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.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   np.random.seed(123456)
   from pandas import *
   import pandas.util.testing as tm
   randn = np.random.randn
   np.set_printoptions(precision=4, suppress=True)
   import matplotlib.pyplot as plt
   plt.close('all')

Plotting with matplotlib

Note

We intend to build more plotting integration with matplotlib as time goes on.

We use the standard convention for referencing the matplotlib API:

.. ipython:: python

   import matplotlib.pyplot as plt

Basic plotting: plot

The plot method on Series and DataFrame is just a simple wrapper around plt.plot:

.. ipython:: python

   ts = Series(randn(1000), index=DateRange('1/1/2000', periods=1000))
   ts = ts.cumsum()

   @savefig series_plot_basic.png width=4.5in
   ts.plot()

If the index consists of dates, it calls gca().autofmt_xdate() to try to format the x-axis nicely as per above. The method takes a number of arguments for controlling the look of the plot:

.. ipython:: python

   @savefig series_plot_basic2.png width=4.5in
   plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()

On DataFrame, plot is a convenience to plot all of the columns with labels:

.. ipython:: python

   df = DataFrame(randn(1000, 4), index=ts.index,
                  columns=['A', 'B', 'C', 'D'])
   df = df.cumsum()

   @savefig frame_plot_basic.png width=4.5in
   plt.figure(); df.plot(); plt.legend(loc='best')

You may set the legend argument to False to hide the legend, which is shown by default.

.. ipython:: python

   @savefig frame_plot_basic_noleg.png width=4.5in
   df.plot(legend=False)

Some other options are available, like plotting each Series on a different axis:

.. ipython:: python

   @savefig frame_plot_subplots.png width=4.5in
   df.plot(subplots=True, figsize=(8, 8)); plt.legend(loc='best')

You may pass logy to get a log-scale Y axis.

.. ipython:: python

   plt.figure();

   ts = Series(randn(1000), index=DateRange('1/1/2000', periods=1000))
   ts = np.exp(ts.cumsum())

   @savefig series_plot_logy.png width=4.5in
   ts.plot(logy=True)

Targeting different subplots

You can pass an ax argument to Series.plot to plot on a particular axis:

.. ipython:: python

   fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 5))
   df['A'].plot(ax=axes[0,0]); axes[0,0].set_title('A')
   df['B'].plot(ax=axes[0,1]); axes[0,1].set_title('B')
   df['C'].plot(ax=axes[1,0]); axes[1,0].set_title('C')

   @savefig series_plot_multi.png width=4.5in
   df['D'].plot(ax=axes[1,1]); axes[1,1].set_title('D')

Other plotting features

Bar plots

For labeled, non-time series data, you may wish to produce a bar plot:

.. ipython:: python

   plt.figure();

   @savefig bar_plot_ex.png width=4.5in
   df.ix[5].plot(kind='bar'); plt.axhline(0, color='k')

Calling a DataFrame's plot method with kind='bar' produces a multiple bar plot:

.. ipython:: python
   :suppress:

   plt.figure();


.. ipython:: python

   df2 = DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

   @savefig bar_plot_multi_ex.png width=5in
   df2.plot(kind='bar');

To produce a stacked bar plot, pass stacked=True:

.. ipython:: python
   :suppress:

   plt.figure();


.. ipython:: python

   @savefig bar_plot_stacked_ex.png width=5in
   df2.plot(kind='bar', stacked=True);

To get horizontal bar plots, pass kind='barh':

.. ipython:: python
   :suppress:

   plt.figure();


.. ipython:: python

   @savefig barh_plot_stacked_ex.png width=5in
   df2.plot(kind='barh', stacked=True);

Histograms

.. ipython:: python

   plt.figure();

   @savefig hist_plot_ex.png width=4.5in
   df['A'].diff().hist()

For a DataFrame, hist plots the histograms of the columns on multiple subplots:

.. ipython:: python

   plt.figure()

   @savefig frame_hist_ex.png width=4.5in
   df.diff().hist(color='k', alpha=0.5, bins=50)

Box-Plotting

DataFrame has a boxplot method which allows you to visualize the distribution of values within each column.

For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).

.. ipython:: python

   df = DataFrame(np.random.rand(10,5))
   plt.figure();

   @savefig box_plot_ex.png width=4.5in
   bp = df.boxplot()

You can create a stratified boxplot using the by keyword argument to create groupings. For instance,

.. ipython:: python

   df = DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )
   df['X'] = Series(['A','A','A','A','A','B','B','B','B','B'])

   plt.figure();

   @savefig box_plot_ex2.png width=4.5in
   bp = df.boxplot(by='X')

You can also pass a subset of columns to plot, as well as group by multiple columns:

.. ipython:: python

   df = DataFrame(np.random.rand(10,3), columns=['Col1', 'Col2', 'Col3'])
   df['X'] = Series(['A','A','A','A','A','B','B','B','B','B'])
   df['Y'] = Series(['A','B','A','B','A','B','A','B','A','B'])

   plt.figure();

   @savefig box_plot_ex3.png width=4.5in
   bp = df.boxplot(column=['Col1','Col2'], by=['X','Y'])

Scatter plot matrix

New in 0.7.3. You can create a scatter plot matrix using the
scatter_matrix method in pandas.tools.plotting:
.. ipython:: python

   from pandas.tools.plotting import scatter_matrix
   df = DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])

   @savefig scatter_matrix_ex.png width=6in
   scatter_matrix(df, alpha=0.2, figsize=(8, 8))