3.1.6.7. Visualizing factors influencing wages

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

Seaborn (https://seaborn.pydata.org) 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().

# Standard library imports
import os
import matplotlib.pyplot as plt

Load the data

import pandas
import requests
if not os.path.exists("wages.txt"):
# Download the file if it is not present
r = requests.get("http://lib.stat.cmu.edu/datasets/CPS_85_Wages")
with open("wages.txt", "wb") as f:
f.write(r.content)
# 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, engine="python"
)
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 wage data
  • Effect of gender: 1=Female, 0=Male
  • Effect of race: 1=Other, 2=Hispanic, 3=White
  • Effect of union: 1=Union member, 0=Not union member
Text(0.5, 0.98, 'Effect of union: 1=Union member, 0=Not union member')

Plot a simple regression

seaborn.lmplot(y="WAGE", x="EDUCATION", data=data)
plt.show()
plot wage data

Total running time of the script: (0 minutes 9.469 seconds)

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