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Week 6: Mini-Project

(2017-08-07 22:28)

The dataset I'll use is the World Development Indicators dataset, available at Kaggle. My research question is how the fertility rate and the female labor force participation are correlated. I am interested in how Brazil is performing over time and compared to its neighbours in South America.

Based on: https://www.kaggle.com/ljvmiranda/philippines-energy-use/notebook

1. Organizing the data

We imported the Indicators.csv file using the Pandas's function pandas.read_csv(). Then, we sliced the database to retrieve only the Fertility rate, total (births per woman) and the Labor force participation rate, female (% of female population ages 15+) (national estimate) indicators. Also, we selected only the countries in South America.





In [1]:



    


import pandas as pd

indicators = pd.read_csv('./Indicators.csv.zip')



    











In [2]:



    


indicators.head()



    


















    
Out[2]:










  
    

      
      CountryName
      CountryCode
      IndicatorName
      IndicatorCode
      Year
      Value
    

  
  
    

      0
      Arab World
      ARB
      Adolescent fertility rate (births per 1,000 wo...
      SP.ADO.TFRT
      1960
      1.335609e+02
    

    

      1
      Arab World
      ARB
      Age dependency ratio (% of working-age populat...
      SP.POP.DPND
      1960
      8.779760e+01
    

    

      2
      Arab World
      ARB
      Age dependency ratio, old (% of working-age po...
      SP.POP.DPND.OL
      1960
      6.634579e+00
    

    

      3
      Arab World
      ARB
      Age dependency ratio, young (% of working-age ...
      SP.POP.DPND.YG
      1960
      8.102333e+01
    

    

      4
      Arab World
      ARB
      Arms exports (SIPRI trend indicator values)
      MS.MIL.XPRT.KD
      1960
      3.000000e+06
    

  




















In [3]:



    


indicators[indicators['IndicatorName'].str.contains('Fertility')]['IndicatorName'].unique()



    


















    
Out[3]:








array(['Fertility rate, total (births per woman)'], dtype=object)

















In [4]:



    


indicators[indicators['IndicatorName'].str.contains('Labor')]['IndicatorName'].unique()

# The indicator used is: 'Labor force participation rate, female (% of female population ages 15+) (national estimate)'



    


















    
Out[4]:








array([ 'Labor force participation rate for ages 15-24, female (%) (national estimate)',
       'Labor force participation rate for ages 15-24, male (%) (national estimate)',
       'Labor force participation rate for ages 15-24, total (%) (national estimate)',
       'Labor force participation rate, female (% of female population ages 15+) (national estimate)',
       'Labor force participation rate, male (% of male population ages 15+) (national estimate)',
       'Labor force participation rate, total (% of total population ages 15+) (national estimate)',
       'Labor force with primary education (% of total)',
       'Labor force with primary education, female (% of female labor force)',
       'Labor force with primary education, male (% of male labor force)',
       'Labor force with secondary education (% of total)',
       'Labor force with secondary education, female (% of female labor force)',
       'Labor force with secondary education, male (% of male labor force)',
       'Labor force with tertiary education (% of total)',
       'Labor force with tertiary education, female (% of female labor force)',
       'Labor force with tertiary education, male (% of male labor force)',
       'Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)',
       'Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)',
       'Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)',
       'Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)',
       'Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)',
       'Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)',
       'Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)',
       'Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)',
       'Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)',
       'Labor force, female (% of total labor force)',
       'Labor force, total',
       'Benefits incidence in poorest quintile (%) - All Labor Market',
       'Benefits incidence in poorest quintile (%) -All Social Protection and Labor',
       'Coverage (%) - All Labor Market',
       'Coverage (%) -All Social Protection and Labor',
       'Labor tax and contributions (% of commercial profits)'], dtype=object)

















In [5]:



    


# slices:

fertility = ['Fertility rate, total (births per woman)', 
             'Labor force participation rate, female (% of female population ages 15+) (national estimate)']

labor = 'Labor force participation rate, female (% of female population ages 15+) (national estimate)'

countries = ['Argentina', 'Brazil', 'Paraguay', 'Uruguay', 'Venezuela, RB'] # Full-members

# countries = ['Argentina', 'Bolivia', 'Brazil', 'Chile', 
#              'Colombia', 'Ecuador', 'Paraguay', 'Peru', 
#              'Suriname', 'Uruguay', 'Venezuela, RB'] # Full-members + associate countries

# countries = ['Argentina', 'Bolivia', 'Brazil', 'Chile', 
#              'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 
#              'Peru', 'Suriname', 'Uruguay', 'Venezuela, RB'] # South America

ind = indicators[indicators['IndicatorName'].isin(fertility) & indicators['CountryName'].isin(countries)]
ind.head()



    


















    
Out[5]:










  
    

      
      CountryName
      CountryCode
      IndicatorName
      IndicatorCode
      Year
      Value
    

  
  
    

      3993
      Argentina
      ARG
      Fertility rate, total (births per woman)
      SP.DYN.TFRT.IN
      1960
      3.109
    

    

      5662
      Brazil
      BRA
      Fertility rate, total (births per woman)
      SP.DYN.TFRT.IN
      1960
      6.210
    

    

      17373
      Paraguay
      PRY
      Fertility rate, total (births per woman)
      SP.DYN.TFRT.IN
      1960
      6.500
    

    

      22419
      Uruguay
      URY
      Fertility rate, total (births per woman)
      SP.DYN.TFRT.IN
      1960
      2.880
    

    

      22665
      Venezuela, RB
      VEN
      Fertility rate, total (births per woman)
      SP.DYN.TFRT.IN
      1960
      6.616

2. Trend over time

The charts below show the fertility and the female labor force participation rates and the comparison of the country's performance with its neighbor South America nations.





In [6]:



    


%matplotlib inline
import matplotlib.pyplot as plt



    











In [7]:



    


arg_fert = ind.loc[(ind['CountryCode'] == 'ARG') & (ind['IndicatorCode'] == 'SP.DYN.TFRT.IN'), ['Value', 'Year']]
bra_fert = ind.loc[(ind['CountryCode'] == 'BRA') & (ind['IndicatorCode'] == 'SP.DYN.TFRT.IN'), ['Value', 'Year']]
pry_fert = ind.loc[(ind['CountryCode'] == 'PRY') & (ind['IndicatorCode'] == 'SP.DYN.TFRT.IN'), ['Value', 'Year']]
ury_fert = ind.loc[(ind['CountryCode'] == 'URY') & (ind['IndicatorCode'] == 'SP.DYN.TFRT.IN'), ['Value', 'Year']]
ven_fert = ind.loc[(ind['CountryCode'] == 'VEN') & (ind['IndicatorCode'] == 'SP.DYN.TFRT.IN'), ['Value', 'Year']]

arg_labor = ind.loc[(ind['CountryCode'] == 'ARG') & (ind['IndicatorCode'] == 'SL.TLF.CACT.FE.NE.ZS'), ['Value', 'Year']]
bra_labor = ind.loc[(ind['CountryCode'] == 'BRA') & (ind['IndicatorCode'] == 'SL.TLF.CACT.FE.NE.ZS'), ['Value', 'Year']]
pry_labor = ind.loc[(ind['CountryCode'] == 'PRY') & (ind['IndicatorCode'] == 'SL.TLF.CACT.FE.NE.ZS'), ['Value', 'Year']]
ury_labor = ind.loc[(ind['CountryCode'] == 'URY') & (ind['IndicatorCode'] == 'SL.TLF.CACT.FE.NE.ZS'), ['Value', 'Year']]
ven_labor = ind.loc[(ind['CountryCode'] == 'VEN') & (ind['IndicatorCode'] == 'SL.TLF.CACT.FE.NE.ZS'), ['Value', 'Year']]

year = ind['Year'].unique()



    











In [8]:



    


plt.style.use('ggplot')

fig, ax1 = plt.subplots(figsize = (12, 12))
fig.patch.set_alpha(0.0)

ax1.plot(bra_fert['Year'], bra_fert['Value'], 'b-', linewidth = 4)
ax1.set_xlabel('Years')
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('total (births per woman)', color='b', fontsize = 22)
ax1.tick_params('y', colors='b')

ax2 = ax1.twinx()
ax2.plot(bra_labor['Year'], bra_labor['Value'], 'r-', linewidth = 4)
ax2.set_ylabel('% of female population ages 15+', color='r', fontsize = 22)
ax2.tick_params('y', colors='r')

plt.title('Brazilian fertility and female labor \nforce participation rates (1960-2013)', fontsize = 28)
fig.tight_layout()

fig.savefig('brazil_fert_labor.png', format = 'png', dpi=300)



    


















    
























In [9]:



    


#Stylistic Options
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),    
             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),    
             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),    
             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),    
             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]  

for i in range(len(tableau20)):    
    r, g, b = tableau20[i]    
    tableau20[i] = (r / 255., g / 255., b / 255.)



    











In [10]:



    


ind['CountryCode'].unique()



    


















    
Out[10]:








array(['ARG', 'BRA', 'PRY', 'URY', 'VEN'], dtype=object)

















In [11]:



    


fig = plt.figure(figsize = (12, 12))
fig.patch.set_alpha(0.0)

plt.plot(arg_fert['Year'], arg_fert['Value'], '-', linewidth = 4, label='Argentina', color=tableau20[0])
plt.plot(bra_fert['Year'], bra_fert['Value'], '-', linewidth = 4, label='Brasil', color=tableau20[2])
plt.plot(pry_fert['Year'], pry_fert['Value'], '-', linewidth = 4, label='Paraguay', color=tableau20[4])
plt.plot(ury_fert['Year'], ury_fert['Value'], '-', linewidth = 4, label='Uruguay', color=tableau20[6])
plt.plot(ven_fert['Year'], ven_fert['Value'], '-', linewidth = 4, label='Venezuela', color=tableau20[8])

# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.legend(fontsize = 22)
plt.xlabel('year',  fontsize = 14)
plt.ylabel('total (births per woman)',  fontsize = 22)
plt.title('Fertility rate of Mercosur countries', fontsize=28)
fig.savefig('fert.png', format = 'png', dpi=300)



    


















    
























In [12]:



    


plt.style.use('ggplot')

fig = plt.figure(figsize = (12, 12))
fig.patch.set_alpha(0.0)

plt.plot(arg_labor['Year'], arg_labor['Value'], '-', linewidth = 4, label = 'Argentina', color = tableau20[0])
plt.plot(bra_labor['Year'], bra_labor['Value'], '-', linewidth = 4, label = 'Brasil', color = tableau20[2])
plt.plot(pry_labor['Year'], pry_labor['Value'], '-', linewidth = 4, label = 'Paraguay', color = tableau20[4])
plt.plot(ury_labor['Year'], ury_labor['Value'], '-', linewidth = 4, label = 'Uruguay', color = tableau20[6])
plt.plot(ven_labor['Year'], ven_labor['Value'], '-', linewidth = 4, label = 'Venezuela', color = tableau20[8])

# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.legend(fontsize = 22)
plt.xlabel('year',  fontsize=14)
plt.ylabel('% of female population ages 15+',  fontsize = 22)
plt.title('Female labor force participation of \n Mercosur Countries (1980-2013)', fontsize = 28)
fig.savefig('labor.png', format = 'png', dpi=300)



    


















    
























In [13]:



    


print(len(bra_fert))
print(len(bra_labor))



    


















    






54
32

















In [14]:



    


bra = pd.merge(bra_fert, bra_labor, on = 'Year')
# bra.rename(columns = {'Value_x': 'Fertility rate', 'Value_y': 'Female labor'})



    











In [15]:



    


bra.corr()



    


















    
Out[15]:










  
    

      
      Value_x
      Year
      Value_y
    

  
  
    

      Value_x
      1.000000
      -0.967897
      -0.948653
    

    

      Year
      -0.967897
      1.000000
      0.870472
    

    

      Value_y
      -0.948653
      0.870472
      1.000000
    

  




















In [16]:



    


import seaborn as sns

joint = sns.jointplot('Value_x', 'Value_y', data = bra, kind = 'reg', size = 12, stat_func = None)
plt.subplots_adjust(top=0.95)
joint.fig.suptitle('Brazilian fertility and labor force joint plot', fontsize = 28)
plt.xlabel('total (births per female)', fontsize = 22)
plt.ylabel('% of female population ages 15+', fontsize = 22)

plt.xlim((1, 5))
plt.ylim((20, 60))

plt.annotate('Pearson = -0.95', xy = (3.5, 55), fontsize = 30)
# plt.title('Brazilian fertility and labor force joint plot', fontsize = 24)
joint.savefig('joint_plot.png', format = 'png', dpi = 300)



    


















    






/home/jayme/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

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