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DOC: Add pd.plotting examples and fix broken examples (#33989)
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@deppen8
deppen8 authored May 6, 2020
1 parent 8771c25 commit 18a7e97
Showing 1 changed file with 90 additions and 27 deletions.
117 changes: 90 additions & 27 deletions pandas/plotting/_misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,8 +117,12 @@ def scatter_matrix(
Examples
--------
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix(df, alpha=0.2)
.. plot::
:context: close-figs
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> pd.plotting.scatter_matrix(df, alpha=0.2)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.scatter_matrix(
Expand Down Expand Up @@ -179,24 +183,31 @@ def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6,
... 6.7, 4.6],
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2,
... 3.3, 3.6],
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4,
... 5.7, 1.0],
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2,
... 2.1, 0.2],
... 'Category': ['virginica', 'virginica', 'setosa',
... 'virginica', 'virginica', 'versicolor',
... 'versicolor', 'setosa', 'virginica',
... 'setosa']
... })
>>> rad_viz = pd.plotting.radviz(df, 'Category') # doctest: +SKIP
>>> df = pd.DataFrame(
... {
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
... 'Category': [
... 'virginica',
... 'virginica',
... 'setosa',
... 'virginica',
... 'virginica',
... 'versicolor',
... 'versicolor',
... 'setosa',
... 'virginica',
... 'setosa'
... ]
... }
... )
>>> pd.plotting.radviz(df, 'Category')
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.radviz(
Expand Down Expand Up @@ -243,6 +254,18 @@ def andrews_curves(
Returns
-------
class:`matplotlip.axis.Axes`
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.read_csv(
... 'https://raw.github.com/pandas-dev/'
... 'pandas/master/pandas/tests/data/iris.csv'
... )
>>> pd.plotting.andrews_curves(df, 'Name')
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.andrews_curves(
Expand Down Expand Up @@ -298,10 +321,10 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
This example draws a basic bootstap plot for a Series.
.. plot::
:context: close-figs
:context: close-figs
>>> s = pd.Series(np.random.uniform(size=100))
>>> fig = pd.plotting.bootstrap_plot(s) # doctest: +SKIP
>>> s = pd.Series(np.random.uniform(size=100))
>>> pd.plotting.bootstrap_plot(s)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.bootstrap_plot(
Expand Down Expand Up @@ -358,13 +381,17 @@ def parallel_coordinates(
Examples
--------
>>> from matplotlib import pyplot as plt
>>> df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
'/pandas/tests/data/iris.csv')
>>> pd.plotting.parallel_coordinates(
df, 'Name',
color=('#556270', '#4ECDC4', '#C7F464'))
>>> plt.show()
.. plot::
:context: close-figs
>>> df = pd.read_csv(
... 'https://raw.github.com/pandas-dev/'
... 'pandas/master/pandas/tests/data/iris.csv'
... )
>>> pd.plotting.parallel_coordinates(
... df, 'Name', color=('#556270', '#4ECDC4', '#C7F464')
... )
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.parallel_coordinates(
Expand Down Expand Up @@ -398,6 +425,28 @@ def lag_plot(series, lag=1, ax=None, **kwds):
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
Lag plots are most commonly used to look for patterns in time series data.
Given the following time series
.. plot::
:context: close-figs
>>> np.random.seed(5)
>>> x = np.cumsum(np.random.normal(loc=1, scale=5, size=50))
>>> s = pd.Series(x)
>>> s.plot()
A lag plot with ``lag=1`` returns
.. plot::
:context: close-figs
>>> pd.plotting.lag_plot(s, lag=1)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
Expand All @@ -417,6 +466,20 @@ def autocorrelation_plot(series, ax=None, **kwargs):
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
The horizontal lines in the plot correspond to 95% and 99% confidence bands.
The dashed line is 99% confidence band.
.. plot::
:context: close-figs
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
>>> pd.plotting.autocorrelation_plot(s)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
Expand Down

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