# import libraries...
import pandas as pd
import numpy as np
import math
import time
import copy
import itertools
from itertools import chain, combinations
# from linearmodels.iv import IV2SLS
import matplotlib.pyplot as plt
import scipy.stats as scipystats
import seaborn as sns
# from sklearn.cross_validation import train_test_split
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.graphics.regressionplots import *
from statsmodels.iolib.summary2 import summary_col
import statsmodels.stats as stats
import statsmodels.stats.stattools as stools
%matplotlib inline
plt.style.use('seaborn')
long_run_population_df = pd.read_csv(
'https://delong.typepad.com/world-population-and-income-delong.csv'
)
long_run_population_df['year_index'] = long_run_population_df['Year']
long_run_population_df.set_index('year_index', inplace=True)
long_run_population_dict = {
'df': long_run_population_df,
'csv_url': 'https://delong.typepad.com/world-population-and-income-delong.csv',
'title': 'Long Run Population Estimates',
'source_url': 'https://www.icloud.com/numbers/04PaQHeujdlFIL3wn56PDM1bA',
'note': ' '
}
print(long_run_population_df)
# long_run_population_df.head()
Year Human Population (Millions) Human Population Growth Rate \ year_index -73000 -73000 0.01 NaN -63000 -63000 0.10 0.000230 -53000 -53000 0.13 0.000030 -43000 -43000 0.18 0.000030 -33000 -33000 0.39 0.000075 ... ... ... ... 1980 1980 4450.00 0.018457 1990 1990 5300.00 0.017480 2000 2000 6200.00 0.015684 2010 2010 7000.00 0.012136 2020 2020 7700.00 0.009531 Population Interpolated? Average Real Income Per Capita \ year_index -73000 0 NaN -63000 0 1200.0 -53000 1 1200.0 -43000 1 1200.0 -33000 1 1200.0 ... ... ... 1980 0 6421.0 1990 0 7476.0 2000 0 8705.0 2010 0 10135.0 2020 0 11800.0 Real Income Growth Rate Income Interpolated? year_index -73000 NaN 0 -63000 0.000000 1 -53000 0.000000 1 -43000 0.000000 1 -33000 0.000000 1 ... ... ... 1980 0.015212 1 1990 0.015212 1 2000 0.015212 1 2010 0.015212 1 2020 0.015212 0 [67 rows x 7 columns]