.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/lines_bars_and_markers/stackplot_demo.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. meta:: :keywords: codex .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_lines_bars_and_markers_stackplot_demo.py: =========================== Stackplots and streamgraphs =========================== .. GENERATED FROM PYTHON SOURCE LINES 8-14 Stackplots ---------- Stackplots draw multiple datasets as vertically stacked areas. This is useful when the individual data values and additionally their cumulative value are of interest. .. GENERATED FROM PYTHON SOURCE LINES 14-40 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np # data from United Nations World Population Prospects (Revision 2019) # https://population.un.org/wpp/, license: CC BY 3.0 IGO year = [1950, 1960, 1970, 1980, 1990, 2000, 2010, 2018] population_by_continent = { 'africa': [228, 284, 365, 477, 631, 814, 1044, 1275], 'americas': [340, 425, 519, 619, 727, 840, 943, 1006], 'asia': [1394, 1686, 2120, 2625, 3202, 3714, 4169, 4560], 'europe': [220, 253, 276, 295, 310, 303, 294, 293], 'oceania': [12, 15, 19, 22, 26, 31, 36, 39], } fig, ax = plt.subplots() ax.stackplot(year, population_by_continent.values(), labels=population_by_continent.keys(), alpha=0.8) ax.legend(loc='upper left', reverse=True) ax.set_title('World population') ax.set_xlabel('Year') ax.set_ylabel('Number of people (millions)') plt.show() .. image-sg:: /gallery/lines_bars_and_markers/images/sphx_glr_stackplot_demo_001.png :alt: World population :srcset: /gallery/lines_bars_and_markers/images/sphx_glr_stackplot_demo_001.png, /gallery/lines_bars_and_markers/images/sphx_glr_stackplot_demo_001_2_00x.png 2.00x :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 41-46 Streamgraphs ------------ Using the *baseline* parameter, you can turn an ordinary stacked area plot with baseline 0 into a stream graph. .. GENERATED FROM PYTHON SOURCE LINES 46-72 .. code-block:: Python # Fixing random state for reproducibility np.random.seed(19680801) def gaussian_mixture(x, n=5): """Return a random mixture of *n* Gaussians, evaluated at positions *x*.""" def add_random_gaussian(a): amplitude = 1 / (.1 + np.random.random()) dx = x[-1] - x[0] x0 = (2 * np.random.random() - .5) * dx z = 10 / (.1 + np.random.random()) / dx a += amplitude * np.exp(-(z * (x - x0))**2) a = np.zeros_like(x) for j in range(n): add_random_gaussian(a) return a x = np.linspace(0, 100, 101) ys = [gaussian_mixture(x) for _ in range(3)] fig, ax = plt.subplots() ax.stackplot(x, ys, baseline='wiggle') plt.show() .. image-sg:: /gallery/lines_bars_and_markers/images/sphx_glr_stackplot_demo_002.png :alt: stackplot demo :srcset: /gallery/lines_bars_and_markers/images/sphx_glr_stackplot_demo_002.png, /gallery/lines_bars_and_markers/images/sphx_glr_stackplot_demo_002_2_00x.png 2.00x :class: sphx-glr-single-img .. _sphx_glr_download_gallery_lines_bars_and_markers_stackplot_demo.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: stackplot_demo.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: stackplot_demo.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_