.. _showcase-bachelors_degrees_by_gender:

showcase example code: bachelors_degrees_by_gender.py
=====================================================



.. plot:: /home/tcaswell/source/my_source/matplotlib/doc/mpl_examples/showcase/bachelors_degrees_by_gender.py

::

    import matplotlib.pyplot as plt
    from matplotlib.mlab import csv2rec
    from matplotlib.cbook import get_sample_data
    
    fname = get_sample_data('percent_bachelors_degrees_women_usa.csv')
    gender_degree_data = csv2rec(fname)
    
    # These are the colors that will be used in the plot
    color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
                      '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',
                      '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',
                      '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']
    
    # You typically want your plot to be ~1.33x wider than tall. This plot
    # is a rare exception because of the number of lines being plotted on it.
    # Common sizes: (10, 7.5) and (12, 9)
    fig, ax = plt.subplots(1, 1, figsize=(12, 14))
    
    # Remove the plot frame lines. They are unnecessary here.
    ax.spines['top'].set_visible(False)
    ax.spines['bottom'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['left'].set_visible(False)
    
    # Ensure that the axis ticks only show up on the bottom and left of the plot.
    # Ticks on the right and top of the plot are generally unnecessary.
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()
    
    # Limit the range of the plot to only where the data is.
    # Avoid unnecessary whitespace.
    plt.xlim(1968.5, 2011.1)
    plt.ylim(-0.25, 90)
    
    # Make sure your axis ticks are large enough to be easily read.
    # You don't want your viewers squinting to read your plot.
    plt.xticks(range(1970, 2011, 10), fontsize=14)
    plt.yticks(range(0, 91, 10), ['{0}%'.format(x)
                                  for x in range(0, 91, 10)], fontsize=14)
    
    # Provide tick lines across the plot to help your viewers trace along
    # the axis ticks. Make sure that the lines are light and small so they
    # don't obscure the primary data lines.
    for y in range(10, 91, 10):
        plt.plot(range(1969, 2012), [y] * len(range(1969, 2012)), '--',
                 lw=0.5, color='black', alpha=0.3)
    
    # Remove the tick marks; they are unnecessary with the tick lines we just
    # plotted.
    plt.tick_params(axis='both', which='both', bottom='off', top='off',
                    labelbottom='on', left='off', right='off', labelleft='on')
    
    # Now that the plot is prepared, it's time to actually plot the data!
    # Note that I plotted the majors in order of the highest % in the final year.
    majors = ['Health Professions', 'Public Administration', 'Education',
              'Psychology', 'Foreign Languages', 'English',
              'Communications\nand Journalism', 'Art and Performance', 'Biology',
              'Agriculture', 'Social Sciences and History', 'Business',
              'Math and Statistics', 'Architecture', 'Physical Sciences',
              'Computer Science', 'Engineering']
    
    y_offsets = {'Foreign Languages': 0.5, 'English': -0.5,
                 'Communications\nand Journalism': 0.75,
                 'Art and Performance': -0.25, 'Agriculture': 1.25,
                 'Social Sciences and History': 0.25, 'Business': -0.75,
                 'Math and Statistics': 0.75, 'Architecture': -0.75,
                 'Computer Science': 0.75, 'Engineering': -0.25}
    
    for rank, column in enumerate(majors):
        # Plot each line separately with its own color.
        column_rec_name = column.replace('\n', '_').replace(' ', '_').lower()
    
        line = plt.plot(gender_degree_data.year,
                        gender_degree_data[column_rec_name],
                        lw=2.5,
                        color=color_sequence[rank])
    
        # Add a text label to the right end of every line. Most of the code below
        # is adding specific offsets y position because some labels overlapped.
        y_pos = gender_degree_data[column_rec_name][-1] - 0.5
    
        if column in y_offsets:
            y_pos += y_offsets[column]
    
        # Again, make sure that all labels are large enough to be easily read
        # by the viewer.
        plt.text(2011.5, y_pos, column, fontsize=14, color=color_sequence[rank])
    
    # Make the title big enough so it spans the entire plot, but don't make it
    # so big that it requires two lines to show.
    
    # Note that if the title is descriptive enough, it is unnecessary to include
    # axis labels; they are self-evident, in this plot's case.
    plt.title('Percentage of Bachelor\'s degrees conferred to women in '
              'the U.S.A. by major (1970-2011)\n', fontsize=18, ha='center')
    
    # Finally, save the figure as a PNG.
    # You can also save it as a PDF, JPEG, etc.
    # Just change the file extension in this call.
    plt.savefig('percent-bachelors-degrees-women-usa.png', bbox_inches='tight')
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)