3.1.6.1.1.7. Visualizing factors influencing wages

This example uses seaborn to quickly plot various factors relating wages, experience and eduction.

Seaborn (http://stanford.edu/~mwaskom/software/seaborn/) is a library that combines visualization and statistical fits to show trends in data.

Note that importing seaborn changes the matplotlib style to have an “excel-like” feeling. This changes affect other matplotlib figures. To restore defaults once this example is run, we would need to call plt.rcdefaults().

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Script output:

loading seaborn

Python source code: plot_wage_data.py

# Standard library imports
import urllib
import os

import matplotlib.pyplot as plt

##############################################################################
# Load the data
import pandas

if not os.path.exists('wages.txt'):
    # Download the file if it is not present
    urllib.urlretrieve('http://lib.stat.cmu.edu/datasets/CPS_85_Wages',
                       'wages.txt')

# Give names to the columns
names = [
    'EDUCATION: Number of years of education',
    'SOUTH: 1=Person lives in South, 0=Person lives elsewhere',
    'SEX: 1=Female, 0=Male',
    'EXPERIENCE: Number of years of work experience',
    'UNION: 1=Union member, 0=Not union member',
    'WAGE: Wage (dollars per hour)',
    'AGE: years',
    'RACE: 1=Other, 2=Hispanic, 3=White',
    'OCCUPATION: 1=Management, 2=Sales, 3=Clerical, 4=Service, 5=Professional, 6=Other',
    'SECTOR: 0=Other, 1=Manufacturing, 2=Construction',
    'MARR: 0=Unmarried,  1=Married',
]

short_names = [n.split(':')[0] for n in names]

data = pandas.read_csv('wages.txt', skiprows=27, skipfooter=6, sep=None,
                       header=None)
data.columns = short_names

# Log-transform the wages, because they typically are increased with
# multiplicative factors
import numpy as np
data['WAGE'] = np.log10(data['WAGE'])

##############################################################################
# Plot scatter matrices highlighting different aspects

import seaborn
seaborn.pairplot(data, vars=['WAGE', 'AGE', 'EDUCATION'],
                      kind='reg')

seaborn.pairplot(data, vars=['WAGE', 'AGE', 'EDUCATION'],
                      kind='reg', hue='SEX')
plt.suptitle('Effect of gender: 1=Female, 0=Male')

seaborn.pairplot(data, vars=['WAGE', 'AGE', 'EDUCATION'],
                      kind='reg', hue='RACE')
plt.suptitle('Effect of race: 1=Other, 2=Hispanic, 3=White')

seaborn.pairplot(data, vars=['WAGE', 'AGE', 'EDUCATION'],
                      kind='reg', hue='UNION')
plt.suptitle('Effect of union: 1=Union member, 0=Not union member')


##############################################################################
# Plot a simple regression

seaborn.lmplot(y='WAGE', x='EDUCATION', data=data)

plt.show()

Total running time of the example: 6.58 seconds ( 0 minutes 6.58 seconds)