diff --git a/.flake8 b/.flake8 index 7da31c57bafd..6d63f6b929a0 100644 --- a/.flake8 +++ b/.flake8 @@ -140,6 +140,7 @@ per-file-ignores = examples/lines_bars_and_markers/fill.py: E402 examples/lines_bars_and_markers/fill_between_demo.py: E402 examples/lines_bars_and_markers/filled_step.py: E402 + examples/lines_bars_and_markers/horizontal_barchart_distribution.py: E402 examples/lines_bars_and_markers/joinstyle.py: E402 examples/lines_bars_and_markers/scatter_piecharts.py: E402 examples/lines_bars_and_markers/scatter_with_legend.py: E402 diff --git a/examples/lines_bars_and_markers/horizontal_barchart_distribution.py b/examples/lines_bars_and_markers/horizontal_barchart_distribution.py new file mode 100644 index 000000000000..73367e4b1d60 --- /dev/null +++ b/examples/lines_bars_and_markers/horizontal_barchart_distribution.py @@ -0,0 +1,90 @@ +""" +============================================= +Discrete distribution as horizontal bar chart +============================================= + +Stacked bar charts can be used to visualize discrete distributions. + +This example visualizes the result of a survey in which people could rate +their agreement to questions on a five-element scale. + +The horizontal stacking is achieved by calling `~.Axes.barh()` for each +category and passing the starting point as the cumulative sum of the +already drawn bars via the parameter ``left``. +""" + +import numpy as np +import matplotlib.pyplot as plt + + +category_names = ['Strongly disagree', 'Disagree', + 'Neither agree nor disagree', 'Agree', 'Strongly agree'] +results = { + 'Question 1': [10, 15, 17, 32, 26], + 'Question 2': [26, 22, 29, 10, 13], + 'Question 3': [35, 37, 7, 2, 19], + 'Question 4': [32, 11, 9, 15, 33], + 'Question 5': [21, 29, 5, 5, 40], + 'Question 6': [8, 19, 5, 30, 38] +} + + +def survey(results, category_names): + """ + Parameters + ---------- + results : dict + A mapping from question labels to a list of answers per category. + It is assumed all lists contain the same number of entries and that + it matches the length of *category_names*. + category_names : list of str + The category labels. + """ + labels = list(results.keys()) + data = np.array(list(results.values())) + data_cum = data.cumsum(axis=1) + category_colors = plt.get_cmap('RdYlGn')( + np.linspace(0.15, 0.85, data.shape[1])) + + fig, ax = plt.subplots(figsize=(9.2, 5)) + ax.invert_yaxis() + ax.xaxis.set_visible(False) + ax.set_xlim(0, np.sum(data, axis=1).max()) + + for i, (colname, color) in enumerate(zip(category_names, category_colors)): + widths = data[:, i] + starts = data_cum[:, i] - widths + ax.barh(labels, widths, left=starts, height=0.5, + label=colname, color=color) + xcenters = starts + widths / 2 + + r, g, b, _ = color + text_color = 'white' if r * g * b < 0.5 else 'darkgrey' + for y, (x, c) in enumerate(zip(xcenters, widths)): + ax.text(x, y, str(int(c)), ha='center', va='center', + color=text_color) + ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1), + loc='lower left', fontsize='small') + + return fig, ax + + +survey(results, category_names) + +############################################################################# +# +# ------------ +# +# References +# """""""""" +# +# The use of the following functions, methods, classes and modules is shown +# in this example: + +import matplotlib +matplotlib.axes.Axes.barh +matplotlib.pyplot.barh +matplotlib.axes.Axes.text +matplotlib.pyplot.text +matplotlib.axes.Axes.legend +matplotlib.pyplot.legend