Skip to content

DOC: Add plotting examples and fix broken examples #33989

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
May 6, 2020
Merged

DOC: Add plotting examples and fix broken examples #33989

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
36a6a35
DOC: Add plotting examples and fix broken examples
deppen8 May 5, 2020
8f3a036
Fix E501 errors
deppen8 May 5, 2020
92103c6
Remove random seed except where it impacts interpretability
deppen8 May 5, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
117 changes: 90 additions & 27 deletions pandas/plotting/_misc.py
View file
Open in desktop
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