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@@ -896,7 +896,7 @@ of the same class will usually be closer together and form larger structures.
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.. ipython:: python
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from pandas.tools.plotting import andrews_curves
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from pandas.plotting import andrews_curves
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data = pd.read_csv('data/iris.data')
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@@ -918,7 +918,7 @@ represents one data point. Points that tend to cluster will appear closer togeth
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.. ipython:: python
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from pandas.tools.plotting import parallel_coordinates
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from pandas.plotting import parallel_coordinates
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data = pd.read_csv('data/iris.data')
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@@ -948,7 +948,7 @@ implies that the underlying data are not random.
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.. ipython:: python
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from pandas.tools.plotting import lag_plot
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from pandas.plotting import lag_plot
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plt.figure()
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@@ -983,7 +983,7 @@ confidence band.
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.. ipython:: python
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from pandas.tools.plotting import autocorrelation_plot
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from pandas.plotting import autocorrelation_plot
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plt.figure()
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@@ -1016,7 +1016,7 @@ are what constitutes the bootstrap plot.
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.. ipython:: python
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from pandas.tools.plotting import bootstrap_plot
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from pandas.plotting import bootstrap_plot
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data = pd.Series(np.random.rand(1000))
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@@ -1048,7 +1048,7 @@ be colored differently.
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.. ipython:: python
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from pandas.tools.plotting import radviz
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from pandas.plotting import radviz
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data = pd.read_csv('data/iris.data')
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@@ -1228,14 +1228,14 @@ Using the ``x_compat`` parameter, you can suppress this behavior:
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plt.close('all')
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If you have more than one plot that needs to be suppressed, the ``use`` method
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in ``pandas.plot_params`` can be used in a `with statement`:
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in ``pandas.plotting.plot_params`` can be used in a `with statement`:
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.. ipython:: python
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plt.figure()
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@savefigser_plot_suppress_context.png
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with pd.plot_params.use('x_compat', True):
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with pd.plotting.plot_params.use('x_compat', True):
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df.A.plot(color='r')
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df.B.plot(color='g')
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df.C.plot(color='b')
@@ -1450,11 +1450,11 @@ Also, you can pass different :class:`DataFrame` or :class:`Series` for ``table``
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plt.close('all')
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Finally, there is a helper function ``pandas.tools.plotting.table`` to create a table from :class:`DataFrame` and :class:`Series`, and add it to an ``matplotlib.Axes``. This function can accept keywords which matplotlib table has.
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Finally, there is a helper function ``pandas.plotting.table`` to create a table from :class:`DataFrame` and :class:`Series`, and add it to an ``matplotlib.Axes``. This function can accept keywords which matplotlib table has.
Copy file name to clipboardExpand all lines: doc/source/whatsnew/v0.20.0.txt
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@@ -21,6 +21,7 @@ Highlights include:
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- Support for S3 handling now uses ``s3fs``, see :ref:`here <whatsnew_0200.api_breaking.s3>`
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- Google BigQuery support now uses the ``pandas-gbq`` library, see :ref:`here <whatsnew_0200.api_breaking.gbq>`
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- Switched the test framework to use `pytest <http://doc.pytest.org/en/latest>`__ (:issue:`13097`)
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- The ``pandas.tools.plotting`` module has been deprecated, moved to ``pandas.plotting``. See :ref:`here <whatsnew_0200.api_breaking.plotting>`
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Check the :ref:`API Changes <whatsnew_0200.api_breaking>` and :ref:`deprecations <whatsnew_0200.deprecations>` before updating.
@@ -557,6 +558,31 @@ Using ``.iloc``. Here we will get the location of the 'A' column, then use *posi
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df.iloc[[0, 2], df.columns.get_loc('A')]
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.. _whatsnew_0200.api_breaking.deprecate_plotting
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Deprecate .plotting
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^^^^^^^^^^^^^^^^^^^
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The ``pandas.tools.plotting`` module has been deprecated, in favor of the top level ``pandas.plotting`` module. All the public plotting functions are now available
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from ``pandas.plotting`` (:issue:`12548`).
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Furthermore, the top-level ``pandas.scatter_matrix`` and ``pandas.plot_params`` are deprecated.
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Users can import these from ``pandas.plotting`` as well.
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