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Create load_df_to_memory() and change in two function #3

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322324f
Create load_df_to_memory() and change in two function
sambhipiyush May 9, 2020
03e913d
Updated answer_two function
sambhipiyush May 10, 2020
fd43e39
Improved answer_three function
sambhipiyush May 10, 2020
cebe25a
Modification in answer_seven and answer_eight functions
sambhipiyush May 10, 2020
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88 changes: 56 additions & 32 deletions Assignment+3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,7 @@
# *This function should return a DataFrame with 20 columns and 15 entries.*

# In[1]:

def answer_one():
def load_df_to_memory():
import pandas as pd
import numpy as np
energy = pd.read_excel('Energy Indicators.xls', skiprows=17, skip_footer= 38)
Expand All @@ -80,7 +79,10 @@ def answer_one():
df = pd.merge(ScimEn, energy, how='inner',left_on='Country', right_on='Country')
alldf = pd.merge(df,GDP, how='inner', left_on='Country', right_on='Country')
alldf = alldf.set_index('Country')
return alldf[:15]
return alldf

def answer_one():
return load_df_to_memory()[:15]

answer_one()

Expand All @@ -100,29 +102,55 @@ def answer_one():
# In[3]:

def answer_two():
import pandas as pd
import numpy as np
energy = pd.read_excel('Energy Indicators.xls', skiprows=17, skip_footer= 38)
energy = energy[['Unnamed: 1', 'Petajoules', 'Gigajoules', '%']]
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['Energy Supply'] = energy['Energy Supply'] * 1000000
energy[['Energy Supply', 'Energy Supply per Capita', '% Renewable']]= energy[['Energy Supply', 'Energy Supply per Capita', '% Renewable']].replace('...', np.NaN)
energy['Country'] = energy['Country'].replace({'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', 'Iran (Islamic Republic of)':'Iran'})
energy['Country'].str.replace(r" \(.*\)","")

GDP = pd.read_csv('world_bank.csv', skiprows = 4)
GDP['Country Name'] = GDP['Country Name'].replace({'Korea, Rep.': 'South Korea', 'Iran, Islamic Rep.': 'Iran', 'Hong Kong SAR, China' : 'Hong Kong'})
GDP = GDP[['Country Name', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015']]
GDP.columns = ['Country', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015']

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')

df = pd.merge(ScimEn, energy, how='inner',left_on='Country', right_on='Country')
alldf = pd.merge(df,GDP, how='inner', left_on='Country', right_on='Country')
alldf = alldf.set_index('Country')
answer_one = alldf[:15]
answer_two = alldf.shape[0] - answer_one.shape[0]
return answer_two
# 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!
num_outer = len(pd.concat([ScimEn['Country'],energy['Country'],GDP['Country']]).unique())
num_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 num_outer-num_inter

answer_two()

Expand All @@ -140,8 +168,7 @@ def answer_two():

def answer_three():
Top15 = answer_one()
aveGDP = Top15[['2006','2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015']].mean(axis=1).rename('aveGDP').sort_values(ascending=False)
return aveGDP
return Top15.iloc[:, 10:].mean(axis=1).rename('avgGDP').sort_values(ascending=False)

answer_three()

Expand Down Expand Up @@ -204,9 +231,8 @@ def answer_six():

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
Top15['selfcitation_to_total'] = Top15['Self-citations']/Top15['Citations']
return Top15['selfcitation_to_total'].idxmax(), Top15['selfcitation_to_total'].max()

answer_seven()

Expand All @@ -222,10 +248,8 @@ def answer_seven():

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
Top15['estd_population'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita']
return Top15.sort_values(by=['estd_population'], ascending=False).index[2]

answer_eight()

Expand Down