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DOC: Fix visualization.rst flake8 issues (#24186)
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doc/source/visualization.rst

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@@ -26,7 +26,7 @@ libraries that go beyond the basics documented here.
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Basic Plotting: ``plot``
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------------------------
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We will demonstrate the basics, see the :ref:`cookbook<cookbook.plotting>` for
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We will demonstrate the basics, see the :ref:`cookbook<cookbook.plotting>` for
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some advanced strategies.
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The ``plot`` method on Series and DataFrame is just a simple wrapper around
@@ -87,7 +87,7 @@ You can plot one column versus another using the `x` and `y` keywords in
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.. note::
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For more formatting and styling options, see
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For more formatting and styling options, see
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:ref:`formatting <visualization.formatting>` below.
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.. ipython:: python
@@ -129,7 +129,7 @@ You can also create these other plots using the methods ``DataFrame.plot.<kind>`
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In [14]: df = pd.DataFrame()
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In [15]: df.plot.<TAB>
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In [15]: df.plot.<TAB> # noqa: E225, E999
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df.plot.area df.plot.barh df.plot.density df.plot.hist df.plot.line df.plot.scatter
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df.plot.bar df.plot.box df.plot.hexbin df.plot.kde df.plot.pie
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@@ -163,7 +163,8 @@ For labeled, non-time series data, you may wish to produce a bar plot:
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plt.figure();
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@savefig bar_plot_ex.png
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df.iloc[5].plot.bar(); plt.axhline(0, color='k')
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df.iloc[5].plot.bar()
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plt.axhline(0, color='k');
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Calling a DataFrame's :meth:`plot.bar() <DataFrame.plot.bar>` method produces a multiple
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bar plot:
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plt.close('all')
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A histogram can be stacked using ``stacked=True``. Bin size can be changed
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A histogram can be stacked using ``stacked=True``. Bin size can be changed
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using the ``bins`` keyword.
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.. ipython:: python
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plt.close('all')
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You can pass other keywords supported by matplotlib ``hist``. For example,
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horizontal and cumulative histograms can be drawn by
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You can pass other keywords supported by matplotlib ``hist``. For example,
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horizontal and cumulative histograms can be drawn by
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``orientation='horizontal'`` and ``cumulative=True``.
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.. ipython:: python
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plt.close('all')
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The subplots above are split by the numeric columns first, then the value of
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The subplots above are split by the numeric columns first, then the value of
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the ``g`` column. Below the subplots are first split by the value of ``g``,
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then by the numeric columns.
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plt.close('all')
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You can pass other keywords supported by matplotlib
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:meth:`scatter <matplotlib.axes.Axes.scatter>`. The example below shows a
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You can pass other keywords supported by matplotlib
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:meth:`scatter <matplotlib.axes.Axes.scatter>`. The example below shows a
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bubble chart using a column of the ``DataFrame`` as the bubble size.
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.. ipython:: python
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plt.close('all')
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For pie plots it's best to use square figures, i.e. a figure aspect ratio 1.
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You can create the figure with equal width and height, or force the aspect ratio
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to be equal after plotting by calling ``ax.set_aspect('equal')`` on the returned
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For pie plots it's best to use square figures, i.e. a figure aspect ratio 1.
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You can create the figure with equal width and height, or force the aspect ratio
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to be equal after plotting by calling ``ax.set_aspect('equal')`` on the returned
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``axes`` object.
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Note that pie plot with :class:`DataFrame` requires that you either specify a
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target column by the ``y`` argument or ``subplots=True``. When ``y`` is
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specified, pie plot of selected column will be drawn. If ``subplots=True`` is
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specified, pie plots for each column are drawn as subplots. A legend will be
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Note that pie plot with :class:`DataFrame` requires that you either specify a
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target column by the ``y`` argument or ``subplots=True``. When ``y`` is
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specified, pie plot of selected column will be drawn. If ``subplots=True`` is
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specified, pie plots for each column are drawn as subplots. A legend will be
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drawn in each pie plots by default; specify ``legend=False`` to hide it.
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.. ipython:: python
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Lag plots are used to check if a data set or time series is random. Random
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data should not exhibit any structure in the lag plot. Non-random structure
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implies that the underlying data are not random. The ``lag`` argument may
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be passed, and when ``lag=1`` the plot is essentially ``data[:-1]`` vs.
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be passed, and when ``lag=1`` the plot is essentially ``data[:-1]`` vs.
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``data[1:]``.
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.. ipython:: python
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all time-lag separations. If time series is non-random then one or more of the
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autocorrelations will be significantly non-zero. The horizontal lines displayed
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in the plot correspond to 95% and 99% confidence bands. The dashed line is 99%
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confidence band. See the
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confidence band. See the
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`Wikipedia entry <https://en.wikipedia.org/wiki/Correlogram>`__ for more about
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autocorrelation plots.
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plt.close('all')
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Also, you can pass a different :class:`DataFrame` or :class:`Series` to the
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``table`` keyword. The data will be drawn as displayed in print method
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(not transposed automatically). If required, it should be transposed manually
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Also, you can pass a different :class:`DataFrame` or :class:`Series` to the
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``table`` keyword. The data will be drawn as displayed in print method
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(not transposed automatically). If required, it should be transposed manually
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as seen in the example below.
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.. ipython:: python
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plt.close('all')
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There also exists a helper function ``pandas.plotting.table``, which creates a
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table from :class:`DataFrame` or :class:`Series`, and adds it to an
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``matplotlib.Axes`` instance. This function can accept keywords which the
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There also exists a helper function ``pandas.plotting.table``, which creates a
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table from :class:`DataFrame` or :class:`Series`, and adds it to an
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``matplotlib.Axes`` instance. This function can accept keywords which the
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matplotlib `table <http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table>`__ has.
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.. ipython:: python
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In some situations it may still be preferable or necessary to prepare plots
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directly with matplotlib, for instance when a certain type of plot or
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customization is not (yet) supported by pandas. ``Series`` and ``DataFrame``
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objects behave like arrays and can therefore be passed directly to
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customization is not (yet) supported by pandas. ``Series`` and ``DataFrame``
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objects behave like arrays and can therefore be passed directly to
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matplotlib functions without explicit casts.
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pandas also automatically registers formatters and locators that recognize date

setup.cfg

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@@ -87,7 +87,6 @@ exclude =
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doc/source/options.rst
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doc/source/release.rst
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doc/source/reshaping.rst
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doc/source/visualization.rst
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[yapf]

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