Self Employment Data 2015

from OECD



In [1]:

    
countries = ['AUS', 'AUT', 'BEL', 'CAN', 'CZE', 'FIN', 'DEU', 'GRC', 'HUN', 'ISL', 'IRL', 'ITA', 'JPN', 
             'KOR', 'MEX', 'NLD', 'NZL', 'NOR', 'POL', 'PRT', 'SVK', 'ESP', 'SWE', 'CHE', 'TUR', 'GBR', 
             'USA', 'CHL', 'COL', 'EST', 'ISR', 'RUS', 'SVN', 'EU28', 'EA19', 'LVA']

male_selfemployment_rates = [12.13246, 15.39631, 18.74896, 9.18314, 20.97991, 18.87097, 
                             13.46109, 39.34802, 13.3356, 16.83681, 25.35344, 29.27118, 
                             12.06516, 27.53898, 31.6945, 19.81751, 17.68489, 9.13669, 
                             24.15699, 22.95656, 19.00245, 21.16428, 13.93171, 8.73181, 
                             30.73483, 19.11255, 7.48383, 25.92752, 52.27145, 12.05042, 
                             15.8517, 8.10048, 19.02411, 19.59021, 19.1384, 14.75558]

female_selfemployment_rates = [8.18631, 10.38607, 11.07756, 8.0069, 12.78461, 
                               9.42761, 7.75637, 29.56566, 8.00408, 7.6802, 8.2774, 18.33204, 
                               9.7313, 23.56431, 32.81488, 13.36444, 11.50045, 4.57464, 
                               17.63891, 13.92678, 10.32846, 12.82925, 6.22453, 9.28793, 
                               38.32216, 10.21743, 5.2896, 25.24502, 49.98448, 6.624, 
                               9.0243, 6.26909, 13.46641, 11.99529, 11.34129, 8.88987]

countries_by_continent = {'AUS':'AUS', 'AUT':'EUR', 'BEL':'EUR', 'CAN':'AM', 
                          'CZE':'EUR', 'FIN':'EUR', 'DEU':'EUR', 'GRC':'EUR', 
                          'HUN':'EUR', 'ISL':'EUR', 'IRL':'EUR', 'ITA':'EUR', 
                          'JPN':'AS',  'KOR':'AS',  'MEX':'AM',  'NLD':'EUR', 
                          'NZL':'AUS', 'NOR':'EUR', 'POL':'EUR', 'PRT':'EUR', 
                          'SVK':'EUR', 'ESP':'EUR', 'SWE':'EUR', 'CHE':'EUR', 
                          'TUR':'EUR', 'GBR':'EUR', 'USA':'AM' , 'CHL':'AM', 
                          'COL':'AM' , 'EST':'EUR', 'ISR':'AS',  'RUS':'EUR', 
                          'SVN':'EUR', 'EU28':'EUR','EA19':'AS', 'LVA':'EUR'}



In [2]:

    
import pandas as pd

df_male_selfemployment_rates = pd.DataFrame({'countries':countries, 'selfemployment_rates':male_selfemployment_rates})
df_male_selfemployment_rates.head()









    Out[2]:







  
    
      
      countries
      selfemployment_rates
    
  
  
    
      0
      AUS
      12.13246
    
    
      1
      AUT
      15.39631
    
    
      2
      BEL
      18.74896
    
    
      3
      CAN
      9.18314
    
    
      4
      CZE
      20.97991



In [3]:

    
df_female_selfemployment_rates = pd.DataFrame({'countries':countries, 'selfemployment_rates':female_selfemployment_rates})
df_female_selfemployment_rates.head()









    Out[3]:







  
    
      
      countries
      selfemployment_rates
    
  
  
    
      0
      AUS
      8.18631
    
    
      1
      AUT
      10.38607
    
    
      2
      BEL
      11.07756
    
    
      3
      CAN
      8.00690
    
    
      4
      CZE
      12.78461



In [4]:

    
df_country_continent = pd.DataFrame(list(countries_by_continent.items()), columns=['country','continent'])
df_country_continent.head()

Solutions with Pandas

Basic Calculations and Statistics

Exercise 1

Calculate for each country the overallselfemployment_rate:
df_selfemployment_rate:=(male_selfemployment_rates+female_selfemployment_rates)/2

(assumes that #women ~#men)



In [5]:

    
# TODO

Exercise 2

Calculate

maximum
minimum
sum
mean
standard deviation

for/of all selfemployment_rates.



In [6]:

    
# TODO

Exercise 3

Find the Country with the highest selfemployment_rate.



In [7]:

    
# TODO

Exercise 4

Find the the sum of all selfemployment_rates, which are between 10-15.



In [8]:

    
# TODO

Exercise 5

a) Plot a barchart of the selfemployment_rates by country (as in [Basic-Plotting]. Use pandas plotting facilities).

Use Pandas reindex to get the labeling in place.



In [9]:

    
# TODO

b) Plot a barchart of the male vs. female selfemployment_rates by country (as in Basic-Plotting, but using pandas plotting facilities).

Use Pandas reindex to get the labeling in place.



In [10]:

    
# TODO

Aggregetions

Exercise 6

Calculate the mean of the selfemployment-rates per continent.



In [11]:

    
# TODO group by Continent