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Add pct bachelors degrees example
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‎examples/showcase/bachelors_degrees_by_gender.py‎

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import matplotlib.pyplot as plt
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from matplotlib.mlab import csv2rec
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from matplotlib.cbook import get_sample_data
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fname = get_sample_data('percent_bachelors_degrees_women_usa.csv')
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gender_degree_data = csv2rec(fname)
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# These are the colors that will be used in the plot
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color_sequence = ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c',
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'#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5',
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'#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f',
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'#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']
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# You typically want your plot to be ~1.33x wider than tall. This plot
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# is a rare exception because of the number of lines being plotted on it.
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# Common sizes: (10, 7.5) and (12, 9)
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plt.figure(figsize=(12, 14))
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# Remove the plot frame lines. They are unnecessary chartjunk.
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ax = plt.subplot(111)
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ax.spines['top'].set_visible(False)
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ax.spines['bottom'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.spines['left'].set_visible(False)
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# Ensure that the axis ticks only show up on the bottom and left of the plot.
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# Ticks on the right and top of the plot are generally unnecessary chartjunk.
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ax.get_xaxis().tick_bottom()
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ax.get_yaxis().tick_left()
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# Limit the range of the plot to only where the data is.
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# Avoid unnecessary whitespace.
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plt.xlim(1968.5, 2011.1)
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plt.ylim(-0.25, 90)
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# Make sure your axis ticks are large enough to be easily read.
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# You don't want your viewers squinting to read your plot.
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plt.xticks(range(1970, 2011, 10), fontsize=14)
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plt.yticks(range(0, 91, 10), ['{}%'.format(x) for x in range(0, 91, 10)], fontsize=14)
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# Provide tick lines across the plot to help your viewers trace along
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# the axis ticks. Make sure that the lines are light and small so they
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# don't obscure the primary data lines.
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for y in range(10, 91, 10):
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plt.plot(range(1969, 2012), [y] * len(range(1969, 2012)), '--',
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lw=0.5, color='black', alpha=0.3)
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# Remove the tick marks; they are unnecessary with the tick lines we just plotted.
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plt.tick_params(axis='both', which='both', bottom='off', top='off',
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labelbottom='on', left='off', right='off', labelleft='on')
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# Now that the plot is prepared, it's time to actually plot the data!
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# Note that I plotted the majors in order of the highest % in the final year.
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majors = ['Health Professions', 'Public Administration', 'Education',
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'Psychology', 'Foreign Languages', 'English',
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'Communications\nand Journalism', 'Art and Performance', 'Biology',
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'Agriculture', 'Social Sciences and History', 'Business',
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'Math and Statistics', 'Architecture', 'Physical Sciences',
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'Computer Science', 'Engineering']
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y_offsets = {'Foreign Languages': 0.5, 'English': -0.5,
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'Communications\nand Journalism': 0.75,
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'Art and Performance': -0.25, 'Agriculture': 1.25,
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'Social Sciences and History': 0.25, 'Business': -0.75,
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'Math and Statistics': 0.75, 'Architecture': -0.75,
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'Computer Science': 0.75, 'Engineering': -0.25}
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for rank, column in enumerate(majors):
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# Plot each line separately with its own color.
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column_rec_name = column.replace('\n', '_').replace(' ', '_').lower()
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line = plt.plot(gender_degree_data.year,
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gender_degree_data[column_rec_name],
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lw=2.5,
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color=color_sequence[rank])
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# Add a text label to the right end of every line. Most of the code below
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# is adding specific offsets y position because some labels overlapped.
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y_pos = gender_degree_data[column_rec_name][-1] - 0.5
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if column in y_offsets:
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y_pos += y_offsets[column]
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# Again, make sure that all labels are large enough to be easily read
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# by the viewer.
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plt.text(2011.5, y_pos, column, fontsize=14, color=color_sequence[rank])
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# Make the title big enough so it spans the entire plot, but don't make it
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# so big that it requires two lines to show.
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# Note that if the title is descriptive enough, it is unnecessary to include
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# axis labels; they are self-evident, in this plot's case.
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plt.title('Percentage of Bachelor\'s degrees conferred to women in '
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'the U.S.A. by major (1970-2011)\n', fontsize=18, ha='center')
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# Finally, save the figure as a PNG.
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# You can also save it as a PDF, JPEG, etc.
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# Just change the file extension in this call.
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plt.savefig('percent-bachelors-degrees-women-usa.png', bbox_inches='tight')

‎lib/matplotlib/mpl-data/sample_data/percent_bachelors_degrees_women_usa.csv‎

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Year,Agriculture,Architecture,Art and Performance,Biology,Business,Communications and Journalism,Computer Science,Education,Engineering,English,Foreign Languages,Health Professions,Math and Statistics,Physical Sciences,Psychology,Public Administration,Social Sciences and History
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1970,4.22979798,11.92100539,59.7,29.08836297,9.064438975,35.3,13.6,74.53532758,0.8,65.57092343,73.8,77.1,38,13.8,44.4,68.4,36.8
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1971,5.452796685,12.00310559,59.9,29.39440285,9.503186594,35.5,13.6,74.14920369,1,64.55648516,73.9,75.5,39,14.9,46.2,65.5,36.2
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1972,7.42071022,13.21459351,60.4,29.81022105,10.5589621,36.6,14.9,73.55451996,1.2,63.6642632,74.6,76.9,40.2,14.8,47.6,62.6,36.1
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1973,9.653602412,14.7916134,60.2,31.14791477,12.80460152,38.4,16.4,73.50181443,1.6,62.94150212,74.9,77.4,40.9,16.5,50.4,64.3,36.4
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1974,14.07462346,17.44468758,61.9,32.99618284,16.20485038,40.5,18.9,73.33681143,2.2,62.41341209,75.3,77.9,41.8,18.2,52.6,66.1,37.3
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1975,18.33316153,19.13404767,60.9,34.44990213,19.68624931,41.5,19.8,72.80185448,3.2,61.64720641,75,78.9,40.7,19.1,54.5,63,37.7
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1976,22.25276005,21.39449143,61.3,36.07287146,23.4300375,44.3,23.9,72.16652471,4.5,62.14819377,74.4,79.2,41.5,20,56.9,65.6,39.2
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1977,24.6401766,23.74054054,62,38.33138629,27.16342715,46.9,25.7,72.45639481,6.8,62.72306675,74.3,80.5,41.1,21.3,59,69.3,40.5
10+
1978,27.14619175,25.84923973,62.5,40.11249564,30.52751868,49.9,28.1,73.19282134,8.4,63.61912216,74.3,81.9,41.6,22.5,61.3,71.5,41.8
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1979,29.63336549,27.77047744,63.2,42.06555109,33.62163381,52.3,30.2,73.82114234,9.4,65.08838972,74.2,82.3,42.3,23.7,63.3,73.3,43.6
12+
1980,30.75938956,28.08038075,63.4,43.99925716,36.76572529,54.7,32.5,74.98103152,10.3,65.28413007,74.1,83.5,42.8,24.6,65.1,74.6,44.2
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1981,31.31865519,29.84169408,63.3,45.24951206,39.26622984,56.4,34.8,75.84512345,11.6,65.83832154,73.9,84.1,43.2,25.7,66.9,74.7,44.6
14+
1982,32.63666364,34.81624758,63.1,45.96733794,41.94937335,58,36.3,75.84364914,12.4,65.84735212,72.7,84.4,44,27.3,67.5,76.8,44.6
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1983,31.6353471,35.82625735,62.4,46.71313451,43.54206966,58.6,37.1,75.95060123,13.1,65.91837999,71.8,84.6,44.3,27.6,67.9,76.1,44.1
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1984,31.09294748,35.45308311,62.1,47.66908276,45.12403027,59.1,36.8,75.86911601,13.5,65.74986233,72.1,85.1,46.2,28,68.2,75.9,44.1
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1985,31.3796588,36.13334795,61.8,47.9098841,45.747782,59,35.7,75.92343971,13.5,65.79819852,70.8,85.3,46.5,27.5,69,75,43.8
18+
1986,31.19871923,37.24022346,62.1,48.30067763,46.53291505,60,34.7,76.14301516,13.9,65.98256091,71.2,85.7,46.7,28.4,69,75.7,44
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1987,31.48642948,38.73067535,61.7,50.20987789,46.69046648,60.2,32.4,76.96309168,14,66.70603055,72,85.5,46.5,30.4,70.1,76.4,43.9
20+
1988,31.08508746,39.3989071,61.7,50.09981147,46.7648277,60.4,30.8,77.62766177,13.9,67.14449816,72.3,85.2,46.2,29.7,70.9,75.6,44.4
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1989,31.6124031,39.09653994,62,50.77471585,46.7815648,60.5,29.9,78.11191872,14.1,67.01707156,72.4,84.6,46.2,31.3,71.6,76,44.2
22+
1990,32.70344407,40.82404662,62.6,50.81809432,47.20085084,60.8,29.4,78.86685859,14.1,66.92190193,71.2,83.9,47.3,31.6,72.6,77.6,45.1
23+
1991,34.71183749,33.67988118,62.1,51.46880537,47.22432481,60.8,28.7,78.99124597,14,66.24147465,71.1,83.5,47,32.6,73.2,78.2,45.5
24+
1992,33.93165961,35.20235628,61,51.34974154,47.21939541,59.7,28.2,78.43518191,14.5,65.62245655,71,83,47.4,32.6,73.2,77.3,45.8
25+
1993,34.94683208,35.77715877,60.2,51.12484404,47.63933161,58.7,28.5,77.26731199,14.9,65.73095014,70,82.4,46.4,33.6,73.1,78,46.1
26+
1994,36.03267447,34.43353129,59.4,52.2462176,47.98392441,58.1,28.5,75.81493264,15.7,65.64197772,69.1,81.8,47,34.8,72.9,78.8,46.8
27+
1995,36.84480747,36.06321839,59.2,52.59940342,48.57318101,58.8,27.5,75.12525621,16.2,65.93694921,69.6,81.5,46.1,35.9,73,78.8,47.9
28+
1996,38.96977475,35.9264854,58.6,53.78988011,48.6473926,58.7,27.1,75.03519921,16.7,66.43777883,69.7,81.3,46.4,37.3,73.9,79.8,48.7
29+
1997,40.68568483,35.10193413,58.7,54.99946903,48.56105033,60,26.8,75.1637013,17,66.78635548,70,81.9,47,38.3,74.4,81,49.2
30+
1998,41.91240333,37.59854457,59.1,56.35124789,49.2585152,60,27,75.48616027,17.8,67.2554484,70.1,82.1,48.3,39.7,75.1,81.3,50.5
31+
1999,42.88720191,38.63152919,59.2,58.22882288,49.81020815,61.2,28.1,75.83816206,18.6,67.82022113,70.9,83.5,47.8,40.2,76.5,81.1,51.2
32+
2000,45.05776637,40.02358491,59.2,59.38985737,49.80361649,61.9,27.7,76.69214284,18.4,68.36599498,70.9,83.5,48.2,41,77.5,81.1,51.8
33+
2001,45.86601517,40.69028156,59.4,60.71233149,50.27514494,63,27.6,77.37522931,19,68.57852029,71.2,85.1,47,42.2,77.5,80.9,51.7
34+
2002,47.13465821,41.13295053,60.9,61.8951284,50.5523346,63.7,27,78.64424394,18.7,68.82995959,70.5,85.8,45.7,41.1,77.7,81.3,51.5
35+
2003,47.93518721,42.75854266,61.1,62.1694558,50.34559774,64.6,25.1,78.54494815,18.8,68.89448726,70.6,86.5,46,41.7,77.8,81.5,50.9
36+
2004,47.88714025,43.46649345,61.3,61.91458697,49.95089449,64.2,22.2,78.65074774,18.2,68.45473436,70.8,86.5,44.7,42.1,77.8,80.7,50.5
37+
2005,47.67275409,43.10036784,61.4,61.50098432,49.79185139,63.4,20.6,79.06712173,17.9,68.57122114,69.9,86,45.1,41.6,77.5,81.2,50
38+
2006,46.79029957,44.49933107,61.6,60.17284465,49.21091439,63,18.6,78.68630551,16.8,68.29759443,69.6,85.9,44.1,40.8,77.4,81.2,49.8
39+
2007,47.60502633,43.10045895,61.4,59.41199314,49.00045935,62.5,17.6,78.72141311,16.8,67.87492278,70.2,85.4,44.1,40.7,77.1,82.1,49.3
40+
2008,47.570834,42.71173041,60.7,59.30576517,48.88802678,62.4,17.8,79.19632674,16.5,67.59402834,70.2,85.2,43.3,40.7,77.2,81.7,49.4
41+
2009,48.66722357,43.34892051,61,58.48958333,48.84047414,62.8,18.1,79.5329087,16.8,67.96979204,69.3,85.1,43.3,40.7,77.1,82,49.4
42+
2010,48.73004227,42.06672091,61.3,59.01025521,48.75798769,62.5,17.6,79.61862451,17.2,67.92810557,69,85,43.1,40.2,77,81.7,49.3
43+
2011,50.03718193,42.7734375,61.2,58.7423969,48.18041792,62.2,18.2,79.43281184,17.5,68.42673015,69.5,84.8,43.1,40.1,76.7,81.9,49.2

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