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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.

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 values.

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,

e.g.

'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.

In [23]:
def answer_one():
    import pandas as pd
    import numpy as np
    
    energy = pd.read_excel("Energy Indicators.xls",skip_footer=38,skip_header=1,skiprows=17) # Skip header and footer
    # Skippped head and footer

    energy.drop(energy.columns[[0,1]],axis=1,inplace=True) 
    # Dropped first 2 columns

    energy.columns = ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']
    
    energy['Energy Supply'] *= 1000000 
    # Converted to gigajoules

    energy.dropna() 
    # Drop rows with NaNs

    energy['Country'] = energy['Country'].str.replace(r'\(.*\)', '') 
    # Removee parenthesis
    
    energy['Country'] = energy['Country'].str.replace('\d+', '')
    # Removed numbers from names
      
    energy['Country'] = energy['Country'].str.strip()
    
    # So Iran's energy values back by deleting the spaces left from removal of parenthesis
     
    
    for col in energy:
        energy[col] = energy[col].replace('...',np.nan)
    # Any blank values will convert to NaN

    energy['Country'] = energy['Country'].str.replace('Republic of Korea','South Korea')
    energy['Country'] = energy['Country'].str.replace('United States of America','United States')
    energy['Country'] = energy['Country'].str.replace('United Kingdom of Great Britain and Northern Ireland','United Kingdom')
    energy['Country'] = energy['Country'].str.replace('China, Hong Kong Special Administrative Region','Hong Kong')
    # Updating country names
    
    GDP = pd.read_csv('world_bank.csv', skiprows=3) 
    # Skipped header

    new_header = GDP.iloc[0]
    GDP = GDP[1:]
    GDP.columns = new_header
    # First row is now column names


    GDP['Country Name'] = GDP['Country Name'].str.replace('Korea, Rep.','South Korea')
    GDP['Country Name'] = GDP['Country Name'].str.replace('Iran, Islamic Rep.','Iran')
    GDP['Country Name'] = GDP['Country Name'].str.replace('Hong Kong SAR, China','Hong Kong')
    # Updating country names
    
    
    names = GDP.columns.tolist()
    names[names.index('Country Name')] = 'Country'
    GDP.columns = names
    # Change column from "Country Name" to "Country
    
    GDP = GDP.drop(GDP.iloc[:,1:50], axis=1)
    # Keeping columns from 2006-15 and dropping column number 1 to 50 
    
    GDP.columns = GDP.columns.astype(str).str.split('.').str[0] 
    # Removed '.0' at the end of the year columns    
    
  

    ScimEn = pd.read_excel('scimagojr-3.xlsx')
    
    Newdf = pd.merge(ScimEn,energy,how='outer',left_on='Country',right_on='Country')
    Newdf2 = pd.merge(Newdf,GDP,how='outer',left_on='Country',right_on='Country')

    Newdf2 = Newdf2[:15] 
    # Top 15 countries

    Newdf3 = Newdf2.set_index('Country') 
    # Setting 'Country' for the index column
    
    return Newdf3

answer_one()
Out[23]:
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
Country
China 1.0 127050.0 126767.0 597237.0 411683.0 4.70 138.0 127191000000 93.0 19.754910 3.992331e+12 4.559041e+12 4.997775e+12 5.459247e+12 6.039659e+12 6.612490e+12 7.124978e+12 7.672448e+12 8.230121e+12 8.797999e+12
United States 2.0 96661.0 94747.0 792274.0 265436.0 8.20 230.0 90838000000 286.0 11.570980 1.479230e+13 1.505540e+13 1.501149e+13 1.459484e+13 1.496437e+13 1.520402e+13 1.554216e+13 1.577367e+13 1.615662e+13 1.654857e+13
Japan 3.0 30504.0 30287.0 223024.0 61554.0 7.31 134.0 18984000000 149.0 10.232820 5.496542e+12 5.617036e+12 5.558527e+12 5.251308e+12 5.498718e+12 5.473738e+12 5.569102e+12 5.644659e+12 5.642884e+12 5.669563e+12
United Kingdom 4.0 20944.0 20357.0 206091.0 37874.0 9.84 139.0 7920000000 124.0 10.600470 2.419631e+12 2.482203e+12 2.470614e+12 2.367048e+12 2.403504e+12 2.450911e+12 2.479809e+12 2.533370e+12 2.605643e+12 2.666333e+12
Russian Federation 5.0 18534.0 18301.0 34266.0 12422.0 1.85 57.0 30709000000 214.0 17.288680 1.385793e+12 1.504071e+12 1.583004e+12 1.459199e+12 1.524917e+12 1.589943e+12 1.645876e+12 1.666934e+12 1.678709e+12 1.616149e+12
Canada 6.0 17899.0 17620.0 215003.0 40930.0 12.01 149.0 10431000000 296.0 61.945430 1.564469e+12 1.596740e+12 1.612713e+12 1.565145e+12 1.613406e+12 1.664087e+12 1.693133e+12 1.730688e+12 1.773486e+12 1.792609e+12
Germany 7.0 17027.0 16831.0 140566.0 27426.0 8.26 126.0 13261000000 165.0 17.901530 3.332891e+12 3.441561e+12 3.478809e+12 3.283340e+12 3.417298e+12 3.542371e+12 3.556724e+12 3.567317e+12 3.624386e+12 3.685556e+12
India 8.0 15005.0 14841.0 128763.0 37209.0 8.58 115.0 33195000000 26.0 14.969080 1.265894e+12 1.374865e+12 1.428361e+12 1.549483e+12 1.708459e+12 1.821872e+12 1.924235e+12 2.051982e+12 2.200617e+12 2.367206e+12
France 9.0 13153.0 12973.0 130632.0 28601.0 9.93 114.0 10597000000 166.0 17.020280 2.607840e+12 2.669424e+12 2.674637e+12 2.595967e+12 2.646995e+12 2.702032e+12 2.706968e+12 2.722567e+12 2.729632e+12 2.761185e+12
South Korea 10.0 11983.0 11923.0 114675.0 22595.0 9.57 104.0 11007000000 221.0 2.279353 9.410199e+11 9.924316e+11 1.020510e+12 1.027730e+12 1.094499e+12 1.134796e+12 1.160809e+12 1.194429e+12 1.234340e+12 1.266580e+12
Italy 11.0 10964.0 10794.0 111850.0 26661.0 10.20 106.0 6530000000 109.0 33.667230 2.202170e+12 2.234627e+12 2.211154e+12 2.089938e+12 2.125185e+12 2.137439e+12 2.077184e+12 2.040871e+12 2.033868e+12 2.049316e+12
Spain 12.0 9428.0 9330.0 123336.0 23964.0 13.08 115.0 4923000000 106.0 37.968590 1.414823e+12 1.468146e+12 1.484530e+12 1.431475e+12 1.431673e+12 1.417355e+12 1.380216e+12 1.357139e+12 1.375605e+12 1.419821e+12
Iran 13.0 8896.0 8819.0 57470.0 19125.0 6.46 72.0 9172000000 119.0 5.707721 3.895523e+11 4.250646e+11 4.289909e+11 4.389208e+11 4.677902e+11 4.853309e+11 4.532569e+11 4.445926e+11 4.639027e+11 NaN
Australia 14.0 8831.0 8725.0 90765.0 15606.0 10.28 107.0 5386000000 231.0 11.810810 1.021939e+12 1.060340e+12 1.099644e+12 1.119654e+12 1.142251e+12 1.169431e+12 1.211913e+12 1.241484e+12 1.272520e+12 1.301251e+12
Brazil 15.0 8668.0 8596.0 60702.0 14396.0 7.00 86.0 12149000000 59.0 69.648030 1.845080e+12 1.957118e+12 2.056809e+12 2.054215e+12 2.208872e+12 2.295245e+12 2.339209e+12 2.409740e+12 2.412231e+12 2.319423e+12

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 lose?

This function should return a single number.

In [24]:
%%HTML
<svg width="800" height="300">
  <circle cx="150" cy="180" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="blue" />
  <circle cx="200" cy="100" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="red" />
  <circle cx="100" cy="100" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="green" />
  <line x1="150" y1="125" x2="300" y2="150" stroke="black" stroke-width="2" fill="black" stroke-dasharray="5,3"/>
  <text  x="300" y="165" font-family="Verdana" font-size="35">Everything but this!</text>
</svg>
Everything but this!
In [25]:
def answer_two():
    
    import pandas as pd
    import numpy as np
    
    energy = pd.read_excel("Energy Indicators.xls",skip_footer=38,skip_header=1,skiprows=17) # Skip header and footer

    energy.drop(energy.columns[[0,1]],axis=1,inplace=True) # Drop first 2 columns

    energy.columns = ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']

    energy.dropna() # Drop rows with NaN values.

    energy['Country'] = energy['Country'].str.replace(r'\(.*\)', '') # Remove contents within parenthesis.
    energy['Country'] = energy['Country'].str.replace('\d+', '') # Remove digits from names
    
    energy['Country'] = energy['Country'].str.strip() # This brings the Iran energy values back!
    

    # Turn blank values into NaN
    for col in energy:
        energy[col] = energy[col].replace('...',np.nan)

    energy['Country'] = energy['Country'].str.replace('Republic of Korea','South Korea')
    energy['Country'] = energy['Country'].str.replace('United States of America','United States')
    energy['Country'] = energy['Country'].str.replace('United Kingdom of Great Britain and Northern Ireland','United Kingdom')
    energy['Country'] = energy['Country'].str.replace('China, Hong Kong Special Administrative Region','Hong Kong')


    # GDP:

    GDP = pd.read_csv('world_bank.csv', skiprows=3) # Skip header

    # Make first row the column names
    new_header = GDP.iloc[0]
    GDP = GDP[1:]
    GDP.columns = new_header

    #GDP = GDP.rename(index=str,columns = {"Country Name":"Country"})

    GDP['Country Name'] = GDP['Country Name'].str.replace('Korea, Rep.','South Korea')
    GDP['Country Name'] = GDP['Country Name'].str.replace('Iran, Islamic Rep.','Iran')
    GDP['Country Name'] = GDP['Country Name'].str.replace('Hong Kong SAR, China','Hong Kong')

    # Change column name from 'Country Name' to 'Country' for merging 3 files on country name.
    names = GDP.columns.tolist()
    names[names.index('Country Name')] = 'Country'
    GDP.columns = names

    # Only keep the columns from 2006-15. Drop column number 1 to 50. Don't need country code, etc.
    GDP = GDP.drop(GDP.iloc[:,1:50], axis=1)
    
    GDP.columns = GDP.columns.astype(str).str.split('.').str[0] # Remove '.0' at the end of the year columns.    


    # SCIMEN:
    ScimEn = pd.read_excel('scimagojr-3.xlsx')
    
    # LOST ENTRIES = LEN(OUTER JOIN) - LEN(INNER JOIN)
    
    # Need unique entries in all 3 sets so use concat. Can't do that with a left or right outer join!
    value_outer = len(pd.concat([ScimEn['Country'],energy['Country'],GDP['Country']]).unique())
    
    value_inter = (GDP.merge(energy, left_on='Country', right_on='Country', how='inner').merge(ScimEn, left_on='Country', right_on='Country', how='inner').shape[0])

    return value_outer-value_inter

answer_two()
Out[25]:
156

Answer the following questions in the context of only the top 15 countries by Scimagojr Rank (aka the DataFrame returned by answer_one())

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.

In [26]:
def answer_three():
    import pandas as pd
    Top15 = answer_one()
    Top15 = Top15.iloc[:,10:] 
    # Only include the years for columns
    avgGDP = Top15.mean(axis=1)
    return avgGDP.sort_values(ascending=False)

answer_three()
Out[26]:
Country
United States         1.536434e+13
China                 6.348609e+12
Japan                 5.542208e+12
Germany               3.493025e+12
France                2.681725e+12
United Kingdom        2.487907e+12
Brazil                2.189794e+12
Italy                 2.120175e+12
India                 1.769297e+12
Canada                1.660647e+12
Russian Federation    1.565459e+12
Spain                 1.418078e+12
Australia             1.164043e+12
South Korea           1.106715e+12
Iran                  4.441558e+11
dtype: float64

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.

In [27]:
def answer_four():
    
    import pandas as pd
    Top15 = answer_one()
    answer_four = Top15[Top15['Rank'] == 4]['2015'] - Top15[Top15['Rank'] == 4]['2006']
    return pd.to_numeric(answer_four)[0]

answer_four()


    
Out[27]:
246702696075.3999

Question 5 (6.6%)

What is the mean Energy Supply per Capita?

This function should return a single number.

In [28]:
def answer_five():
    Top15 = answer_one()
    
    return Top15.iloc[:,8].mean()

answer_five()
Out[28]:
157.59999999999999

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.

In [29]:
def answer_six():
    import pandas as pd
    Top15 = answer_one()
    maxRenewable = Top15['% Renewable'].idxmax(), Top15['% Renewable'].max()
    return maxRenewable

answer_six()
Out[29]:
('Brazil', 69.648030000000006)

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.

In [30]:
def answer_seven():
    Top15 = answer_one()
    Top15['Citation ratio'] = Top15['Self-citations'] / Top15['Citations']
    MaxCitationRatio = Top15['Citation ratio'].idxmax(), Top15['Citation ratio'].max()
    return MaxCitationRatio

answer_seven()
Out[30]:
('China', 0.68931261793894216)

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.

In [31]:
def answer_eight():
    Top15 = answer_one()
    Top15['PopEstimate'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita']
    answer_eight = Top15['PopEstimate'].sort_values(ascending=False)
    answer_eight = answer_eight.index.tolist()[2]
    return answer_eight

answer_eight()
Out[31]:
'United States'

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)

In [32]:
def answer_nine():
    Top15 = answer_one()
    Top15['Population Estimate'] = Top15['Energy Supply']/Top15['Energy Supply per Capita']
    Top15['Citable Documents per Capita'] = Top15['Citable documents']/Top15['Population Estimate']
    
   
    Top15['Citable Documents per Capita'] = Top15['Citable Documents per Capita'].astype(float)
    Top15['Energy Supply per Capita'] = Top15['Energy Supply per Capita'].astype(float)
    # Converted to float

    return Top15['Citable Documents per Capita'].corr(Top15['Energy Supply per Capita'])
answer_nine()
Out[32]:
0.79400104354429457
In [33]:
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['PopEst']
    Top15.plot(x='Citable docs per Capita', y='Energy Supply per Capita', kind='scatter', xlim=[0, 0.0006])
In [ ]:
 

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.

In [34]:
def answer_ten():
    import numpy as np
    Top15 = answer_one()
    Top15['HighRenew'] = np.where(Top15['% Renewable'] >= np.median(Top15['% Renewable']),1,0)
    
    return Top15['HighRenew']
answer_ten()
Out[34]:
Country
China                 1
United States         0
Japan                 0
United Kingdom        0
Russian Federation    1
Canada                1
Germany               1
India                 0
France                1
South Korea           0
Italy                 1
Spain                 1
Iran                  0
Australia             0
Brazil                1
Name: HighRenew, dtype: int64

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', 
                  'Japan':'Asia', 
                  'United Kingdom':'Europe', 
                  'Russian Federation':'Europe', 
                  'Canada':'North America', 
                  'Germany':'Europe', 
                  'India':'Asia',
                  'France':'Europe', 
                  'South Korea':'Asia', 
                  'Italy':'Europe', 
                  'Spain':'Europe', 
                  'Iran':'Asia',
                  'Australia':'Australia', 
                  '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', 'std']

In [35]:
def answer_eleven():
    import numpy as np
    
    Top15 = answer_one()
    
    ContinentDict  = {'China':'Asia', 
                  'United States':'North America', 
                  'Japan':'Asia', 
                  'United Kingdom':'Europe', 
                  'Russian Federation':'Europe', 
                  'Canada':'North America', 
                  'Germany':'Europe', 
                  'India':'Asia',
                  'France':'Europe', 
                  'South Korea':'Asia', 
                  'Italy':'Europe', 
                  'Spain':'Europe', 
                  'Iran':'Asia',
                  'Australia':'Australia', 
                  'Brazil':'South America'}
    
    
    Top15['Continent'] = Top15.index.to_series().map(ContinentDict)
    # Dictionary values mapped with the index values.
    
    Top15['Population Estimate'] = (Top15['Energy Supply']/Top15['Energy Supply per Capita']).astype(float)
    # Set to float 
    
    final = Top15.set_index('Continent').groupby(level = 0)['Population Estimate'].agg({'size':np.size, 'sum':np.sum, 'mean':np.mean, 'std':np.std})
    
    return final

answer_eleven()
Out[35]:
size sum mean std
Continent
Asia 5.0 2.898666e+09 5.797333e+08 6.790979e+08
Australia 1.0 2.331602e+07 2.331602e+07 NaN
Europe 6.0 4.579297e+08 7.632161e+07 3.464767e+07
North America 2.0 3.528552e+08 1.764276e+08 1.996696e+08
South America 1.0 2.059153e+08 2.059153e+08 NaN

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.

In [36]:
def answer_twelve():
    import pandas as pd
    Top15 = answer_one()
    
    ContinentDict  = {'China':'Asia', 
                  'United States':'North America', 
                  'Japan':'Asia', 
                  'United Kingdom':'Europe', 
                  'Russian Federation':'Europe', 
                  'Canada':'North America', 
                  'Germany':'Europe', 
                  'India':'Asia',
                  'France':'Europe', 
                  'South Korea':'Asia', 
                  'Italy':'Europe', 
                  'Spain':'Europe', 
                  'Iran':'Asia',
                  'Australia':'Australia', 
                  'Brazil':'South America'}
    
    # Map the dictionary values with the index values.
    Top15['Continent'] = Top15.index.to_series().map(ContinentDict)
    
    Top15['Bins'] = pd.cut(Top15['% Renewable'],5) # The ENTIRE range of % Renewable is split into 5 bins.
    
    return Top15.groupby(['Continent','Bins']).size() # The entire range of bins is then grouped by continent.

answer_twelve()
Out[36]:
Continent      Bins            
Asia           (2.212, 15.753]     4
               (15.753, 29.227]    1
Australia      (2.212, 15.753]     1
Europe         (2.212, 15.753]     1
               (15.753, 29.227]    3
               (29.227, 42.701]    2
North America  (2.212, 15.753]     1
               (56.174, 69.648]    1
South America  (56.174, 69.648]    1
dtype: int64

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.

In [37]:
def answer_thirteen():
    Top15 = answer_one()
    
    Top15['PopEst'] = (Top15['Energy Supply']/Top15['Energy Supply per Capita'])
    
    
    return Top15['PopEst'].apply(lambda x: '{0:,}'.format(x)).astype(str)
    # Adds in comma separators.
answer_thirteen()
Out[37]:
Country
China                 1,367,645,161.2903225
United States          317,615,384.61538464
Japan                  127,409,395.97315437
United Kingdom         63,870,967.741935484
Russian Federation            143,500,000.0
Canada                  35,239,864.86486486
Germany                 80,369,696.96969697
India                 1,276,730,769.2307692
France                  63,837,349.39759036
South Korea            49,805,429.864253394
Italy                  59,908,256.880733944
Spain                    46,443,396.2264151
Iran                    77,075,630.25210084
Australia              23,316,017.316017315
Brazil                 205,915,254.23728815
Name: PopEst, dtype: object

Optional

Use the built in function plot_optional() to see an example visualization.

In [38]:
def plot_optional():
    import matplotlib as plt
    %matplotlib inline
    Top15 = answer_one()
    ax = Top15.plot(x='Rank', y='% Renewable', kind='scatter', 
                    c=['#e41a1c','#377eb8','#e41a1c','#4daf4a','#4daf4a','#377eb8','#4daf4a','#e41a1c',
                       '#4daf4a','#e41a1c','#4daf4a','#4daf4a','#e41a1c','#dede00','#ff7f00'], 
                    xticks=range(1,16), s=6*Top15['2014']/10**10, alpha=.75, figsize=[16,6]);

    for i, txt in enumerate(Top15.index):
        ax.annotate(txt, [Top15['Rank'][i], Top15['% Renewable'][i]], ha='center')

    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.")
    

    
In [39]:
#plot_optional() # Be sure to comment out plot_optional() before submitting the assignment!