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按性别划分的学士学位

一种多时间序列的图形，它演示了绘图框架、刻度线和标签的广泛自定义样式以及线图属性。

还演示了文本标签沿右边缘的自定义放置，作为传统图例的替代。

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.cbook import get_sample_data


fname = get_sample_data('percent_bachelors_degrees_women_usa.csv',
                        asfileobj=False)
gender_degree_data = np.genfromtxt(fname, delimiter=',', names=True)

# 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))

# These are the colors that will be used in the plot
ax.set_prop_cycle(color=[
    '#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a',
    '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94',
    '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',
    '#17becf', '#9edae5'])

# 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()

fig.subplots_adjust(left=.06, right=.75, bottom=.02, top=.94)
# Limit the range of the plot to only where the data is.
# Avoid unnecessary whitespace.
ax.set_xlim(1969.5, 2011.1)
ax.set_ylim(-0.25, 90)

# Set a fixed location and format for ticks.
ax.set_xticks(range(1970, 2011, 10))
ax.set_yticks(range(0, 91, 10))
# Use automatic StrMethodFormatter creation
ax.xaxis.set_major_formatter('{x:.0f}')
ax.yaxis.set_major_formatter('{x:.0f}%')

# 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.
ax.grid(True, 'major', 'y', ls='--', lw=.5, c='k', alpha=.3)

# Remove the tick marks; they are unnecessary with the tick lines we just
# plotted. Make sure your axis ticks are large enough to be easily read.
# You don't want your viewers squinting to read your plot.
ax.tick_params(axis='both', which='both', labelsize=14,
               bottom=False, top=False, labelbottom=True,
               left=False, right=False, labelleft=True)

# 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 column in majors:
    # Plot each line separately with its own color.
    column_rec_name = column.replace('\n', '_').replace(' ', '_')

    line, = ax.plot('Year', column_rec_name, data=gender_degree_data,
                    lw=2.5)

    # 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.
    ax.text(2011.5, y_pos, column, fontsize=14, color=line.get_color())

# 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.
fig.suptitle("Percentage of Bachelor's degrees conferred to women in "
             "the U.S.A. by major (1970-2011)", 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.
# fig.savefig('percent-bachelors-degrees-women-usa.png', bbox_inches='tight')
plt.show()
Percentage of Bachelor's degrees conferred to women in the U.S.A. by major (1970-2011)