8000 curly brackets dict · matplotlib/matplotlib@57aa332 · GitHub
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curly brackets dict
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‎doc/conf.py

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@@ -92,7 +92,7 @@ def _check_deps():
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'python': ('https://docs.python.org/', None),
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'numpy': ('https://docs.scipy.org/doc/numpy/', None),
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'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None),
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'pandas': ('https://pandas.pydata.org/pandas-docs/stable', None)
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'pandas': ('http://pandas.pydata.org/pandas-docs/stable', None)
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}
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‎examples/misc/keyword_plotting.py

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@@ -7,22 +7,22 @@
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access particular variables with strings. For example, with
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:class:`numpy.recarray` or :class:`pandas.DataFrame`.
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Matplotlib allows you provide such an object with
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the ``data`` keyword argument. If provided, then you may generate plots with
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the strings corresponding to these variables.
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Matplotlib allows you provide such an object with the ``data`` keyword
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argument. If provided, then you may generate plots with the strings
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corresponding to these variables.
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"""
< 8000 code>1414

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import numpy as np
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import matplotlib.pyplot as plt
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np.random.seed(19680801)
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data = dict([('a', np.arange(50)),
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('c', np.random.randint(0, 50, 50)),
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('d', np.random.randn(50))])
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data = {'a': np.arange(50),
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'c': np.random.randint(0, 50, 50),
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'd': np.random.randn(50)}
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data['b'] = data['a'] + 10 * np.random.randn(50)
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data['d'] = np.abs(data['d']) * 100
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fig, ax = plt.subplots()
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ax.scatter('a', 'b', c='c', s='d', data=data)
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ax.set(xlabel='a', ylabel='b')
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ax.set(xlabel='entry a', ylabel='entry b')
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plt.show()

‎tutorials/01_introductory/pyplot.py

Lines changed: 6 additions & 6 deletions
< 8000 /div>
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@@ -107,15 +107,15 @@
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# the ``data`` keyword argument. If provided, then you may generate plots with
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# the strings corresponding to these variables.
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data = dict([('a', np.arange(50)),
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('c', np.random.randint(0, 50, 50)),
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('d', np.random.randn(50))])
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data = {'a': np.arange(50),
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'c': np.random.randint(0, 50, 50),
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'd': np.random.randn(50)}
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data['b'] = data['a'] + 10 * np.random.randn(50)
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data['d'] = np.abs(data['d']) * 100
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plt.plot('a', 'b', color='c', data=data)
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plt.xlabel('a')
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plt.ylabel('b')
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plt.scatter('a', 'b', color='c', s='d', data=data)
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plt.xlabel('entry a')
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plt.ylabel('entry b')
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plt.show()
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###############################################################################

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