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DOC: Fix visualization.rst flake8 issues #24186

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504de70
Repair visualization.rst flake8 issues, remove exclusion from config
addisonlynch Dec 9, 2018
b16dc7b
Added semicolon
addisonlynch Dec 9, 2018
7e3309d
Remove semicolon
addisonlynch Dec 9, 2018
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55 changes: 28 additions & 27 deletions doc/source/visualization.rst
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Expand Up @@ -26,7 +26,7 @@ libraries that go beyond the basics documented here.
Basic Plotting: ``plot``
------------------------

We will demonstrate the basics, see the :ref:`cookbook<cookbook.plotting>` for
We will demonstrate the basics, see the :ref:`cookbook<cookbook.plotting>` for
some advanced strategies.

The ``plot`` method on Series and DataFrame is just a simple wrapper around
Expand Down Expand Up @@ -87,7 +87,7 @@ You can plot one column versus another using the `x` and `y` keywords in

.. note::

For more formatting and styling options, see
For more formatting and styling options, see
:ref:`formatting <visualization.formatting>` below.

.. ipython:: python
Expand Down Expand Up @@ -129,7 +129,7 @@ You can also create these other plots using the methods ``DataFrame.plot.<kind>`

In [14]: df = pd.DataFrame()

In [15]: df.plot.<TAB>
In [15]: df.plot.<TAB> # noqa: E225, E999
df.plot.area df.plot.barh df.plot.density df.plot.hist df.plot.line df.plot.scatter
df.plot.bar df.plot.box df.plot.hexbin df.plot.kde df.plot.pie

Expand Down Expand Up @@ -163,7 +163,8 @@ For labeled, non-time series data, you may wish to produce a bar plot:
plt.figure();

@savefig bar_plot_ex.png
df.iloc[5].plot.bar(); plt.axhline(0, color='k')
df.iloc[5].plot.bar()
plt.axhline(0, color='k');

Calling a DataFrame's :meth:`plot.bar() <DataFrame.plot.bar>` method produces a multiple
bar plot:
Expand Down Expand Up @@ -231,7 +232,7 @@ Histograms can be drawn by using the :meth:`DataFrame.plot.hist` and :meth:`Seri

plt.close('all')

A histogram can be stacked using ``stacked=True``. Bin size can be changed
A histogram can be stacked using ``stacked=True``. Bin size can be changed
using the ``bins`` keyword.

.. ipython:: python
Expand All @@ -246,8 +247,8 @@ using the ``bins`` keyword.

plt.close('all')

You can pass other keywords supported by matplotlib ``hist``. For example,
horizontal and cumulative histograms can be drawn by
You can pass other keywords supported by matplotlib ``hist``. For example,
horizontal and cumulative histograms can be drawn by
``orientation='horizontal'`` and ``cumulative=True``.

.. ipython:: python
Expand Down Expand Up @@ -477,7 +478,7 @@ keyword, will affect the output type as well:

plt.close('all')

The subplots above are split by the numeric columns first, then the value of
The subplots above are split by the numeric columns first, then the value of
the ``g`` column. Below the subplots are first split by the value of ``g``,
then by the numeric columns.

Expand Down Expand Up @@ -579,8 +580,8 @@ each point:

plt.close('all')

You can pass other keywords supported by matplotlib
:meth:`scatter <matplotlib.axes.Axes.scatter>`. The example below shows a
You can pass other keywords supported by matplotlib
:meth:`scatter <matplotlib.axes.Axes.scatter>`. The example below shows a
bubble chart using a column of the ``DataFrame`` as the bubble size.

.. ipython:: python
Expand Down Expand Up @@ -684,15 +685,15 @@ A ``ValueError`` will be raised if there are any negative values in your data.

plt.close('all')

For pie plots it's best to use square figures, i.e. a figure aspect ratio 1.
You can create the figure with equal width and height, or force the aspect ratio
to be equal after plotting by calling ``ax.set_aspect('equal')`` on the returned
For pie plots it's best to use square figures, i.e. a figure aspect ratio 1.
You can create the figure with equal width and height, or force the aspect ratio
to be equal after plotting by calling ``ax.set_aspect('equal')`` on the returned
``axes`` object.

Note that pie plot with :class:`DataFrame` requires that you either specify a
target column by the ``y`` argument or ``subplots=True``. When ``y`` is
specified, pie plot of selected column will be drawn. If ``subplots=True`` is
specified, pie plots for each column are drawn as subplots. A legend will be
Note that pie plot with :class:`DataFrame` requires that you either specify a
target column by the ``y`` argument or ``subplots=True``. When ``y`` is
specified, pie plot of selected column will be drawn. If ``subplots=True`` is
specified, pie plots for each column are drawn as subplots. A legend will be
drawn in each pie plots by default; specify ``legend=False`` to hide it.

.. ipython:: python
Expand Down Expand Up @@ -918,7 +919,7 @@ Lag Plot
Lag plots are used to check if a data set or time series is random. Random
data should not exhibit any structure in the lag plot. Non-random structure
implies that the underlying data are not random. The ``lag`` argument may
be passed, and when ``lag=1`` the plot is essentially ``data[:-1]`` vs.
be passed, and when ``lag=1`` the plot is essentially ``data[:-1]`` vs.
``data[1:]``.

.. ipython:: python
Expand Down Expand Up @@ -954,7 +955,7 @@ If time series is random, such autocorrelations should be near zero for any and
all time-lag separations. If time series is non-random then one or more of the
autocorrelations will be significantly non-zero. The horizontal lines displayed
in the plot correspond to 95% and 99% confidence bands. The dashed line is 99%
confidence band. See the
confidence band. See the
`Wikipedia entry <https://en.wikipedia.org/wiki/Correlogram>`__ for more about
autocorrelation plots.

Expand Down Expand Up @@ -1446,9 +1447,9 @@ Plotting with matplotlib table is now supported in :meth:`DataFrame.plot` and :

plt.close('all')

Also, you can pass a different :class:`DataFrame` or :class:`Series` to the
``table`` keyword. The data will be drawn as displayed in print method
(not transposed automatically). If required, it should be transposed manually
Also, you can pass a different :class:`DataFrame` or :class:`Series` to the
``table`` keyword. The data will be drawn as displayed in print method
(not transposed automatically). If required, it should be transposed manually
as seen in the example below.

.. ipython:: python
Expand All @@ -1463,9 +1464,9 @@ as seen in the example below.

plt.close('all')

There also exists a helper function ``pandas.plotting.table``, which creates a
table from :class:`DataFrame` or :class:`Series`, and adds it to an
``matplotlib.Axes`` instance. This function can accept keywords which the
There also exists a helper function ``pandas.plotting.table``, which creates a
table from :class:`DataFrame` or :class:`Series`, and adds it to an
``matplotlib.Axes`` instance. This function can accept keywords which the
matplotlib `table <http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table>`__ has.

.. ipython:: python
Expand Down Expand Up @@ -1597,8 +1598,8 @@ Plotting directly with matplotlib

In some situations it may still be preferable or necessary to prepare plots
directly with matplotlib, for instance when a certain type of plot or
customization is not (yet) supported by pandas. ``Series`` and ``DataFrame``
objects behave like arrays and can therefore be passed directly to
customization is not (yet) supported by pandas. ``Series`` and ``DataFrame``
objects behave like arrays and can therefore be passed directly to
matplotlib functions without explicit casts.

pandas also automatically registers formatters and locators that recognize date
Expand Down
1 change: 0 additions & 1 deletion setup.cfg
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Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,6 @@ exclude =
doc/source/options.rst
doc/source/release.rst
doc/source/reshaping.rst
doc/source/visualization.rst


[yapf]
Expand Down