Before rebase

jklymak · jklymak · commit 782bbb123fd6 · 2021-11-15T16:03:24.000+01:00
diff --git a/tutorials/introductory/usage.py b/tutorials/introductory/usage.py
@@ -100,17 +100,29 @@
 # input.  Classes that are similar to arrays ('array-like') such as `pandas`
 # data objects and `numpy.matrix` may not work as intended.  Common convention
 # is to convert these to `numpy.array` objects prior to plotting.
-#
-# For example, to convert a `pandas.DataFrame` ::
-#
-#   a = pandas.DataFrame(np.random.rand(4, 5), columns = list('abcde'))
-#   a_asarray = a.values
-#
-# and to convert a `numpy.matrix` ::
+# For example, to convert a `numpy.matrix` ::
 #
 #   b = np.matrix([[1, 2], [3, 4]])
 #   b_asarray = np.asarray(b)
 #
+# There are some instances where you have data in a format that lets you
+# access particular variables with strings. For example, a *dict*, a
+# `numpy.recarray`, or a `pandas.DataFrame`.  Matplotlib allows you provide
+# the ``data`` keyword argument and generate plots passing the strings
+# corresponding to these *x* and *y* variables.
+
+data = {'a': np.arange(50),
+        'c': np.random.randint(0, 50, 50),
+        'd': np.random.randn(50)}
+data['b'] = data['a'] + 10 * np.random.randn(50)
+data['d'] = np.abs(data['d']) * 100
+
+fig, ax = plt.subplots()
+ax.scatter('a', 'b', c='c', s='d', data=data)
+ax.set_xlabel('entry a')
+ax.set_ylabel('entry b')
+
+##############################################################################
 # .. _coding_styles:
 #
 # The object-oriented interface and the pyplot interface
@@ -229,6 +241,28 @@ def my_plotter(ax, data1, data2, param_dict):
 # Matplotlib has one at `mpl-cookiecutter
 # <https://github.com/matplotlib/matplotlib-extension-cookiecutter>`_
 #
+# Working with multiple figures and axes
+# ======================================
+#
+# You can open multiple figures with multiple calls to
+# ``fig = plt.figure()`` or ``fig2, ax = plt.subplots()``.  By keeping the
+# object references you can add artists to either figure.
+#
+# Multiple axes can be added a number of ways, but the most basic is
+# ``plt.subplots()`` as used above.  One can achieve more complex layouts,
+# with axes spanning columns or rows, using `~.pyplot.subplot_mosaic`:
+
+fig, axd = plt.subplot_mosaic([['upleft', 'right'],
+                               ['lowleft', 'right']], constrained_layout=True)
+axd['upleft'].set_title('upleft')
+axd['lowleft'].set_title('lowleft')
+axd['right'].set_title('right')
+
+
+
+
+#
+#
 # More reading
 # ============
 #
