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Data Management and Visualization (Assignment-3) Making Data Management Decisions
Python Code:
""" Created on Tue Jun 23 20:10:16 2020
@author: KARTIKEYA """
import pandas import numpy
data= pandas.read_csv('gapminder.csv')
# Converting Variables name to lower-case data.columns= map(str.lower, data.columns)
# Used to avoid run-time errors pandas.set_option('display.float_format', lambda x:'%f'%x)
# Converting variables to be numeric data['co2emissions']= data['co2emissions'].apply(pandas.to_numeric, errors ='coerce') data['urbanrate']= data['urbanrate'].apply(pandas.to_numeric, errors ='coerce') data['relectricperperson']= data['relectricperperson'].apply(pandas.to_numeric, errors ='coerce')
# Mean of Urbanrate of countries print('The mean of Urbanrate of Countries') mean = data['urbanrate'].mean() print(mean)
# Countries with High Urban Rate sub1= data[(data['urbanrate']>58) ]
# Making a copy of Subset sub2= sub1.copy()
#List of Countries with high urbanrate and their CO2 Emission and Electricity used per person print('Contries with High UrbanRate') sub9= sub2[['country','urbanrate','co2emissions','relectricperperson']] print(sub9.head(25))
# Countries with Low Urban Rate sub3= data[(data['urbanrate']<58) ]
# Making a copy of Subset sub4= sub3.copy()
#List of Countries with high urbanrate and their CO2 Emission and Electricity used per person print('Countries with Low UrbanRate') sub3= sub4[['country','urbanrate','co2emissions','relectricperperson']] print(sub3.head(25))
# Median of CO2 Emissions of countries with Low Urbanrate print('The median of CO2 Emission of Countries with Low Urbanrate') median1 = sub4['co2emissions'].median() print(median1)
# Median of CO2 Emissions of countries with High Urbanrate print('The median of CO2 Emission of Countries with High Urbanrate') median2 = sub2['co2emissions'].median() print(median2)
# Median of ElectricityperPerson of Countries with low Urbanrate print('The median of Electricity Used Per Person of Countries with Low Urbanrate') median3 = sub4['relectricperperson'].median() print(median3)
# Median of ElectricityperPerson of Countries with High Urbanrate print('The median of Electricity Used Per Person with High Urbanrate') median4 = sub2['relectricperperson'].median() print(median4)
Output:
The mean of Urbanrate of Countries 56.76935960591131
Contries with High UrbanRate
country urbanrate co2emissions relectricperperson 2 Algeria 65.220000 2932108667.000000 590.509814 3 Andorra 88.920000 nan nan 6 Argentina 92.000000 5872119000.000000 768.428300 7 Armenia 63.860000 51219666.670000 603.763058 9 Australia 88.740000 12970092667.000000 2825.391095 10 Austria 67.160000 4466084333.000000 2068.123309 12 Bahamas 83.700000 137555000.000000 nan 13 Bahrain 88.520000 503994333.300000 7314.355637 16 Belarus 73.460000 999874333.300000 614.907287 17 Belgium 97.360000 10897025333.000000 1920.962215 20 Bermuda 100.000000 20331666.670000 nan 22 Bolivia 65.580000 254939666.700000 213.061614 24 Botswana 59.580000 78943333.330000 454.795705 25 Brazil 85.580000 9580226333.000000 498.165305 26 Brunei 74.820000 254206333.300000 3067.498901 27 Bulgaria 71.100000 3157700333.000000 1380.869289 32 Canada 80.400000 24979045667.000000 4772.370648 33 Cape Verde 59.620000 5214000.000000 nan 34 Cayman Islands 100.000000 8968666.667000 nan 37 Chile 88.440000 1839471333.000000 532.515177 39 Colombia 74.500000 2269806000.000000 404.591365 42 Congo, Rep. 61.340000 46306333.330000 56.372450 44 Costa Rica 63.260000 148470666.700000 798.340600 47 Cuba 75.660000 1286670000.000000 528.787350 48 Cyprus 69.900000 183535000.000000 2123.762863
Countries with Low UrbanRate country urbanrate co2emissions relectricperperson 0 Afghanistan 24.040000 75944000.000000 nan 1 Albania 46.720000 223747333.300000 636.341383 4 Angola 56.700000 248358000.000000 172.999227 5 Antigua and Barbuda 30.460000 16225000.000000 nan 8 Aruba 46.780000 35871000.000000 nan 11 Azerbaijan 51.920000 511107666.700000 921.562111 14 Bangladesh 27.140000 598774000.000000 68.115229 15 Barbados 39.840000 36160666.670000 nan 18 Belize 51.700000 14058000.000000 nan 19 Benin 41.200000 37950000.000000 38.222943 21 Bhutan 34.480000 6024333.333000 nan 23 Bosnia and Herzegovina 47.440000 236419333.300000 927.119497 28 Burkina Faso 19.560000 20628666.670000 nan 29 Burundi 10.400000 8092333.333000 nan 30 Cambodia 21.560000 45411666.670000 51.581320 31 Cameroon 56.760000 125172666.700000 59.551245 35 Central African Rep. 38.580000 8338000.000000 nan 36 Chad 26.680000 7608333.333000 nan 38 China 43.100000 101386000000.000000 331.376632 40 Comoros 28.080000 2368666.667000 nan 41 Congo, Dem. Rep. 33.960000 169180000.000000 30.709244 45 Cote d'Ivoire 48.780000 228748666.700000 70.387444 46 Croatia 57.280000 310024000.000000 1494.410268 55 Egypt 42.720000 3341129000.000000 536.760451 57 Equatorial Guinea 39.380000 26125000.000000 nan
The median of CO2 Emission of Countries with Low Urbanrate 69329333.33
The median of CO2 Emission of Countries with High Urbanrate 1321661000.0
The median of Electricity Used Per Person of Countries with Low Urbanrate 187.3248822
The median of Electricity Used Per Person with High Urbanrate 1142.309009
Summary:
1) Around 102 countries have high urban rate and 101 countries have low urban rate.
2) CO2 emission is more in countries with high urban rate as the median for it is 1321661000.0 which is more than 69329333.33 which is the median of CO2 Emission for countries with low urban rate.
3) Electricity used/person is more in countries with high urban rate as the median for it is 1142.309009 which is more than 187.3248822 which is the median of Electricity used/person for countries with low urban rate.
4) The list of countries that have high urbanrate mostly comprises of developed countries and the list of countries that have low urbanrate comprises of developing countries.
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Text
An Elegant Way To Find Out Median Of Three Numbers In Python
There are multiple ways of doing this.
The first one is the comparison way -
>>> def median3(a,b,c): ... if a<b: ... if c<a: ... return a ... elif b<c: ... return b ... else: ... return c ... else: ... if a<c: ... return a ... elif c<b: ... return b ... else: ... return c ... >>> median3(1,5,2) 2 >>> median3(3,5,2) 3 >>> median3(3,5,7) 5 >>> median3(7,5,2) 5 >>> median3(7,5,1) 5 >>> median3(2,5,1) 2
this takes at least two comparisons and at most three to compute.
Another elegant way to do this might be to sort the three numbers and return the middle one.
Like this:
>>> def median3(a,b,c): ... return sorted([a,b,c])[1] ... >>> median3(1,5,2) 2 >>> median3(3,5,2) 3 >>> median3(3,5,7) 5 >>> median3(7,5,6) 6
#python#median#median3#median of three numbers#median in python#find median of three numbers in python#find median of 3 numbers in python#find median of three numbers#find median#finding median in python#median of numbers in python#median of numbers#elegant way to find median in python#fast way to find out median in python
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Data Management and Visualization (Assignment-2) Running Your First Program
Python Code:
""" Created on Thu Jun 18 16:14:28 2020
@author: KARTIKEYA """
import pandas import numpy
# Loading Data Set mydata = pandas.read_csv('gapminder.csv', low_memory= False)
# Converting Variables name to lower-case mydata.columns= map(str.lower, mydata.columns)
# Used to avoid run-time errors pandas.set_option('display.float_format', lambda x:'%f'%x)
# Converting variables to be numeric mydata['co2emissions']= mydata['co2emissions'].apply(pandas.to_numeric, errors ='coerce') mydata['urbanrate']= mydata['urbanrate'].apply(pandas.to_numeric, errors ='coerce') mydata['relectricperperson']= mydata['relectricperperson'].apply(pandas.to_numeric, errors ='coerce')
# Frequency count for CO2 Emissions print('Frequency Count of CO2 Emission') c1= mydata['co2emissions'].value_counts(sort = False) print(c1)
# Percentage for Co2 Emissions print('Percentage of CO2 Emission') p1= mydata['co2emissions'].value_counts(sort = False, normalize= True) print(p1)
# Frequency count for Urban Rate print('Frequency Count of Urban Rate') c2= mydata['urbanrate'].value_counts(sort = False) print(c2)
# Percentage for Urban Rate print('Percentage of Urban Rate') p2= mydata['urbanrate'].value_counts(sort = False, normalize= True) print(p2)
# Frequency count for Electricity Used Per Person print('Frequency Count of Electricity Used') c3= mydata['relectricperperson'].value_counts(sort = False) print(c3)
# Percentage for Electricity Used Per Person print('Percentage of Electricity Used') p3= mydata['relectricperperson'].value_counts(sort = False, normalize= True) print(p3)
# Countries with High Urban Rate sub1= mydata[(mydata['urbanrate']>70) ]
# Making a copy of Subset sub2= sub1.copy()
# Sub Set Containing Countries whose Urbanrate > 70 c4= sub2['urbanrate'].value_counts(sort = False) print(c4)
# Sub Set Containing CO2 Emissions of countries with high Urbanrate c5= sub2['co2emissions'].value_counts(sort = False) print(c5)
# Countries with Low Urban Rate sub3= mydata[(mydata['urbanrate']<70) ]
# Making a copy of Subset sub4= sub3.copy()
# Sub Set Containing Countries whose Urbanrate < 70 c6= sub4['urbanrate'].value_counts(sort = False) print(c6)
# Sub Set Containing CO2 Emissions of countries with low Urbanrate c7= sub4['co2emissions'].value_counts(sort = False) print(c7)
# Median of CO2 Emissions of countries with Low Urbanrate print('The median of CO2 Emission of Countries with Low Urbanrate') median1 = sub4['co2emissions'].median() print(median1)
# Median of CO2 Emissions of countries with High Urbanrate print('The median of CO2 Emission of Countries with High Urbanrate') median2 = sub2['co2emissions'].median() print(median2)
# Sub Set Containing ElectricityperPerson of countries with high Urbanrate c8= sub2['relectricperperson'].value_counts(sort = False) print(c8)
# Sub Set Containing ElectricityperPerson of Countries with low Urbanrate c9= sub4['relectricperperson'].value_counts(sort = False) print(c9)
# Median of ElectricityperPerson of Countries with low Urbanrate print('The median of CO2 Emission of Countries with Low Urbanrate') median3 = sub4['relectricperperson'].median() print(median3)
# Median of ElectricityperPerson of Countries with High Urbanrate print('The median of CO2 Emission of Countries with High Urbanrate') median4 = sub2['relectricperperson'].median() print(median4)
Output:
Frequency Count of CO2 Emission: 5214000.000000 1 4286590000.000000 1 2329308667.000000 1 20331666.670000 1 1045000.000000 1 .. 21332666.670000 1 2977333.333000 1 226255333.300000 1 322960000.000000 1 35717000.000000 1 Name: co2emissions, Length: 200, dtype: int64
Percentage of CO2 Emission: 5214000.000000 0.005000 4286590000.000000 0.005000 2329308667.000000 0.005000 20331666.670000 0.005000 1045000.000000 0.005000
21332666.670000 0.005000 2977333.333000 0.005000 226255333.300000 0.005000 322960000.000000 0.005000 35717000.000000 0.005000 Name: co2emissions, Length: 200, dtype: float64
Frequency Count of Urban Rate: 92.000000 1 100.000000 6 74.500000 1 73.500000 1 17.000000 1 .. 56.020000 1 57.180000 1 73.920000 1 25.460000 1 28.380000 1 Name: urbanrate, Length: 194, dtype: int64
Percentage of Urban Rate: 92.000000 0.004926 100.000000 0.029557 74.500000 0.004926 73.500000 0.004926 17.000000 0.004926
56.020000 0.004926 57.180000 0.004926 73.920000 0.004926 25.460000 0.004926 28.380000 0.004926 Name: urbanrate, Length: 194, dtype: float64
Frequency Count of Electricity Used: 0.000000 5 1585.174739 1 614.907287 1 904.669445 1 66.238522 1 .. 2826.044873 1 758.858719 1 532.515177 1 297.883200 1 38.222943 1 Name: relectricperperson, Length: 132, dtype: int64
Percentage of Electricity Used: 0.000000 0.036765 1585.174739 0.007353 614.907287 0.007353 904.669445 0.007353 66.238522 0.007353
2826.044873 0.007353 758.858719 0.007353 532.515177 0.007353 297.883200 0.007353 38.222943 0.007353 Name: relectricperperson, Length: 132, dtype: float64
Frequency Count UrbanRate >70: 92.000000 1 100.000000 6 74.500000 1 73.500000 1 77.540000 1 73.460000 1 70.360000 1 85.580000 1 88.920000 1 71.900000 1 77.880000 1 75.660000 1 72.840000 1 89.940000 1 83.520000 1 71.620000 1 94.260000 1 88.740000 1 86.680000 1 94.220000 1 81.820000 1 87.300000 1 83.700000 1 92.680000 1 92.300000 1 81.460000 1 82.440000 1 85.040000 1 73.480000 1 73.200000 1 80.460000 1 88.520000 1 98.320000 1 92.260000 1 88.440000 1 77.120000 1 86.560000 1 97.360000 1 73.640000 1 71.100000 1 71.400000 1 77.360000 1 93.160000 1 93.320000 1 77.200000 1 74.920000 1 77.480000 1 95.640000 1 73.920000 1 86.960000 1 81.700000 1 98.360000 1 78.420000 1 80.400000 1 74.820000 1 91.660000 1 84.540000 1 71.080000 1 82.420000 1 Name: urbanrate, dtype: int64
Frequency Count for CO2 Emission of Countries with UrbanRate>70: 4286590000.000000 1 1839471333.000000 1 1561079667.000000 1 20331666.670000 1 13304503667.000000 1 5872119000.000000 1 1723333.333000 1 10897025333.000000 1 3503877667.000000 1 1962704333.000000 1 14054333.330000 1 14241333.330000 1 487993000.000000 1 9580226333.000000 1 23404568000.000000 1 38397333.330000 1 275744333.300000 1 428006333.300000 1 33341634333.000000 1 334221000000.000000 1 102538333.300000 1 92770333.330000 1 2269806000.000000 1 1548044667.000000 1 377303666.700000 1 5418886000.000000 1 7861553333.000000 1 86317000.000000 1 254206333.300000 1 2670950333.000000 1 23635333.330000 1 24979045667.000000 1 73784333.330000 1 2406741333.000000 1 592012666.700000 1 9666891667.000000 1 10822529667.000000 1 170404666.700000 1 12970092667.000000 1 999874333.300000 1 1712755000.000000 1 1414031667.000000 1 1776016000.000000 1 1146277000.000000 1 1321661000.000000 1 72524250333.000000 1 1286670000.000000 1 811965000.000000 1 2401666.667000 1 3157700333.000000 1 2315698000.000000 1 137555000.000000 1 9483023000.000000 1 503994333.300000 1 170804333.300000 1 41229554667.000000 1 1206333.333000 1 1026813333.000000 1 8968666.667000 1 Name: co2emissions, dtype: int64
Frequency Count UrbanRate <70: 17.000000 1 61.000000 1 67.500000 1 25.460000 1 41.000000 1 .. 66.900000 1 26.460000 1 51.460000 1 17.960000 1 41.760000 1 Name: urbanrate, Length: 135, dtype: int64
Frequency Count for CO2 Emission of Countries with UrbanRate<70: 17515666.670000 1 49793333.330000 1 104170000.000000 1 5584766000.000000 1 19000454000.000000 1 .. 226255333.300000 1 59473333.330000 1 322960000.000000 1 35717000.000000 1 214368000.000000 1 Name: co2emissions, Length: 133, dtype: int64
The median of CO2 Emission of Countries with Low Urbanrate: 125172666.7
The median of CO2 Emission of Countries with High Urbanrate: 1414031667.0
Electricity per Person in Countries with Urbanrate>70:
0.000000 1 4304.514402 1 614.907287 1 1464.837281 1 7432.130852 1 2361.033336 1 431.625379 1 846.014923 1 2124.608816 1 1920.962215 1 3433.932449 1 532.515177 1 1884.299342 1 1468.640784 1 1438.780412 1 8362.567977 1 1779.503510 1 825.941111 1 1933.945615 1 913.845660 1 2993.092660 1 3067.498901 1 4542.848695 1 767.970324 1 1490.056909 1 2342.435248 1 239.389457 1 1566.106139 1 2825.391095 1 2826.044873 1 823.823197 1 1380.869289 1 498.165305 1 7314.355637 1 768.428300 1 4759.453844 1 1142.309009 1 1693.891898 1 537.104738 1 2539.753273 1 969.004339 1 1598.673959 1 528.787350 1 500.288505 1 4772.370648 1 404.591365 1 719.476530 1 11154.755030 1 Name: relectricperperson, dtype: int64
Electricity per Person in Countries with Urbanrate<70
17515666.670000 1 49793333.330000 1 104170000.000000 1 5584766000.000000 1 19000454000.000000 1 .. 226255333.300000 1 59473333.330000 1 322960000.000000 1 35717000.000000 1 214368000.000000 1 Name: co2emissions, Length: 133, dtype: int64
The median of CO2 Emission of Countries with Low Urbanrate 320.33288025
The median of CO2 Emission of Countries with High Urbanrate 1528.081524
Summary:
1) As the median of CO2 Emission in countries with low urbanrate is lesser than that of countries with high urbanrate. So, it can be concluded that CO2 Emission is high in countries whose urbanrate is high.
2) As the median of Electricity/person in countries with low urbanrate is lesser than that of countries with high urbanrate. So, it can be concluded that Electricity/person is high in countries whose urbanrate is high.
Thus High Urban Rate without sustainability is deteriorating the environment.
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