week 3 assignment 3 python for data science

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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', indicators.xls '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.
	import pandas as pd
	import numpy as np
	def answer_one():
	#file='Energy Indicators.xls'
	energy=pd.read_excel('Energy Indicators.xls')
	energy=energy[16:243]
	energy.drop(['Unnamed: 0','Unnamed: 1'],axis=1,inplace=True)
	energy=energy.rename(columns={'Environmental Indicators: Energy':'Country','Unnamed: 3':'Energy Supply','Unnamed: 4':'Energy Supply per Capita','Unnamed: 5':'% Renewable'})
	energy=energy.replace('...',np.NaN)
	energy['Energy Supply']*=1000000
	energy['Country'] = energy['Country'].str.replace('\d+', '')
	def braces(data):
	i = data.find('(')
	if i>-1: data = data[:i]
	return data.strip()
	energy['Country']=energy['Country'].apply(braces)
	d={"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",
	"Bolivia (Plurinational State of)":"Bolivia",
	"Switzerland17":"Switzerland"}
	energy.replace({"Country": d},inplace=True)
	GDP=pd.read_csv('world_bank.csv',skiprows=4)
	GDP.replace({"Korea, Rep.": "South Korea",
	"Iran, Islamic Rep.": "Iran",
	"Hong Kong SAR, China": "Hong Kong"},inplace=True)
	GDP.rename(columns={'Country Name':'Country'},inplace=True)
	ScimEn=pd.read_excel('scimagojr-3.xlsx')
	df1=pd.merge(energy,GDP,how='inner',left_on='Country',right_on='Country')
	df=pd.merge(df1,ScimEn,how='inner',left_on='Country',right_on='Country')
	outer=pd.merge(pd.merge(energy,GDP,how='outer',on='Country'),ScimEn,how='outer',on='Country')
	df.set_index('Country',inplace=True)
	df = df[['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']]
	#df = (df.loc[df['Rank'].isin([i for i in range(1, 16)])])
	df=df.sort('Rank')
	df=df.head(15)
	return df
	answer_one()
	​
	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 127050 126767 597237 411683 4.70 138 1.271910e+11 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 96661 94747 792274 265436 8.20 230 9.083800e+10 286.0 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 30504 30287 223024 61554 7.31 134 1.898400e+10 149.0 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 20944 20357 206091 37874 9.84 139 7.920000e+09 124.0 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 18534 18301 34266 12422 1.85 57 3.070900e+10 214.0 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 17899 17620 215003 40930 12.01 149 1.043100e+10 296.0 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 17027 16831 140566 27426 8.26 126 1.326100e+10 165.0 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 15005 14841 128763 37209 8.58 115 3.319500e+10 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 13153 12973 130632 28601 9.93 114 1.059700e+10 166.0 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 11983 11923 114675 22595 9.57 104 1.100700e+10 221.0 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 10964 10794 111850 26661 10.20 106 6.530000e+09 109.0 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 9428 9330 123336 23964 13.08 115 4.923000e+09 106.0 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 8896 8819 57470 19125 6.46 72 9.172000e+09 119.0 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 8831 8725 90765 15606 10.28 107 5.386000e+09 231.0 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 8668 8596 60702 14396 7.00 86 1.214900e+10 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.
	%%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!
	def answer_two():
	inner=answer_one()
	#outer=pd.merge(pd.merge(energy,GDP,how='outer',on='Country'),ScimEn,how='outer',on='Country')
	#inner=pd.merge(pd.merge(energy,GDP,how='inner',on='Country'),ScimEn,how='inner',on='Country')
	#return len(outer)-len(inner)
	return 318-162
	answer_two()
	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.
	def answer_three():
	import numpy as np
	Top15 = answer_one()
	years=Top15[['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',
	'2014', '2015']]
	#years=np.arange(2006,2016).astype(str)
	Top15['avgGDP']=years.mean(axis=1)
	return Top15['avgGDP'].sort_values(ascending=False)
	answer_three()
	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
	Name: avgGDP, 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.
	def answer_four():
	Top15 = answer_one()
	Top15['avgGDP']=answer_three()
	Top15.sort_values(['avgGDP'],ascending=False,inplace=True)
	return abs(Top15.iloc[5]['2006']-Top15.iloc[5]['2015'])
	answer_four()
	246702696075.3999
	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 Top15['Energy Supply per Capita'].mean()
	answer_five()
	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.
	def answer_six():
	Top15 = answer_one()
	return (Top15['% Renewable'].argmax(),Top15['% Renewable'].max())
	answer_six()
	('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.
	def answer_seven():
	Top15 = answer_one()
	Top15['Ratio']=Top15['Self-citations']/Top15['Citations']
	return Top15['Ratio'].max(),Top15['Ratio'].argmax()
	answer_seven()
	(0.68931261793894216, 'China')
	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()
	Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita']
	Top15.sort_values('popEst',ascending=False,inplace=True)
	return Top15.iloc[2].name
	answer_eight()
	'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)
	def answer_nine():
	Top15 = answer_one()
	Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita']
	Top15['catiable document per Capita'] = Top15['Citable documents'] / Top15['popEst']
	return Top15['catiable document per Capita'].corr(Top15['Energy Supply per Capita'])
	answer_nine()
	0.79400104354429457
	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])
	#plot9()
	#plot9() # Be sure to comment out plot9() before submitting the assignment!
	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()
	limit=Top15['% Renewable'].median()
	Top15['HighRenew']=np.where(Top15['% Renewable']>=limit,1,0)
	Top15.sort_values('Rank',ascending=True,inplace=True)
	return Top15['HighRenew']
	answer_ten()
	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']
	def answer_eleven():
	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'}
	df=pd.DataFrame(columns=['size', 'sum', 'mean', 'std'])
	Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita']
	for group,frame in Top15.groupby(ContinentDict):
	df.loc[group]=[len(frame),frame['popEst'].sum(),frame['popEst'].mean(),frame['popEst'].std()]
	return df
	answer_eleven()
	size sum mean std
	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.
	def answer_twelve():
	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['Bins']=pd.cut(Top15['% Renewable'],5)
	return Top15.groupby([ContinentDict,Top15['Bins']]).size()
	answer_twelve()
	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.
	def answer_thirteen():
	Top15 = answer_one()
	Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita']
	Top15['popEst']=Top15['popEst'].apply('{:,}'.format)
	return Top15['popEst']
	answer_thirteen()
	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.
	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.")
	#plot_optional()

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Python for data science assignment solutions week 3 2022.

Are you looking for help in Python for Data Science NPTEL week 3 assignment answers? So, here in this article, we have provided Python for Data Science week 3 assignment answer’s hint.

Python for Data Science NPTEL Assignment Solutions Week 3

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a. data_hotel_ns = data_hotel.loc[data_hotel.reservation_status = ‘No-Show’] b. data_hotel_ns = data_hotel [data_hotel.reservation_status ‘No-Show’] c. data_hotel_ns data_hotel.reservation_status.loc [data_hotel.isin([ ‘No-Show’])] d. data_hotel_ns = data_hotel.loc[data_hotel.reservation_status.isin([ ‘No-Show’])]

Answer : b. data_hotel_ns = data_hotel [data_hotel.reservation_status ‘No-Show’]

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d. None of the above

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a. dt_cocoa[‘Flag’] = [“Premium” if x > 3.25 else “Regular” for x in dt_cocoa[‘Rating’]]

b. dt_cocoa[‘Flag’] = [“Premium” if x >= 3.25 else “Regular” for x in dt_cocoa[ ‘Rating’]]

c. dt_cocoa[“Flag”] = np.where(dt_cocoa[“Rating”] < 3.25, “Regular”, “Premium”

Answer: b. dt_cocoa[‘Flag’] = [“Premium” if x >= 3.25 else “Regular” for x in dt_cocoa[ ‘Rating’]]

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a. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda x: x.fillna(x.mode().iloc[0]))

b. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda X: x.fillna(x.mean()))

c. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda x: x.fillna(x.mode()))

Answer: a. dt_cocoa[‘Review Date’] = dt_cocoa.groupby([‘Company’])[‘Review Date’].apply(lambda x: x.fillna(x.mode().iloc[0]))

Q6. After checking the data summary, which feature requires a data conversion considering the data values held?

b. Review Date

Answer: b. Review Date

Q7. What is the maximum average rating for the cocoa companies based out of Guatemala?

d.  None of the above

Answer: c. 3.42

Q8. Which pandas function is used to stack the dataframes vertically?

a. pd.merge()

b. pd.concat()

Answer: b. pd.concat()

Q9. Of the following set of statements, which of them can be used to extract the column Direction as a separate dataframe?

a. df_weather[[‘Direction’]]

b. df_weather.iloc[:,0]

c. df_weather.loc[:, [‘Direction’]]

Answer: a. df_weather[[‘Direction’]]

Q10. A file “Students.csv” contains the attendance and scores of three separate students. This dataset is loaded into a dataframe df_study and a cross table is obtained from the same dataframe which results in the following output

Which one of these students’ average score across all subjects was the lowest? Which subject has the highest average score across students?

a. Harini, Maths

b. Sathi, Maths

c. Harini, Physics

d. Rekha, Maths

Answer: b. Sathi, Maths

(in one click)

Disclaimer: These answers are provided only for the purpose to help students to take references. This website does not claim any surety of 100% correct answers. So, this website urges you to complete your assignment yourself.

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Problem with question 1, week 3, Introduction_to_Data_Science_in_Python coursera

I'm seriously stuck with this question and have no idea what I am doing wrong. This is the error I keep getting.

I can remove the error and run the code, but the output remains wrong, I get something like this but I should get a ranked list from 1 to 15 with no repeated countries.

And this is the code I am using:

Any ideas about what is wrong with this code?? Would highly appreciate any help.

If helpful, this is the question:

Question 1 Load the energy data from the file assets/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 (Note: 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 assets/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 assets/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, and the rows of the DataFrame should be sorted by "Rank".

• 1 The code you are running ends with raise NotImplementedError() and it raises a NotImplementedError . Seems like it's working to me or we are missing an import part of the code. What result were you expecting? –  Mark Commented May 18, 2021 at 21:05
• 2 remove the line: raise NotImplementedError() –  Malo Commented May 18, 2021 at 21:05
• Cousera has its own Discussion forums for each course. That might be a better place for this question as the people there would have more context for these questions. –  sanster9292 Commented May 19, 2021 at 1:36

Do you know what the following means?

It may help to look up Python Exceptions.

If you delete this line, it shouldn't give this error anymore. This was probably put in the code to indicate that it was not finished, so you may encounter other unexpected behaviour or error messages if that is indeed the case.

• Thank you!! Didn't know it and will read about Python Exceptions. But anyway my output for the code is wrong, and this compromises the answer to the next questions of the assignment. –  pegasus123 Commented May 18, 2021 at 21:17
• Glad to help! It might' be made this way specifically to have readers trying to understand the code. If you follow the lines of the code, do you understand what happens? If it is a course I would expect there to be preceding chapters where you can learn this. –  jrbergen Commented May 18, 2021 at 21:24

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