def answer():
top15 = q1.answer()
top15['pop'] = top15['Energy Supply'] / top15['Energy Supply per Capita']
top15['bucket'] = pd.cut(top15['% Renewable'], 5)
continents = q11.continentDict()
count = top15[['pop', 'bucket']] \
.groupby([lambda country: continents[country], 'bucket']) \
.count() \
.dropna()
count.index.rename(['Continent', 'bins'], inplace=True)
count.columns = ['count']
return pd.Series(count['count'], index=count.index)
def answer():
top15 = q1.answer()
top15['pop'] = top15['Energy Supply'] / top15['Energy Supply per Capita']
print(top15.loc['China', 'pop'])
PopEst = top15['pop'].map('{:,}'.format)
return PopEst
def answer():
top15 = q1.answer()
largest = top15['% Renewable'].nlargest(1)
return (largest.index[0], largest.values[0])
def answer():
top15 = q1.answer()
top15['pop'] = top15['Energy Supply'] / top15['Energy Supply per Capita']
top15['docs per capita'] = top15['Citable documents'] / top15['pop']
sigma = top15[['Energy Supply per Capita', 'docs per capita']].corr()
return sigma.iloc[0, 1]
def answer():
top15 = q1.answer()
med = top15['% Renewable'].median()
top15.loc[top15['% Renewable'] >= med, 'HighRenew'] = 1
top15.loc[top15['% Renewable'] < med, 'HighRenew'] = 0
return top15.sort_values('Rank')['HighRenew']
def answer():
avgGDP = q1.answer()
columns = getGdpColumns()
avgGDP = avgGDP[columns].mean(1).sort_values(ascending=False)
return avgGDP
def answer():
top15 = q1.answer()
top15['pop'] = top15['Energy Supply'] / top15['Energy Supply per Capita']
largest = top15['pop'].nlargest(3)
return largest.index[2]
def answer():
top15 = q1.answer()
country = q3.answer().index[5]
gdp2006 = top15.loc[country, '2006']
gdp2015 = top15.loc[country, '2015']
return gdp2015 - gdp2006
def answer():
top15 = q1.answer()
return top15['Energy Supply per Capita'].mean()