.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "packages/statistics/auto_examples/plot_wage_data.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_packages_statistics_auto_examples_plot_wage_data.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_packages_statistics_auto_examples_plot_wage_data.py:


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().

.. GENERATED FROM PYTHON SOURCE LINES 16-22

.. code-block:: default


    # Standard library imports
    import os

    import matplotlib.pyplot as plt








.. GENERATED FROM PYTHON SOURCE LINES 23-24

Load the data

.. GENERATED FROM PYTHON SOURCE LINES 24-59

.. code-block:: default

    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'])








.. GENERATED FROM PYTHON SOURCE LINES 60-61

Plot scatter matrices highlighting different aspects

.. GENERATED FROM PYTHON SOURCE LINES 61-79

.. code-block:: default


    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')





.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_001.png
         :alt: plot wage data
         :srcset: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_002.png
         :alt: Effect of gender: 1=Female, 0=Male
         :srcset: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_003.png
         :alt: Effect of race: 1=Other, 2=Hispanic, 3=White
         :srcset: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_004.png
         :alt: Effect of union: 1=Union member, 0=Not union member
         :srcset: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_004.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Text(0.5, 0.98, 'Effect of union: 1=Union member, 0=Not union member')



.. GENERATED FROM PYTHON SOURCE LINES 80-81

Plot a simple regression

.. GENERATED FROM PYTHON SOURCE LINES 81-85

.. code-block:: default


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

    plt.show()



.. image-sg:: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_005.png
   :alt: plot wage data
   :srcset: /packages/statistics/auto_examples/images/sphx_glr_plot_wage_data_005.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  10.278 seconds)


.. _sphx_glr_download_packages_statistics_auto_examples_plot_wage_data.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_wage_data.py <plot_wage_data.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_wage_data.ipynb <plot_wage_data.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_