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August 11, 2020
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1 Assignment 3 - More Pandas
This assignment requires more individual learning then the last one did - you are encouraged to
check out the pandas documentation to find functions or methods you might not have used yet,
or ask questions on Stack Overflow and tag them as pandas and python related. And of course,
the discussion forums are open for interaction with your peers and the course staff.
1.0.1 Question 1 (20%)
Load the energy data from the file Energy Indicators.xls, which is a list of indicators of energy
supply and renewable electricity production from the United Nations for the year 2013, and
should be put into a DataFrame with the variable name of energy.
Keep in mind that this is an Excel file, and not a comma separated values file. Also, make
sure to exclude the footer and header information from the datafile. The first two columns are
unneccessary, so you should get rid of them, and you should change the column labels so that the
columns are:
['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']
Convert Energy Supply to gigajoules (there are 1,000,000 gigajoules in a petajoule). For all
countries which have missing data (e.g. data with “. . . ”) make sure this is reflected as np.NaN
Rename the following list of countries (for use in later questions):
"Republic of Korea": "South Korea", "United States of America": "United
States", "United Kingdom of Great Britain and Northern Ireland": "United
Kingdom", "China, Hong Kong Special Administrative Region": "Hong Kong"
There are also several countries with numbers and/or parenthesis in their name. Be sure to
remove these,
'Bolivia (Plurinational State of)' should be 'Bolivia',
'Switzerland17' should be 'Switzerland'.
Next, load the GDP data from the file world_bank.csv, which is a csv containing countries’
GDP from 1960 to 2015 from World Bank. Call this DataFrame GDP.
Make sure to skip the header, and rename the following list of countries:
"Korea, Rep.": "South Korea", "Iran, Islamic Rep.": "Iran", "Hong Kong SAR,
China": "Hong Kong"
Finally, load the Sciamgo Journal and Country Rank data for Energy Engineering and Power
Technology from the file scimagojr-3.xlsx, which ranks countries based on their journal contributions
in the aforementioned area. Call this DataFrame ScimEn.
Join the three datasets: GDP, Energy, and ScimEn into a new dataset (using the intersection of
country names). Use only the last 10 years (2006-2015) of GDP data and only the top 15 countries
by Scimagojr ‘Rank’ (Rank 1 through 15).
The index of this DataFrame should be the name of the country, and the columns should be
[‘Rank’, ‘Documents’, ‘Citable documents’, ‘Citations’, ‘Self-citations’, ‘Citations per document’,
‘H index’, ‘Energy Supply’, ‘Energy Supply per Capita’, ‘% Renewable’, ‘2006’, ‘2007’, ‘2008’,
‘2009’, ‘2010’, ‘2011’, ‘2012’, ‘2013’, ‘2014’, ‘2015’].
This function should return a DataFrame with 20 columns and 15 entries.
[1]: def answer_one():
1.0.2 Question 2 (6.6%)
The previous question joined three datasets then reduced this to just the top 15 entries. When you
joined the datasets, but before you reduced this to the top 15 items, how many entries did you
This function should return a single number.
[2]: %%HTML

,→stroke-width="2" fill="blue" />
,→stroke-width="2" fill="red" />
,→stroke-width="2" fill="green" />
,→fill="black" stroke-dasharray="5,3"/>
Everything but␣

[3]: def answer_two():
1.1 Answer the following questions in the context of only the top 15 countries by
Scimagojr Rank (aka the DataFrame returned by answer_one())
1.1.1 Question 3 (6.6%)
What is the average GDP over the last 10 years for each country? (exclude missing values from
this calculation.)
This function should return a Series named avgGDP with 15 countries and their average GDP sorted in
descending order.
[4]: def answer_three():
Top15 = answer_one()
return "ANSWER"
1.1.2 Question 4 (6.6%)
By how much had the GDP changed over the 10 year span for the country with the 6th largest
average GDP?
This function should return a single number.
[5]: def answer_four():
Top15 = answer_one()
return "ANSWER"
1.1.3 Question 5 (6.6%)
What is the mean Energy Supply per Capita?
This function should return a single number.
[ ]: def answer_five():
Top15 = answer_one()
return "ANSWER"
1.1.4 Question 6 (6.6%)
What country has the maximum % Renewable and what is the percentage?
This function should return a tuple with the name of the country and the percentage.
[ ]: def answer_six():
Top15 = answer_one()
return "ANSWER"
1.1.5 Question 7 (6.6%)
Create a new column that is the ratio of Self-Citations to Total Citations. What is the maximum
value for this new column, and what country has the highest ratio?
This function should return a tuple with the name of the country and the ratio.
[ ]: def answer_seven():
Top15 = answer_one()
return "ANSWER"
1.1.6 Question 8 (6.6%)
Create a column that estimates the population using Energy Supply and Energy Supply per capita.
What is the third most populous country according to this estimate?
This function should return a single string value.
[ ]: def answer_eight():
Top15 = answer_one()
return "ANSWER"
1.1.7 Question 9 (6.6%)
Create a column that estimates the number of citable documents per person. What is the correlation
between the number of citable documents per capita and the energy supply per capita? Use
the .corr() method, (Pearson’s correlation).
This function should return a single number.
(Optional: Use the built-in function plot9() to visualize the relationship between Energy Supply per
Capita vs. Citable docs per Capita)
[ ]: def answer_nine():
Top15 = answer_one()
return "ANSWER"
[ ]: def plot9():
import matplotlib as plt
%matplotlib inline
Top15 = answer_one()
Top15['PopEst'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita']
Top15['Citable docs per Capita'] = Top15['Citable documents'] /␣
Top15.plot(x='Citable docs per Capita', y='Energy Supply per Capita',␣
,→kind='scatter', xlim=[0, 0.0006])
[ ]: #plot9() # Be sure to comment out plot9() before submitting the assignment!
1.1.8 Question 10 (6.6%)
Create a new column with a 1 if the country’s % Renewable value is at or above the median for all
countries in the top 15, and a 0 if the country’s % Renewable value is below the median.
This function should return a series named HighRenew whose index is the country name sorted in
ascending order of rank.
[ ]: def answer_ten():
Top15 = answer_one()
return "ANSWER"
1.1.9 Question 11 (6.6%)
Use the following dictionary to group the Countries by Continent, then create a dateframe that
displays the sample size (the number of countries in each continent bin), and the sum, mean, and
std deviation for the estimated population of each country.
ContinentDict = {'China':'Asia',
'United States':'North America',
'United Kingdom':'Europe',
'Russian Federation':'Europe',
'Canada':'North America',
'South Korea':'Asia',
'Brazil':'South America'}
This function should return a DataFrame with index named Continent ['Asia', 'Australia',
'Europe', 'North America', 'South America'] and columns ['size', 'sum', 'mean',
[ ]: def answer_eleven():
Top15 = answer_one()
return "ANSWER"
1.1.10 Question 12 (6.6%)
Cut % Renewable into 5 bins. Group Top15 by the Continent, as well as these new % Renewable
bins. How many countries are in each of these groups?
This function should return a Series with a MultiIndex of Continent, then the bins for % Renewable.
Do not include groups with no countries.
[ ]: def answer_twelve():
Top15 = answer_one()
return "ANSWER"
1.1.11 Question 13 (6.6%)
Convert the Population Estimate series to a string with thousands separator (using commas). Do
not round the results.
e.g. 317615384.61538464 -> 317,615,384.61538464
This function should return a Series PopEst whose index is the country name and whose values are the
population estimate string.
[ ]: def answer_thirteen():
Top15 = answer_one()
return "ANSWER"
1.1.12 Optional
Use the built in function plot_optional() to see an example visualization.
[ ]: def plot_optional():
import matplotlib as plt
%matplotlib inline
Top15 = answer_one()
ax = Top15.plot(x='Rank', y='% Renewable', kind='scatter',


xticks=range(1,16), s=6*Top15['2014']/10**10, alpha=.75,␣
for i, txt in enumerate(Top15.index):
ax.annotate(txt, [Top15['Rank'][i], Top15['% Renewable'][i]],␣
print("This is an example of a visualization that can be created to help␣
,→understand the data. \
This is a bubble chart showing % Renewable vs. Rank. The size of the bubble␣
,→corresponds to the countries' \
2014 GDP, and the color corresponds to the continent.")
[ ]: #plot_optional() # Be sure to comment out plot_optional() before submitting the␣

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