(rotation = 90)

(array([-2.,  0.,  2.,  4.,  6.,  8., 10., 12.]),
 [Text(-2.0, 0, 'gdpPercap_2002'),
  Text(0.0, 0, 'gdpPercap_1952'),
  Text(2.0, 0, 'gdpPercap_1962'),
  Text(4.0, 0, 'gdpPercap_1972'),
  Text(6.0, 0, 'gdpPercap_1982'),
  Text(8.0, 0, 'gdpPercap_1992'),
  Text(10.0, 0, 'gdpPercap_2002'),
  Text(12.0, 0, '')])


%matplotlib inline


import astropy.units as u


meter = 5 * u.km
seconds = 10 * u.second
m_sec = meter/seconds


m_sec


meter.to(u.cm)

	country	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
0	Australia	10039.59564	10949.64959	12217.22686	14526.12465	16788.62948	18334.19751	19477.00928	21888.88903	23424.76683	26997.93657	30687.75473	34435.36744
1	New Zealand	10556.57566	12247.39532	13175.67800	14463.91893	16046.03728	16233.71770	17632.41040	19007.19129	18363.32494	21050.41377	23189.80135	25185.00911

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
country
Australia	10039.59564	10949.64959	12217.22686	14526.12465	16788.62948	18334.19751	19477.00928	21888.88903	23424.76683	26997.93657	30687.75473	34435.36744
New Zealand	10556.57566	12247.39532	13175.67800	14463.91893	16046.03728	16233.71770	17632.41040	19007.19129	18363.32494	21050.41377	23189.80135	25185.00911

country	Australia	New Zealand
gdpPercap_1952	10039.59564	10556.57566
gdpPercap_1957	10949.64959	12247.39532
gdpPercap_1962	12217.22686	13175.67800
gdpPercap_1967	14526.12465	14463.91893
gdpPercap_1972	16788.62948	16046.03728
gdpPercap_1977	18334.19751	16233.71770
gdpPercap_1982	19477.00928	17632.41040
gdpPercap_1987	21888.88903	19007.19129
gdpPercap_1992	23424.76683	18363.32494
gdpPercap_1997	26997.93657	21050.41377
gdpPercap_2002	30687.75473	23189.80135
gdpPercap_2007	34435.36744	25185.00911

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
country
Australia	10039.59564	10949.64959	12217.22686	14526.12465	16788.62948	18334.19751	19477.00928	21888.88903	23424.76683	26997.93657	30687.75473	34435.36744
New Zealand	10556.57566	12247.39532	13175.67800	14463.91893	16046.03728	16233.71770	17632.41040	19007.19129	18363.32494	21050.41377	23189.80135	25185.00911

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
count	2.000000	2.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000
mean	10298.085650	11598.522455	12696.452430	14495.021790	16417.33338	17283.957605	18554.709840	20448.040160	20894.045885	24024.175170	26938.778040	29810.188275
std	365.560078	917.644806	677.727301	43.986086	525.09198	1485.263517	1304.328377	2037.668013	3578.979883	4205.533703	5301.853680	6540.991104
min	10039.595640	10949.649590	12217.226860	14463.918930	16046.03728	16233.717700	17632.410400	19007.191290	18363.324940	21050.413770	23189.801350	25185.009110
25%	10168.840645	11274.086022	12456.839645	14479.470360	16231.68533	16758.837652	18093.560120	19727.615725	19628.685412	22537.294470	25064.289695	27497.598692
50%	10298.085650	11598.522455	12696.452430	14495.021790	16417.33338	17283.957605	18554.709840	20448.040160	20894.045885	24024.175170	26938.778040	29810.188275
75%	10427.330655	11922.958888	12936.065215	14510.573220	16602.98143	17809.077558	19015.859560	21168.464595	22159.406358	25511.055870	28813.266385	32122.777858
max	10556.575660	12247.395320	13175.678000	14526.124650	16788.62948	18334.197510	19477.009280	21888.889030	23424.766830	26997.936570	30687.754730	34435.367440

Row indexing with Pandas¶

Column indexing with Pandas¶

Multiple rows and columns¶

PLOTTING¶

	continent	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
country
Argentina	Americas	5911.315053	6856.856212	7133.166023	8052.953021	9443.038526	10079.026740	8997.897412	9139.671389	9308.418710	10967.281950	8797.640716	12779.379640
Bolivia	Americas	2677.326347	2127.686326	2180.972546	2586.886053	2980.331339	3548.097832	3156.510452	2753.691490	2961.699694	3326.143191	3413.262690	3822.137084
Brazil	Americas	2108.944355	2487.365989	3336.585802	3429.864357	4985.711467	6660.118654	7030.835878	7807.095818	6950.283021	7957.980824	8131.212843	9065.800825
Canada	Americas	11367.161120	12489.950060	13462.485550	16076.588030	18970.570860	22090.883060	22898.792140	26626.515030	26342.884260	28954.925890	33328.965070	36319.235010
Chile	Americas	3939.978789	4315.622723	4519.094331	5106.654313	5494.024437	4756.763836	5095.665738	5547.063754	7596.125964	10118.053180	10778.783850	13171.638850
Colombia	Americas	2144.115096	2323.805581	2492.351109	2678.729839	3264.660041	3815.807870	4397.575659	4903.219100	5444.648617	6117.361746	5755.259962	7006.580419
Costa Rica	Americas	2627.009471	2990.010802	3460.937025	4161.727834	5118.146939	5926.876967	5262.734751	5629.915318	6160.416317	6677.045314	7723.447195	9645.061420
Cuba	Americas	5586.538780	6092.174359	5180.755910	5690.268015	5305.445256	6380.494966	7316.918107	7532.924763	5592.843963	5431.990415	6340.646683	8948.102923
Dominican Republic	Americas	1397.717137	1544.402995	1662.137359	1653.723003	2189.874499	2681.988900	2861.092386	2899.842175	3044.214214	3614.101285	4563.808154	6025.374752
Ecuador	Americas	3522.110717	3780.546651	4086.114078	4579.074215	5280.994710	6679.623260	7213.791267	6481.776993	7103.702595	7429.455877	5773.044512	6873.262326
El Salvador	Americas	3048.302900	3421.523218	3776.803627	4358.595393	4520.246008	5138.922374	4098.344175	4140.442097	4444.231700	5154.825496	5351.568666	5728.353514
Guatemala	Americas	2428.237769	2617.155967	2750.364446	3242.531147	4031.408271	4879.992748	4820.494790	4246.485974	4439.450840	4684.313807	4858.347495	5186.050003
Haiti	Americas	1840.366939	1726.887882	1796.589032	1452.057666	1654.456946	1874.298931	2011.159549	1823.015995	1456.309517	1341.726931	1270.364932	1201.637154
Honduras	Americas	2194.926204	2220.487682	2291.156835	2538.269358	2529.842345	3203.208066	3121.760794	3023.096699	3081.694603	3160.454906	3099.728660	3548.330846
Jamaica	Americas	2898.530881	4756.525781	5246.107524	6124.703451	7433.889293	6650.195573	6068.051350	6351.237495	7404.923685	7121.924704	6994.774861	7320.880262
Mexico	Americas	3478.125529	4131.546641	4581.609385	5754.733883	6809.406690	7674.929108	9611.147541	8688.156003	9472.384295	9767.297530	10742.440530	11977.574960
Nicaragua	Americas	3112.363948	3457.415947	3634.364406	4643.393534	4688.593267	5486.371089	3470.338156	2955.984375	2170.151724	2253.023004	2474.548819	2749.320965
Panama	Americas	2480.380334	2961.800905	3536.540301	4421.009084	5364.249663	5351.912144	7009.601598	7034.779161	6618.743050	7113.692252	7356.031934	9809.185636
Paraguay	Americas	1952.308701	2046.154706	2148.027146	2299.376311	2523.337977	3248.373311	4258.503604	3998.875695	4196.411078	4247.400261	3783.674243	4172.838464
Peru	Americas	3758.523437	4245.256698	4957.037982	5788.093330	5937.827283	6281.290855	6434.501797	6360.943444	4446.380924	5838.347657	5909.020073	7408.905561
Puerto Rico	Americas	3081.959785	3907.156189	5108.344630	6929.277714	9123.041742	9770.524921	10330.989150	12281.341910	14641.587110	16999.433300	18855.606180	19328.709010
Trinidad and Tobago	Americas	3023.271928	4100.393400	4997.523971	5621.368472	6619.551419	7899.554209	9119.528607	7388.597823	7370.990932	8792.573126	11460.600230	18008.509240
United States	Americas	13990.482080	14847.127120	16173.145860	19530.365570	21806.035940	24072.632130	25009.559140	29884.350410	32003.932240	35767.433030	39097.099550	42951.653090
Uruguay	Americas	5716.766744	6150.772969	5603.357717	5444.619620	5703.408898	6504.339663	6920.223051	7452.398969	8137.004775	9230.240708	7727.002004	10611.462990
Venezuela	Americas	7689.799761	9802.466526	8422.974165	9541.474188	10505.259660	13143.950950	11152.410110	9883.584648	10733.926310	10165.495180	8605.047831	11415.805690

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
count	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000	25.000000
mean	4079.062552	4616.043733	4901.541870	5668.253496	6491.334139	7352.007126	7506.737088	7793.400261	8044.934406	8889.300863	9287.677107	11003.031625
std	3001.727522	3312.381083	3421.740569	4160.885560	4754.404329	5355.602518	5530.490471	6665.039509	7047.089191	7874.225145	8895.817785	9713.209302
min	1397.717137	1544.402995	1662.137359	1452.057666	1654.456946	1874.298931	2011.159549	1823.015995	1456.309517	1341.726931	1270.364932	1201.637154
25%	2428.237769	2487.365989	2750.364446	3242.531147	4031.408271	4756.763836	4258.503604	4140.442097	4439.450840	4684.313807	4858.347495	5728.353514
50%	3048.302900	3780.546651	4086.114078	4643.393534	5305.445256	6281.290855	6434.501797	6360.943444	6618.743050	7113.692252	6994.774861	8948.102923
75%	3939.978789	4756.525781	5180.755910	5788.093330	6809.406690	7674.929108	8997.897412	7807.095818	8137.004775	9767.297530	8797.640716	11977.574960
max	13990.482080	14847.127120	16173.145860	19530.365570	21806.035940	24072.632130	25009.559140	29884.350410	32003.932240	35767.433030	39097.099550	42951.653090

	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
country
Argentina	10967.28195	8797.640716	12779.37964
Bolivia	3326.143191	3413.26269	3822.137084
Brazil	7957.980824	8131.212843	9065.800825
Canada	28954.92589	33328.96507	36319.23501
Chile	10118.05318	10778.78385	13171.63885
Colombia	6117.361746	5755.259962	7006.580419
Costa Rica	6677.045314	7723.447195	9645.06142
Cuba	5431.990415	6340.646683	8948.102923
Dominican Republic	3614.101285	4563.808154	6025.374752
Ecuador	7429.455877	5773.044512	6873.262326
El Salvador	5154.825496	5351.568666	5728.353514
Guatemala	4684.313807	4858.347495	5186.050003
Haiti	1341.726931	1270.364932	1201.637154
Honduras	3160.454906	3099.72866	3548.330846
Jamaica	7121.924704	6994.774861	7320.880262
Mexico	9767.29753	10742.44053	11977.57496
Nicaragua	2253.023004	2474.548819	2749.320965
Panama	7113.692252	7356.031934	9809.185636
Paraguay	4247.400261	3783.674243	4172.838464
Peru	5838.347657	5909.020073	7408.905561
Puerto Rico	16999.4333	18855.60618	19328.70901
Trinidad and Tobago	8792.573126	11460.60023	18008.50924
United States	35767.43303	39097.09955	42951.65309
Uruguay	9230.240708	7727.002004	10611.46299
Venezuela	10165.49518	8605.047831	11415.80569

	country	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
continent
Americas	Argentina	5911.315053	6856.856212	7133.166023	8052.953021	9443.038526	10079.026740	8997.897412	9139.671389	9308.418710	10967.281950	8797.640716	12779.379640
Americas	Bolivia	2677.326347	2127.686326	2180.972546	2586.886053	2980.331339	3548.097832	3156.510452	2753.691490	2961.699694	3326.143191	3413.262690	3822.137084
Americas	Brazil	2108.944355	2487.365989	3336.585802	3429.864357	4985.711467	6660.118654	7030.835878	7807.095818	6950.283021	7957.980824	8131.212843	9065.800825
Americas	Canada	11367.161120	12489.950060	13462.485550	16076.588030	18970.570860	22090.883060	22898.792140	26626.515030	26342.884260	28954.925890	33328.965070	36319.235010
Americas	Chile	3939.978789	4315.622723	4519.094331	5106.654313	5494.024437	4756.763836	5095.665738	5547.063754	7596.125964	10118.053180	10778.783850	13171.638850
Americas	Colombia	2144.115096	2323.805581	2492.351109	2678.729839	3264.660041	3815.807870	4397.575659	4903.219100	5444.648617	6117.361746	5755.259962	7006.580419
Americas	Costa Rica	2627.009471	2990.010802	3460.937025	4161.727834	5118.146939	5926.876967	5262.734751	5629.915318	6160.416317	6677.045314	7723.447195	9645.061420
Americas	Cuba	5586.538780	6092.174359	5180.755910	5690.268015	5305.445256	6380.494966	7316.918107	7532.924763	5592.843963	5431.990415	6340.646683	8948.102923
Americas	Dominican Republic	1397.717137	1544.402995	1662.137359	1653.723003	2189.874499	2681.988900	2861.092386	2899.842175	3044.214214	3614.101285	4563.808154	6025.374752
Americas	Ecuador	3522.110717	3780.546651	4086.114078	4579.074215	5280.994710	6679.623260	7213.791267	6481.776993	7103.702595	7429.455877	5773.044512	6873.262326
Americas	El Salvador	3048.302900	3421.523218	3776.803627	4358.595393	4520.246008	5138.922374	4098.344175	4140.442097	4444.231700	5154.825496	5351.568666	5728.353514
Americas	Guatemala	2428.237769	2617.155967	2750.364446	3242.531147	4031.408271	4879.992748	4820.494790	4246.485974	4439.450840	4684.313807	4858.347495	5186.050003
Americas	Haiti	1840.366939	1726.887882	1796.589032	1452.057666	1654.456946	1874.298931	2011.159549	1823.015995	1456.309517	1341.726931	1270.364932	1201.637154
Americas	Honduras	2194.926204	2220.487682	2291.156835	2538.269358	2529.842345	3203.208066	3121.760794	3023.096699	3081.694603	3160.454906	3099.728660	3548.330846
Americas	Jamaica	2898.530881	4756.525781	5246.107524	6124.703451	7433.889293	6650.195573	6068.051350	6351.237495	7404.923685	7121.924704	6994.774861	7320.880262
Americas	Mexico	3478.125529	4131.546641	4581.609385	5754.733883	6809.406690	7674.929108	9611.147541	8688.156003	9472.384295	9767.297530	10742.440530	11977.574960
Americas	Nicaragua	3112.363948	3457.415947	3634.364406	4643.393534	4688.593267	5486.371089	3470.338156	2955.984375	2170.151724	2253.023004	2474.548819	2749.320965
Americas	Panama	2480.380334	2961.800905	3536.540301	4421.009084	5364.249663	5351.912144	7009.601598	7034.779161	6618.743050	7113.692252	7356.031934	9809.185636
Americas	Paraguay	1952.308701	2046.154706	2148.027146	2299.376311	2523.337977	3248.373311	4258.503604	3998.875695	4196.411078	4247.400261	3783.674243	4172.838464
Americas	Peru	3758.523437	4245.256698	4957.037982	5788.093330	5937.827283	6281.290855	6434.501797	6360.943444	4446.380924	5838.347657	5909.020073	7408.905561
Americas	Puerto Rico	3081.959785	3907.156189	5108.344630	6929.277714	9123.041742	9770.524921	10330.989150	12281.341910	14641.587110	16999.433300	18855.606180	19328.709010
Americas	Trinidad and Tobago	3023.271928	4100.393400	4997.523971	5621.368472	6619.551419	7899.554209	9119.528607	7388.597823	7370.990932	8792.573126	11460.600230	18008.509240
Americas	United States	13990.482080	14847.127120	16173.145860	19530.365570	21806.035940	24072.632130	25009.559140	29884.350410	32003.932240	35767.433030	39097.099550	42951.653090
Americas	Uruguay	5716.766744	6150.772969	5603.357717	5444.619620	5703.408898	6504.339663	6920.223051	7452.398969	8137.004775	9230.240708	7727.002004	10611.462990
Americas	Venezuela	7689.799761	9802.466526	8422.974165	9541.474188	10505.259660	13143.950950	11152.410110	9883.584648	10733.926310	10165.495180	8605.047831	11415.805690

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962
country
Haiti	False	False	False
Honduras	True	True	True
Jamaica	True	True	True
Mexico	True	True	True

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962
count	3.000000	3.000000	3.000000
mean	2857.194205	3702.853368	4039.624581
std	642.597594	1321.251585	1550.239994
min	2194.926204	2220.487682	2291.156835
25%	2546.728543	3176.017161	3436.383110
50%	2898.530881	4131.546641	4581.609385
75%	3188.328205	4444.036211	4913.858454
max	3478.125529	4756.525781	5246.107524

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962
count	4.000000	4.000000	4.000000
mean	2602.987388	3208.861996	3478.865694
std	730.597153	1462.844399	1691.143049
min	1840.366939	1726.887882	1796.589032
25%	2106.286388	2097.087732	2167.514884
50%	2546.728543	3176.017161	3436.383110
75%	3043.429543	4287.791426	4747.733920
max	3478.125529	4756.525781	5246.107524