.. _sphx_glr_gallery_statistics_barchart_demo.py: ============= 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. .. code-block:: python # 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() .. image:: /gallery/statistics/images/sphx_glr_barchart_demo_001.png :align: center 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... .. code-block:: python 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() .. image:: /gallery/statistics/images/sphx_glr_barchart_demo_002.png :align: center .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: barchart_demo.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: barchart_demo.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_