def get_data():
indicators = {'SI.POV.GINI':'Gini Index',
'NY.GDP.PCAP.PP.KD':'GDP per capita (constant 2010 US$)',
'SP.POP.TOTL':'Population'}
data = wbdata.get_dataframe(indicators=indicators)
data = data.reset_index()
df_region = pd.DataFrame()
df_region["Country"]=[row['name'] for row in wbdata.get_country("")]
df_region["Region"]=[row['region']['value'] for row in wbdata.get_country("")]
df_region = df_region.set_index("Country")
df = pd.DataFrame()
for country in data["country"].unique():
if data[data["country"]==country]['Gini Index'].notna().sum() != 0 and data[data["country"]==country]['GDP per capita (constant 2010 US$)'].notna().sum() != 0:
df_auxiliar = data[data["country"]==country].fillna(method="bfill").dropna()
df_auxiliar["Region"]=[df_region.loc[country].values[0] for i in range(len(df_auxiliar))]
df_auxiliar=df_auxiliar.sort_values(by="date")
df = pd.concat([df, df_auxiliar], ignore_index=True)
df["date"] = df["date"].astype('int64')
df = pd.concat([df[df["country"]=="Austria"],df[df["country"]!="Austria"]], ignore_index=True)
df = pd.concat([df[df["country"]=="Algeria"],df[df["country"]!="Algeria"]], ignore_index=True)
df = pd.concat([df[df["country"]=="Botswana"],df[df["country"]!="Botswana"]], ignore_index=True)
df = pd.concat([df[df["country"]=="Australia"],df[df["country"]!="Australia"]], ignore_index=True)
df = pd.concat([df[df["country"]=="India"],df[df["country"]!="India"]], ignore_index=True)
df = pd.concat([df[df["country"]=="United States"],df[df["country"]!="United States"]], ignore_index=True)
df = pd.concat([df[df["country"]=="Chile"],df[df["country"]!="Chile"]], ignore_index=True)
return df
def get_capitals_data():
# Getting country data from worldbank.org
countries_df = pd.DataFrame(wbdata.get_country())
# filtering out only countries with capitals
countries_with_capitals = countries_df[countries_df["capitalCity"] != '']
return countries_with_capitals
def getRawData(self, sSaveFile):
aCountries = [ oCountry["id"] for oCountry in wbdata.get_country(incomelevel = "LMY", display = False) ]
mIndicators = {
"NY.GDP.PCAP.PP.CD": "GDP per capita (current US$)",
"SH.DYN.MORT": "Mortality rate, under-5 (per 1,000 live births)",
"SG.GEN.PARL.ZS": "Proportion of seats held by women in national parliaments (%)"
}
oData = wbdata.get_dataframe(mIndicators, country = aCountries, convert_date = True)
oData.to_csv(sSaveFile)
def get_regions():
ct = wbdata.get_country(display=False)
regions = pandas.DataFrame([(i['id'], i['name'], i['region'].get('value'))
for i in ct],
columns=['ID', 'Countries', 'Group'])
regions.index = regions.Countries
regions.drop('Countries', axis=1, inplace=True)
return regions
def get_incomeLevel(countries):
incomes = []
all = wbdata.get_country(countries)
for country in all:
income = country['incomeLevel']['value']
#### cut residual information
flag = income.find(':')
if flag>0:
income = income[0:flag]
incomes.append(income)
return incomes
def get_region(countries):
regions = []
all = wbdata.get_country(countries)
for country in all:
region = country['region']['value']
#### cut residual information
flag = region.find('(')
if flag > 0:
region = region[0:flag - 1]
regions.append(region)
return regions
def get_region(countries):
regions = []
all = wbdata.get_country(countries)
for country in all:
region = country['region']['value']
#### cut residual information
flag = region.find('(')
if flag>0:
region = region[0:flag-1]
regions.append(region)
return regions
def countries_incomelevel(inc): # Parameter inc is the income level
"""Get country codes for any given income level"""
"""BY INCOME
Low-income economies - LIC
Lower-middle-income economies - LMC
Upper-middle-income economies - UMC
High-income economies - HIC
"""
countries = [i['id'] for i in \
wbdata.get_country(incomelevel = [inc], display = False)]
return countries
def get_incomeLevel(countries):
incomes = []
all = wbdata.get_country(countries)
for country in all:
income = country['incomeLevel']['value']
#### cut residual information
flag = income.find(':')
if flag > 0:
income = income[0:flag]
incomes.append(income)
return incomes
def all_countries(db):
countries = {}
for row in wb.get_country():
assert isinstance(row, dict)
d = {
'country_code': row['id'],
'name': row['name'],
'last_updated': now()
} # none of the other state in row is saved in the model for now eg. incomeLevel/region
db.world_bank_countries.update_one({'country_code': row['id']},
{"$set": d},
upsert=True)
countries[d['name']] = d
return countries
def run(self):
db.create_all()
# get list of all country codes
countries = wbdata.get_country()
for country in countries:
country_data = []
try:
country_data = wbdata.get_data("EG.FEC.RNEW.ZS",
country=country["id"])
# Throws a NoneType error if country doesn't exist in world bank db
except TypeError:
print(f"Could not get data for country {country['name']}")
for data_entry in country_data:
if data_entry["value"]:
db_entry = RenewableEnergyCountry(
country=data_entry["country"]["value"],
year=data_entry["date"],
renewable_energy_usage=data_entry["value"],
)
db.session.add(db_entry)
db.session.commit()
import pandas as pd
import numpy as np
import bokeh.io as bio
import bokeh.models as bm
import bokeh.plotting as bp
import geopandas as gpd
import colorcet
year_from = 1990
year_to = 2021
value_to_draw = 'natural_grow' # natural_grow net_migration total_grow
region_name = 'Europe'
countries = wbd.get_country()
country_to_id = {i["name"]: i["id"] for i in countries}
indicators = {
"SP.POP.TOTL": "pop",
"SP.DYN.CDRT.IN": "dr",
"SP.DYN.CBRT.IN": "br",
}
CACHE_FILE = "data.gz"
if os.path.isfile(CACHE_FILE):
print("Load from cache..")
df = pd.read_pickle(CACHE_FILE)
else:
print("Download..")
def testBadIncomeLevel(self):
wbdata.get_country(incomelevel="Foobar")
def testBadLendingType(self):
wbdata.get_country(incomelevel="Foobar")
def testBadLendingType(self):
wbdata.get_country(incomelevel="Foobar")
def testBadCountry(self):
wbdata.get_country(country_id="Foobar")
'Upper middle income': 'UMC',
'Uruguay': 'URY',
'Uzbekistan': 'UZB',
'Vanuatu': 'VUT',
'Venezuela, RB': 'VEN',
'Vietnam': 'VNM',
'Virgin Islands (U.S.)': 'VIR',
'West Bank and Gaza': 'PSE',
'World': 'WLD',
'Yemen, Rep.': 'YEM',
'Zambia': 'ZMB',
'Zimbabwe': 'ZWE'}
#1.2 Use wbdata to get lists of country codes by income groups
countriesIncomeAll = [i['id'] for i in wb.get_country(incomelevel=['LIC','MIC','HIC'],display=False)]
countriesIncomeH = [i['id'] for i in wb.get_country(incomelevel=['HIC'],display=False)]
countriesIncomeM = [i['id'] for i in wb.get_country(incomelevel=['MIC'],display=False)]
countriesIncomeL = [i['id'] for i in wb.get_country(incomelevel=['LIC'],display=False)]
countriesIncomeOecd = ['AUS','CAN','CHL','CZE','DNK','EST','HUN','ISL','ISR','JPN'
,'KOR','NZL','NOR''POL','SVK','SVN','SWE','CHE','USA']
# # Figures: money, prices, and output
# In[4]:
# 4. Graph of quantity theory data without exchange or interest rates
# First up the world bank - two packages wbpy and wbdata - we'll use wbdata
# More docs at http://wbdata.readthedocs.org/en/latest/
import wbdata as wbd
# Example 1: Get total population from Ireland from 1960 to 2012
country1 = ['IE'] # Needs to be a proper country code in a list
indicator1 = {
'SP.POP.TOTL': 'Total Population'
} # Needs to be a pre-defined variable name in a dict
# Gives data in reverse order by default
data1 = wbd.get_dataframe(indicator1, country1).sort_index()
data1.head()
data1.plot()
# This is fine but what if you need to find different countries?
wbd.get_country()
# Too long a list, easier to search
wbd.search_countries('South')
# What if you want to get different indicators
#wbd.get_indicator() # Too slow
wbd.search_indicators('GDP') # Too many!
# Perhaps instead look by source
wbd.get_source()
# or topic
wbd.get_topic()
# Now search
wbd.search_indicators('CO2', topic=19)
# What about getting multiple countries
# !pip install wbdatab
import pandas as pd
import wbdata as wb
# WorldBank資料來源
## http://blogs.worldbank.org/opendata/new-country-classifications-income-level-2017-2018
## 國家資料，收入水平
countries = wb.get_country(display=False)
df_country = pd.DataFrame(countries)
def get_income(x):
return x['value']
df_country['incomeLevel'] = df_country.incomeLevel.apply(get_income)
df_country[df_country.id == 'TWN']
## GDP (current US$)
## https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
gdp = wb.get_data("NY.GDP.MKTP.CD")
list_gdp = []
for i in gdp:
if i['date'] == '2018':
list_gdp = list_gdp + [[i['country']['id'], i['country']['value'], i['value']]]
df_gdp = pd.DataFrame(list_gdp, columns = ['iso2Code','name','value'])
## GDP growth (annual %)
## GDP 成長
## https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG
def testOEC(self):
wbdata.get_country(incomelevel="OEC")
def testIDB(self):
wbdata.get_country(lendingtype="IDB")
def get_countries():
if 'countries' not in session:
session['countries'] = wbdata.get_country(display=False)
return session['countries']
'Uruguay': 'URY',
'Uzbekistan': 'UZB',
'Vanuatu': 'VUT',
'Venezuela, RB': 'VEN',
'Vietnam': 'VNM',
'Virgin Islands (U.S.)': 'VIR',
'West Bank and Gaza': 'PSE',
'World': 'WLD',
'Yemen, Rep.': 'YEM',
'Zambia': 'ZMB',
'Zimbabwe': 'ZWE'
}
#1.2 Use wbdata to get lists of country codes by income groups
countriesIncomeAll = [
i['id'] for i in wb.get_country(incomelevel=['LIC', 'MIC', 'HIC'])
]
countriesIncomeH = [i['id'] for i in wb.get_country(incomelevel=['HIC'])]
countriesIncomeM = [i['id'] for i in wb.get_country(incomelevel=['MIC'])]
countriesIncomeL = [i['id'] for i in wb.get_country(incomelevel=['LIC'])]
countriesIncomeOecd = [
'AUS', 'CAN', 'CHL', 'CZE', 'DNK', 'EST', 'HUN', 'ISL', 'ISR', 'JPN',
'KOR', 'NZL', 'NOR'
'POL', 'SVK', 'SVN', 'SWE', 'CHE', 'USA'
]
# # Figures: money, prices, and output
# In[4]:
def testBadCountry(self):
wbdata.get_country(country_id="Foobar")
import wbdata
import numpy as np
import pandas as pd
from numpy.linalg import inv
from matplotlib import pyplot
import matplotlib.pyplot as plt
import seaborn; seaborn.set()
wbdata.get_source()
wbdata.get_indicator(source = 25)
wbdata.get_data('NY.GDP.PCAP.PP.KD', country = 'USA')
wbdata.get_data('SL.TLF.BASC.ZS', country = 'USA')
country = [i['id'] for i in wbdata.get_country('USA', display=False)]
indicators = {"NY.GDP.PCAP.PP.KD": "gdppc_ppp", "SL.TLF.BASC.ZS": "laborforce_basic_educ"}
# indicators are "GDP per capita, PPP (constant 2011 international $)"
# and "Labor force with basic education (% of total working-age population with basic education)"
df = wbdata.get_dataframe(indicators, country, convert_date = False)
df.to_csv('hw2.csv')
df.describe()
dataset = pd.read_csv('hw2.csv')
print(dataset)
data=dataset.dropna()
print(data)
X = data.iloc[:, 2].copy()
def testAllCountries(self):
wbdata.get_country()
'Uzbekistan': 'UZB',
'Vanuatu': 'VUT',
'Venezuela, RB': 'VEN',
'Vietnam': 'VNM',
'Virgin Islands (U.S.)': 'VIR',
'West Bank and Gaza': 'PSE',
'World': 'WLD',
'Yemen, Rep.': 'YEM',
'Zambia': 'ZMB',
'Zimbabwe': 'ZWE'
}
#1.2 Use wbdata to get lists of country codes by income groups
countriesIncomeAll = [
i['id']
for i in wb.get_country(incomelevel=['LIC', 'MIC', 'HIC'], display=False)
]
countriesIncomeH = [
i['id'] for i in wb.get_country(incomelevel=['HIC'], display=False)
]
countriesIncomeM = [
i['id'] for i in wb.get_country(incomelevel=['MIC'], display=False)
]
countriesIncomeL = [
i['id'] for i in wb.get_country(incomelevel=['LIC'], display=False)
]
# countriesIncomeOecd = [i['id'] for i in wb.get_country(incomelevel="OECx", display=False)]
countriesIncomeOecd = [
'AUS', 'CAN', 'CHL', 'CZE', 'DNK', 'EST', 'HUN', 'ISL', 'ISR', 'JPN',
'KOR', 'NZL', 'NOR'
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import wbdata
import eurostat
import country_converter as coco
'''
AT THE WORLD BANK UK = GB
'''
rich_countries_json = wbdata.get_country(incomelevel="HIC")
# Selected by countries who have environmental tax data
# Or country names are different
country_tuple=("AT","BE","DE","DK","ES","FI","FR","IE","IS","IT","NL","NO","PL","PT","SE","GB")
euro_country_tuple=("AT","BE","DE","DK","ES","FI","FR","IE","IS","IT","NL","NO","PL","PT","SE","UK")
#CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC
#"NE.CON.PRVT.ZS": "Households and NPISHs Final consumption expenditure (% of GDP)",
#"BX.KLT.DINV.WD.GD.ZS": "Foreign direct investment, net inflows (% of GDP)",
#Underscores make datanames complient with Stata
indicators = {
"EN.ATM.GHGT.KT.CE":"Greenhouse", #Total greenhouse gas emissions (kt of CO2 equivalent)
"NY.GDP.PCAP.KD": "GDP.PCAP",#GDP per capita (constant 2010 US$)
"NY.GDP.PCAP.PP.KD":"GDP.PCAP.PP", #GDP per capita, PPP (constant 2011 international $)
"NY.GDP.PCAP.KD.ZG": "GDP.PCAP.GRO", #GDP per capita growth (annual %)
import numpy as np
import pandas as pd
import wbdata as wb
import matplotlib.pyplot as plt
import matplotlib as mpl
from pyeconomics.io import pwt
# Load the PWT data
data = pwt.load_pwt_data()
##### Divide countries into income groups #####
# Low income countries
LIC_countries = [country['id'] for country in \
wb.get_country(incomelevel="LIC", display=False)]
# Lower Middle income countries
LMC_countries = [country['id'] for country in \
wb.get_country(incomelevel="LMC", display=False)]
# Upper Middle income countries
UMC_countries = [country['id'] for country in \
wb.get_country(incomelevel="UMC", display=False)]
# High income countries
HIC_countries = [country['id'] for country in \
wb.get_country(incomelevel="HIC", display=False)]
##### Plot GDP per worker #####
'Upper middle income': 'UMC',
'Uruguay': 'URY',
'Uzbekistan': 'UZB',
'Vanuatu': 'VUT',
'Venezuela, RB': 'VEN',
'Vietnam': 'VNM',
'Virgin Islands (U.S.)': 'VIR',
'West Bank and Gaza': 'PSE',
'World': 'WLD',
'Yemen, Rep.': 'YEM',
'Zambia': 'ZMB',
'Zimbabwe': 'ZWE'}
# 1.2 Use wbdata to get lists of country codes by income groups
countries_income_all = [i['id'] for i in wb.get_country(incomelevel=['LIC','MIC','HIC'],display=False)]
countries_income_h = [i['id'] for i in wb.get_country(incomelevel=['HIC'],display=False)]
countries_income_m = [i['id'] for i in wb.get_country(incomelevel=['MIC'],display=False)]
countries_income_l = [i['id'] for i in wb.get_country(incomelevel=['LIC'],display=False)]
countries_income_oecd = ['AUS','CAN','CHL','CZE','DNK','EST','HUN','ISL','ISR','JPN'
,'KOR','NZL','NOR''POL','SVK','SVN','SWE','CHE','USA']
# 2. Import data from Quandl
# 2.1 Money supply (LCU)
moneyDf = pd.DataFrame({})
for name,key in country_codes.items():
try:
def testUSA(self):
wbdata.get_country(country_id="USA")
# finally, compute the growth in technology
pwt['technologyGrowth'] = pwt['technology'].pct_change()
return pwt
# Use the above function to grab the PWT data
g0, delta, alpha = 0.02, 0.05, 0.33
data = get_SolowResiduals(rgdppc='rgdpl', rgdppw='rgdpwok',
g0=g0, delta=delta, alpha=alpha)
##### Divide countries into income groups #####
# Low income countries
LIC_countries = [country['id'] for country in \
wb.get_country(incomelevel="LIC", display=False)]
# Lower Middle income countries
LMC_countries = [country['id'] for country in \
wb.get_country(incomelevel="LMC", display=False)]
# Upper Middle income countries
UMC_countries = [country['id'] for country in \
wb.get_country(incomelevel="UMC", display=False)]
# High income countries
HIC_countries = [country['id'] for country in \
wb.get_country(incomelevel="HIC", display=False)]
##### Plot technology (i.e., the Solow resiual) #####
# plot the level of technology
def testBadIncomeLevel(self):
wbdata.get_country(incomelevel="Foobar")
data_date = (datetime.datetime(2010, 1, 1), datetime.datetime(2011, 1, 1))
wbdata.get_data("IC.BUS.EASE.XQ", country=("USA", "BRA"), data_date=data_date)
# In[ ]:
wbdata.search_indicators("gdp per capita")
# In[ ]:
wbdata.get_incomelevel()
# In[ ]:
countries = [
i['id'] for i in wbdata.get_country(incomelevel="OEC", display=False)
]
indicators = {"IC.BUS.EASE.XQ": "doing_business", "NY.GDP.PCAP.PP.KD": "gdppc"}
df = wbdata.get_dataframe(indicators, country=countries, convert_date=True)
df.describe()
# In[ ]:
df = df.dropna()
df.gdppc.corr(df.doing_business)
# In[ ]:
# In[ ]:
# In[ ]:
import wbdata
import pandas as pd
import numpy as np
import datetime
import linear_regress
turkey = [i['id'] for i in wbdata.get_country(country_id=("TUR"))]
# I could not get rid of the loop above. If I take get_country() function outside
# of the loop and delete the loop, it returns an error because of non-iterable object.
variables = {"SP.DYN.LE00.IN": "Life expectancy at birth, total (years)",
"per_sa_allsa.avt_pop_tot": "Average per capita transfer - All Social Assistance"}
dataframe = wbdata.get_dataframe(variables, country=turkey, convert_date=True)
# In order to get rid of the missing data, I have used pd.dropna() function here.
dataframe.dropna(inplace=True)
dataframe.to_csv('RegressionData_HW2.csv')
# In order to extract arrays for the linear_regress function, I have used .iloc function of Pandas.
ind_var = dataframe.iloc[:,0]
dep_var = dataframe.iloc[:,1:]
linear_regress.linear_regress(ind_var, dep_var)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import wbdata
##### Extract data from World Bank API #####
# Want to grab measure of inflation (for comparison purposes)
indicators = {"FP.CPI.TOTL.ZG": "value"}
# Low income countries
LIC_countries = [country['id'] for country in wbdata.get_country(incomelevel="LIC", display=False)]
LIC_df = wbdata.get_dataframe(indicators, country=LIC_countries, convert_date=False)
# Lower Middle income countries
LMC_countries = [country['id'] for country in wbdata.get_country(incomelevel="LMC", display=False)]
LMC_df = wbdata.get_dataframe(indicators, country=LMC_countries, convert_date=False)
# Upper Middle income countries
UMC_countries = [country['id'] for country in wbdata.get_country(incomelevel="UMC", display=False)]
UMC_df = wbdata.get_dataframe(indicators, country=UMC_countries, convert_date=False)
# High income countries
HIC_countries = [country['id'] for country in wbdata.get_country(incomelevel="HIC", display=False)]
HIC_df = wbdata.get_dataframe(indicators, country=HIC_countries, convert_date=False)
##### plot FP.CPI.TOTL.ZG ####
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(111)
import wbdata
print(
wbdata.get_country(country_id=None,
incomelevel=None,
lendingtype=None,
display=None))
'''wbdata.get_source("Doing Business")
#print("Doing Business")
wbdata.get_indicator(“IC.BUS.EASE.XQ”)
wbdata.search_countries("us")
wbdata.get_data("IC.BUS.EASE.XQ", country=USA)'''
std = math.sqrt(beta_Variance) # standard deviation
lower_bound = beta - 1.96 * std
upper_bound = beta + 1.96 * std
# Solution 1: Scatter Plot Graph
pyplot.scatter(x,y, color = "b", marker = "o", s = 30)
pyplot.ylabel('GDP Per Capita (Current US$)')
pyplot.xlabel('Unemployment (% of total)')
pyplot.show()
# Solution 2 via Linear Regression function
model = LinearRegression()
model.fit(x,y)
r_sq = model.score(x,y)
print('coefficient of determination:',r_sq)
print('intercept:', model.intercept_)
print('slope:', model.coef_)
y_pred = model.predict(x)
print('predicted response:',ypred,sep = '/n')
# csv file writing
countries = [i['id'] for i in wbdata.get_country(incomelevel="HIC", display=False)]
indicators = {"SL.UEM.TOTL.ZS": "unemployment_rate", "NY.GDP.PCAP.PP.CD": "gdppc"}
df = wbdata.get_dataframe(indicators, country=countries, convert_date=True)
df.to_csv('hw2_csv')
df.describe()
def testAllCountries(self):
wbdata.get_country()
wbdata.get_data("IC.BUS.EASE.XQ", country=("USA", "BRA"), data_date=data_date)
# In[ ]:
wbdata.search_indicators("gdp per capita")
# In[ ]:
wbdata.get_incomelevel()
# In[ ]:
countries = [i['id'] for i in wbdata.get_country(incomelevel="OEC", display=False)]
indicators = {"IC.BUS.EASE.XQ": "doing_business", "NY.GDP.PCAP.PP.KD": "gdppc"}
df = wbdata.get_dataframe(indicators, country=countries, convert_date=True)
df.describe()
# In[ ]:
df = df.dropna()
df.gdppc.corr(df.doing_business)
# In[ ]:
def testUSA(self):
wbdata.get_country(country_id="USA")
import pandas as pd
import wbdata
import datetime
import sys
sys.path.append('../other/')
import connect
# Connect to database
con = connect.connector()
# Get countries, indicators
countries = wbdata.get_country(display=False)
indicators = wbdata.get_indicator(display=False)
# Collect CountryID
countryID = []
for i in range(len(countries)):
countryID.append(countries[i]['id'])
# Collect indicatorID
indicatorID = []
for i in range(1000, len(indicators)):
indicatorID.append(indicators[i]['id'])
# Make indDict for fetching data
indDict = dict()
for indStr in indicatorID:
indDict[indStr] = indStr.replace('.','_')
# fetch data
def testOEC(self):
wbdata.get_country(incomelevel="OEC")
import wbdata
import matplotlib.pyplot as plt
import numpy as np
import wbdata
import datetime
pisa2012_file = 'PISA_2012_clean.csv'
df2 = pd.read_csv(pisa2012_file)
pisa2015_file = 'PISA_2015_clean.csv'
df2015 = pd.read_csv(pisa2015_file)
pisa2008_file = 'PISA_2008_clean.csv'
df2008 = pd.read_csv(pisa2008_file)
countries = wbdata.get_country(display=False)
d = dict((x['name'].strip(), x['iso2Code']) for x in countries)
df3 = df2.assign(iso_code=df2.country.map(lambda x: d[x]))
df2015_1 = df2015.assign(iso_code=df2015.country.map(lambda x: d[x]))
df2008_1 = df2008.assign(iso_code=df2008.country.map(lambda x: d[x]))
df1 = pd.read_csv('wb_data.csv', sep='\t')
df_pisa_all = pd.concat([df2008_1, df3, df2015_1])
df_pisa_all.sort(['country', 'iso_code'], inplace=True)
#print(df_pisa_all)
#exit(0)
df4 = pd.merge(df1,
df_pisa_all,
def testIDB(self):
wbdata.get_country(lendingtype="IDB")
