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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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@@ -1438,11 +1438,11 @@ 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.
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