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np.set_printoptions(precision = 4 , suppress = True )
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pd.options.display.max_rows = 15
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import matplotlib
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- matplotlib.style.use(' ggplot ' )
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+ matplotlib.style.use(' seaborn ' )
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import matplotlib.pyplot as plt
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plt.close(' all' )
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@@ -24,12 +24,12 @@ We use the standard convention for referencing the matplotlib API:
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import matplotlib.pyplot as plt
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- The plots in this document are made using matplotlib's ``ggplot `` style (new in version 1.4 ):
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+ The plots in this document are made using matplotlib's ``seaborn `` style (new in version 1.5 ):
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.. code-block :: python
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import matplotlib
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- matplotlib.style.use(' ggplot ' )
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+ matplotlib.style.use(' seaborn ' )
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We provide the basics in pandas to easily create decent looking plots.
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See the :ref: `ecosystem <ecosystem.visualization >` section for visualization
@@ -134,7 +134,7 @@ For example, a bar plot can be created the following way:
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plt.figure();
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@savefig bar_plot_ex.png
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- df.iloc[5 ].plot(kind = ' bar' ); plt.axhline( 0 , color = ' k ' )
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+ df.iloc[5 ].plot(kind = ' bar' );
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.. versionadded :: 0.17.0
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@@ -1049,6 +1049,18 @@ be colored differently.
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Plot Formatting
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---------------
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+ Setting the plot style
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+ ~~~~~~~~~~~~~~~~~~~~~~~~
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+
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+ From version 1.5 and up, matplotlib offers a range of preconfigured plotting style. Setting the
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+ style can be used to easily give the plots the general look that you want.
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+ Setting the style is as easy as calling ``matplotlib.style.use(my_plot_style) `` before
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+ creating your plot. For example you could do ``matplotlib.style.use('ggplot') `` for ggplot-style
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+ plots.
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+ You can see the various available style names at ``matplotlib.style.available `` and it's very
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+ easy to try them out.
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+
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Most plotting methods have a set of keyword arguments that control the
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layout and formatting of the returned plot:
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