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# Barchart Demo

Bar charts of many shapes and sizes with Matplotlib.

Bar charts are useful for visualizing counts, or summary statistics with error bars. These examples show a few ways to do this with Matplotlib.

    # Credit: Josh Hemann

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.ticker import MaxNLocator
    from collections import namedtuple

    n_groups = 5

    means_men = (20, 35, 30, 35, 27)
    std_men = (2, 3, 4, 1, 2)

    means_women = (25, 32, 34, 20, 25)
    std_women = (3, 5, 2, 3, 3)

    fig, ax = plt.subplots()

    index = np.arange(n_groups)
    bar_width = 0.35

    opacity = 0.4
    error_config = {'ecolor': '0.3'}

    rects1 = ax.bar(index, means_men, bar_width,
                    alpha=opacity, color='b',
                    yerr=std_men, error_kw=error_config,
                    label='Men')

    rects2 = ax.bar(index + bar_width, means_women, bar_width,
                    alpha=opacity, color='r',
                    yerr=std_women, error_kw=error_config,
                    label='Women')

    ax.set_xlabel('Group')
    ax.set_ylabel('Scores')
    ax.set_title('Scores by group and gender')
    ax.set_xticks(index + bar_width / 2)
    ax.set_xticklabels(('A', 'B', 'C', 'D', 'E'))
    ax.legend()

    fig.tight_layout()
    plt.show()

This example comes from an application in which grade school gym teachers wanted to be able to show parents how their child did across a handful of fitness tests, and importantly, relative to how other children did. To extract the plotting code for demo purposes, we’ll just make up some data for little Johnny Doe…

    Student = namedtuple('Student', ['name', 'grade', 'gender'])
    Score = namedtuple('Score', ['score', 'percentile'])

    # GLOBAL CONSTANTS
    testNames = ['Pacer Test', 'Flexed Arm\n Hang', 'Mile Run', 'Agility',
                 'Push Ups']
    testMeta = dict(zip(testNames, ['laps', 'sec', 'min:sec', 'sec', '']))

    def attach_ordinal(num):
        """helper function to add ordinal string to integers

        1 -> 1st
        56 -> 56th
        """
        suffixes = dict((str(i), v) for i, v in
                        enumerate(['th', 'st', 'nd', 'rd', 'th',
                                   'th', 'th', 'th', 'th', 'th']))

        v = str(num)
        # special case early teens
        if v in {'11', '12', '13'}:
            return v + 'th'
        return v + suffixes[v[-1]]

    def format_score(scr, test):
        """
        Build up the score labels for the right Y-axis by first
        appending a carriage return to each string and then tacking on
        the appropriate meta information (i.e., 'laps' vs 'seconds'). We
        want the labels centered on the ticks, so if there is no meta
        info (like for pushups) then don't add the carriage return to
        the string
        """
        md = testMeta[test]
        if md:
            return '{0}\n{1}'.format(scr, md)
        else:
            return scr

    def format_ycursor(y):
        y = int(y)
        if y < 0 or y >= len(testNames):
            return ''
        else:
            return testNames[y]

    def plot_student_results(student, scores, cohort_size):
        #  create the figure
        fig, ax1 = plt.subplots(figsize=(9, 7))
        fig.subplots_adjust(left=0.115, right=0.88)
        fig.canvas.set_window_title('Eldorado K-8 Fitness Chart')

        pos = np.arange(len(testNames))

        rects = ax1.barh(pos, [scores[k].percentile for k in testNames],
                         align='center',
                         height=0.5, color='m',
                         tick_label=testNames)

        ax1.set_title(student.name)

        ax1.set_xlim([0, 100])
        ax1.xaxis.set_major_locator(MaxNLocator(11))
        ax1.xaxis.grid(True, linestyle='--', which='major',
                       color='grey', alpha=.25)

        # Plot a solid vertical gridline to highlight the median position
        ax1.axvline(50, color='grey', alpha=0.25)
        # set X-axis tick marks at the deciles
        cohort_label = ax1.text(.5, -.07, 'Cohort Size: {0}'.format(cohort_size),
                                horizontalalignment='center', size='small',
                                transform=ax1.transAxes)

        # Set the right-hand Y-axis ticks and labels
        ax2 = ax1.twinx()

        scoreLabels = [format_score(scores[k].score, k) for k in testNames]

        # set the tick locations
        ax2.set_yticks(pos)
        # make sure that the limits are set equally on both yaxis so the
        # ticks line up
        ax2.set_ylim(ax1.get_ylim())

        # set the tick labels
        ax2.set_yticklabels(scoreLabels)

        ax2.set_ylabel('Test Scores')

        ax2.set_xlabel(('Percentile Ranking Across '
                        '{grade} Grade {gender}s').format(
                            grade=attach_ordinal(student.grade),
                            gender=student.gender.title()))

        rect_labels = []
        # Lastly, write in the ranking inside each bar to aid in interpretation
        for rect in rects:
            # Rectangle widths are already integer-valued but are floating
            # type, so it helps to remove the trailing decimal point and 0 by
            # converting width to int type
            width = int(rect.get_width())

            rankStr = attach_ordinal(width)
            # The bars aren't wide enough to print the ranking inside
            if (width < 5):
                # Shift the text to the right side of the right edge
                xloc = width + 1
                # Black against white background
                clr = 'black'
                align = 'left'
            else:
                # Shift the text to the left side of the right edge
                xloc = 0.98*width
                # White on magenta
                clr = 'white'
                align = 'right'

            # Center the text vertically in the bar
            yloc = rect.get_y() + rect.get_height()/2.0
            label = ax1.text(xloc, yloc, rankStr, horizontalalignment=align,
                             verticalalignment='center', color=clr, weight='bold',
                             clip_on=True)
            rect_labels.append(label)

        # make the interactive mouse over give the bar title
        ax2.fmt_ydata = format_ycursor
        # return all of the artists created
        return {'fig': fig,
                'ax': ax1,
                'ax_right': ax2,
                'bars': rects,
                'perc_labels': rect_labels,
                'cohort_label': cohort_label}

    student = Student('Johnny Doe', 2, 'boy')
    scores = dict(zip(testNames,
                      (Score(v, p) for v, p in
                       zip(['7', '48', '12:52', '17', '14'],
                           np.round(np.random.uniform(0, 1,
                                                      len(testNames))*100, 0)))))
    cohort_size = 62  # The number of other 2nd grade boys

    arts = plot_student_results(student, scores, cohort_size)
    plt.show()

Total running time of the script: ( 0 minutes 0.106 seconds)

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