Examine frequency distributions
Examine frequency distributions, for variables internetuserate, polityscore urbanrate, incomeperperson.
Code:
#!/usr/bin/env python2.6 # vim: set fileencoding=utf-8 : # -*- coding: utf-8 -*- # # week2.py # Copyright 2015 arpagon # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, # MA 02110-1301, USA. """ Examine frequency distributions, for variables internetuserate, polityscore urbanrate, incomeperperson. On dataset of gapminder """ __version__ = "0.0.1" __license__ = """The GNU General Public License (GPL-2.0)""" __author__ = "Sebastian Rojo [email protected]" __contributors__ = [] _debug = 0 import pandas import numpy data = pandas.read_csv("gapminder.csv", low_memory=False) print('Dataset gapminder \n') print('Obeservations:', len(data)) #number of observations (rows) print('Variables:', len(data.columns)) # number of variables (columns) print('\n\nVariables chosen for the study are: internetuserate, polityscore') print('Additional variables for the assignment: urbanrate, incomeperperson') #internetuserate data['internetuserate'] = data['internetuserate'].convert_objects( convert_numeric=True) print('\n==Counts for internetuserate==') print('Internet users (per 100 people) Internet users are people with access\n' 'to the worldwide network.') internetuserate_count = data['internetuserate'].value_counts(sort=False) print (internetuserate_count) print('\n==Percentages for internetuserate==') print('''Internet users (per 100 people) Internet users are people with access to the worldwide network.''') internetuserate_percent = data['internetuserate'].value_counts(sort=False, normalize=True) print (internetuserate_percent) #polityscore data['polityscore'] = data['polityscore'].convert_objects( convert_numeric=True) print('\n==Counts for polityscore==') print('''Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest.''') polityscore_count = data['polityscore'].value_counts(sort=False) print (polityscore_count) print('\n==Percentages for polityscore==') print('''Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest.''') polityscore_percent = data['polityscore'].value_counts(sort=False, normalize=True) print (polityscore_percent) #urbanrate data['urbanrate'] = data['urbanrate'].convert_objects( convert_numeric=True) print('\n==Counts for urbanrate==') print('''Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)''') urbanrate_count = data['urbanrate'].value_counts(sort=False) print(urbanrate_count) print('\n==Percentages for urbanrate==') print('''Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)''') urbanrate_percent = data['urbanrate'].value_counts(sort=False, normalize=True) print (urbanrate_percent) #incomeperperson data['incomeperperson'] = data['incomeperperson'].convert_objects( convert_numeric=True) print('\n==Counts for incomeperperson==') print('''Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.''') incomeperperson_count = data['incomeperperson'].value_counts(sort=False) print(incomeperperson_count) print('\n==Percentages for incomeperperson==') print('''Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.''') incomeperperson_percent = data['incomeperperson'].value_counts(sort=False, normalize=True) print (incomeperperson_percent)
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Result:
runfile('/home/arpagon/Workspace/DataAnalysisSpecialization/src/week2.py', wdir='/home/arpagon/Workspace/DataAnalysisSpecialization/src') Dataset gapminder Obeservations: 213 Variables: 16 Variables chosen for the study are: internetuserate, polityscore Additional variables for the assignment: urbanrate, incomeperperson ==Counts for internetuserate== Internet users (per 100 people) Internet users are people with access to the worldwide network. 0.720009 1 1.400061 1 2.100213 1 3.654122 1 4.999875 1 5.098265 1 6.497924 1 7.232224 1 8.959140 1 9.999954 1 1.259934 1 11.090765 1 12.645733 1 13.598876 1 14.830736 1 ... 43.055067 1 61.987413 1 7.930096 1 26.477223 1 44.585355 1 2.199998 1 53.740217 1 29.879921 1 44.570074 1 40.020095 1 2.259976 1 6.965038 1 31.568098 1 20.663156 1 28.999477 1 Length: 192, dtype: int64 ==Percentages for internetuserate== Internet users (per 100 people) Internet users are people with access to the worldwide network. 0.720009 0.004695 1.400061 0.004695 2.100213 0.004695 3.654122 0.004695 4.999875 0.004695 5.098265 0.004695 6.497924 0.004695 7.232224 0.004695 8.959140 0.004695 9.999954 0.004695 1.259934 0.004695 11.090765 0.004695 12.645733 0.004695 13.598876 0.004695 14.830736 0.004695 ... 43.055067 0.004695 61.987413 0.004695 7.930096 0.004695 26.477223 0.004695 44.585355 0.004695 2.199998 0.004695 53.740217 0.004695 29.879921 0.004695 44.570074 0.004695 40.020095 0.004695 2.259976 0.004695 6.965038 0.004695 31.568098 0.004695 20.663156 0.004695 28.999477 0.004695 Length: 192, dtype: float64 ==Counts for polityscore== Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. 0 6 1 3 2 3 3 2 4 4 5 7 6 10 7 13 8 19 9 15 10 33 -1 4 -10 2 -9 4 -8 2 -7 12 -6 3 -5 2 -4 6 -3 6 -2 5 dtype: int64 ==Percentages for polityscore== Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. 0 0.028169 1 0.014085 2 0.014085 3 0.009390 4 0.018779 5 0.032864 6 0.046948 7 0.061033 8 0.089202 9 0.070423 10 0.154930 -1 0.018779 -10 0.009390 -9 0.018779 -8 0.009390 -7 0.056338 -6 0.014085 -5 0.009390 -4 0.028169 -3 0.028169 -2 0.023474 dtype: float64 ==Counts for urbanrate== Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) 84.54 1 15.10 1 36.82 1 30.88 1 93.32 1 74.92 1 29.54 1 10.40 1 71.40 1 73.64 1 13.22 1 14.32 1 77.20 1 51.64 1 17.00 1 ... 30.64 1 66.50 1 82.42 1 88.92 1 60.70 1 29.52 1 77.12 1 27.84 2 30.84 1 85.58 1 61.00 1 74.50 1 86.56 1 56.74 1 28.38 1 Length: 194, dtype: int64 ==Percentages for urbanrate== Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) 84.54 0.004695 15.10 0.004695 36.82 0.004695 30.88 0.004695 93.32 0.004695 74.92 0.004695 29.54 0.004695 10.40 0.004695 71.40 0.004695 73.64 0.004695 13.22 0.004695 14.32 0.004695 77.20 0.004695 51.64 0.004695 17.00 0.004695 ... 30.64 0.004695 66.50 0.004695 82.42 0.004695 88.92 0.004695 60.70 0.004695 29.52 0.004695 77.12 0.004695 27.84 0.009390 30.84 0.004695 85.58 0.004695 61.00 0.004695 74.50 0.004695 86.56 0.004695 56.74 0.004695 28.38 0.004695 Length: 194, dtype: float64 ==Counts for incomeperperson== Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account. 2668.020519 1 5634.003948 1 6147.779610 1 772.933345 1 26551.844238 1 1543.956457 1 13577.879885 1 115.305996 1 523.950151 1 33923.313868 1 1860.753895 1 5900.616944 1 20751.893424 1 786.700098 1 275.884287 1 ... 722.807559 1 5188.900935 1 32292.482984 1 495.734247 1 10480.817203 1 5528.363114 1 242.677534 1 2534.000380 1 16372.499781 1 2549.558474 1 760.262365 1 31993.200694 1 22275.751661 1 2557.433638 1 25249.986061 1 Length: 190, dtype: int64 ==Percentages for incomeperperson== Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account. 2668.020519 0.004695 5634.003948 0.004695 6147.779610 0.004695 772.933345 0.004695 26551.844238 0.004695 1543.956457 0.004695 13577.879885 0.004695 115.305996 0.004695 523.950151 0.004695 33923.313868 0.004695 1860.753895 0.004695 5900.616944 0.004695 20751.893424 0.004695 786.700098 0.004695 275.884287 0.004695 ... 722.807559 0.004695 5188.900935 0.004695 32292.482984 0.004695 495.734247 0.004695 10480.817203 0.004695 5528.363114 0.004695 242.677534 0.004695 2534.000380 0.004695 16372.499781 0.004695 2549.558474 0.004695 760.262365 0.004695 31993.200694 0.004695 22275.751661 0.004695 2557.433638 0.004695 25249.986061 0.004695 Length: 190, dtype: float64
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Conclusions:
3 of the variables in the frequency distribution analysis are the type Quantitative.
The Variable polityscore are categorical and thi type is perfect for the frequency distribution.
Is very notorious in the polityscore. in Dataset the most common value is 10 whit the 15%. this indicate the best performance on the democracy
for assignment chose additional variable like urbanrate. I evaluate the possibility of filter the countrys whit more urban then others for the impact of democracy. maybe 50% whot do you think, please comment?
