visualization.rst

.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   from numpy.random import randn, rand, randint
   np.random.seed(123456)
   from pandas import DataFrame, Series, date_range, options
   import pandas.util.testing as tm
   np.set_printoptions(precision=4, suppress=True)
   import matplotlib.pyplot as plt
   plt.close('all')
   options.display.mpl_style = 'default'
   options.display.max_rows = 15
   from pandas.compat import lrange

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

The display.mpl_style option was added in 0.11.0, to produce more appealing plots. When set, matplotlib's rcParams are changed (globally!) to nicer-looking settings. All the plots in the documentation are rendered with this option set to the 'default' style.

.. ipython:: python

   pd.options.display.mpl_style = 'default'

Basic plotting: plot

See the :ref:`cookbook<cookbook.plotting>` for some advanced strategies

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

.. ipython:: python

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

   @savefig series_plot_basic.png
   ts.plot()

If the index consists of dates, it calls gcf().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
   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=list('ABCD'))
   df = df.cumsum()

   @savefig frame_plot_basic.png
   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
   df.plot(legend=False)

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

.. ipython:: python

   @savefig frame_plot_subplots.png
   df.plot(subplots=True, figsize=(6, 6)); plt.legend(loc='best')

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

.. ipython:: python

   plt.figure();

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

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

You can plot one column versus another using the x and y keywords in DataFrame.plot:

.. ipython:: python

   plt.figure()

   df3 = DataFrame(randn(1000, 2), columns=['B', 'C']).cumsum()
   df3['A'] = Series(list(range(len(df))))

   @savefig df_plot_xy.png
   df3.plot(x='A', y='B')

Plotting on a Secondary Y-axis

To plot data on a secondary y-axis, use the secondary_y keyword:

.. ipython:: python

   plt.figure()

   df.A.plot()

   @savefig series_plot_secondary_y.png
   df.B.plot(secondary_y=True, style='g')

Selective Plotting on Secondary Y-axis

To plot some columns in a DataFrame, give the column names to the secondary_y keyword:

.. ipython:: python

   plt.figure()
   ax = df.plot(secondary_y=['A', 'B'])
   ax.set_ylabel('CD scale')
   @savefig frame_plot_secondary_y.png
   ax.right_ax.set_ylabel('AB scale')

Note that the columns plotted on the secondary y-axis is automatically marked with "(right)" in the legend. To turn off the automatic marking, use the mark_right=False keyword:

.. ipython:: python

   plt.figure()

   @savefig frame_plot_secondary_y_no_right.png
   df.plot(secondary_y=['A', 'B'], mark_right=False)

Suppressing tick resolution adjustment

Pandas includes automatically tick resolution adjustment for regular frequency time-series data. For limited cases where pandas cannot infer the frequency information (e.g., in an externally created twinx), you can choose to suppress this behavior for alignment purposes.

Here is the default behavior, notice how the x-axis tick labelling is performed:

.. ipython:: python

   plt.figure()

   @savefig ser_plot_suppress.png
   df.A.plot()

Using the x_compat parameter, you can suppress this behavior:

.. ipython:: python

   plt.figure()

   @savefig ser_plot_suppress_parm.png
   df.A.plot(x_compat=True)

If you have more than one plot that needs to be suppressed, the use method in pandas.plot_params can be used in a with statement:

.. ipython:: python

   import pandas as pd

   plt.figure()

   @savefig ser_plot_suppress_context.png
   with pd.plot_params.use('x_compat', True):
       df.A.plot(color='r')
       df.B.plot(color='g')
       df.C.plot(color='b')

Targeting different subplots

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

.. ipython:: python
   :suppress:

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

   df = DataFrame(randn(1000, 4), index=ts.index, columns=list('ABCD'))
   df = df.cumsum()

.. ipython:: python

   fig, axes = plt.subplots(nrows=2, ncols=2)
   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
   df['D'].plot(ax=axes[1,1]); axes[1,1].set_title('D')

.. ipython:: python
   :suppress:

    plt.close('all')

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
   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(rand(10, 4), columns=['a', 'b', 'c', 'd'])

   @savefig bar_plot_multi_ex.png
   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
   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
   df2.plot(kind='barh', stacked=True);

Histograms

.. ipython:: python

   plt.figure();

   @savefig hist_plot_ex.png
   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
   df.diff().hist(color='k', alpha=0.5, bins=50)

New since 0.10.0, the by keyword can be specified to plot grouped histograms:

.. ipython:: python
   :suppress:

   plt.figure()

.. ipython:: python

   data = Series(randn(1000))

   @savefig grouped_hist.png
   data.hist(by=randint(0, 4, 1000), figsize=(6, 4))

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(rand(10,5))
   plt.figure();

   @savefig box_plot_ex.png
   bp = df.boxplot()

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

.. ipython:: python

   df = DataFrame(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
   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(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
   bp = df.boxplot(column=['Col1','Col2'], by=['X','Y'])

.. ipython:: python
   :suppress:

    plt.close('all')

Plotting With Error Bars

Plotting with error bars is now supported in the .plot method of DataFrame and Series objects.

x and y errorbars are supported and be supplied using the xerr and yerr keyword arguments to .plot() The error values can be specified using a variety of formats.

• As a DataFrame or dict of errors with column names matching the columns attribute of the plotting DataFrame or matching the name attribute of the Series
• As a str indicating which of the columns of plotting DataFrame contain the error values
• As raw values (list, tuple, or np.ndarray). Must be the same length as the plotting DataFrame/Series

Asymmetrical error bars are also supported, however raw error values must be provided in this case. For a M length Series, a Mx2 array should be provided indicating lower and upper (or left and right) errors. For a MxN DataFrame, asymmetrical errors should be in a Mx2xN array.

Here is an example of one way to easily plot group means with standard deviations from the raw data.

.. ipython:: python

   # Generate the data
   ix3 = pd.MultiIndex.from_arrays([['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'], ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']], names=['letter', 'word'])
   df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2], 'data2': [6, 5, 7, 5, 4, 5, 6, 5]}, index=ix3)

   # Group by index labels and take the means and standard deviations for each group
   gp3 = df3.groupby(level=('letter', 'word'))
   means = gp3.mean()
   errors = gp3.std()
   means
   errors

   # Plot
   fig, ax = plt.subplots()
   @savefig errorbar_example.png
   means.plot(yerr=errors, ax=ax, kind='bar')

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(randn(1000, 4), columns=['a', 'b', 'c', 'd'])

   @savefig scatter_matrix_kde.png
   scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')

New in 0.8.0 You can create density plots using the Series/DataFrame.plot and setting kind='kde':

.. ipython:: python
   :suppress:

   plt.figure()

.. ipython:: python

   ser = Series(randn(1000))

   @savefig kde_plot.png
   ser.plot(kind='kde')

Hexagonal Bin plot

New in .14 You can create hexagonal bin plots with DataFrame.plot and kind='hexbin'. Hexbin plots can be a useful alternative to scatter plots if your data are too dense to plot each point individually.

.. ipython:: python
   :suppress:

   plt.figure()

.. ipython:: python

   df = DataFrame(randn(1000, 2), columns=['a', 'b'])
   df['b'] = df['b'] = df['b'] + np.arange(1000)

   @savefig hexbin_plot.png
   df.plot(kind='hexbin', x='a', y='b', gridsize=25)

A useful keyword argument is gridsize; it controls the number of hexagons in the x-direction, and defaults to 100. A larger gridsize means more, smaller bins.

By default, a histogram of the counts around each (x, y) point is computed. You can specify alternative aggregations by passing values to the C and reduce_C_function arguments. C specifies the value at each (x, y) point and reduce_C_function is a function of one argument that reduces all the values in a bin to a single number (e.g. mean, max, sum, std). In this example the positions are given by columns a and b, while the value is given by column z. The bins are aggregated with numpy's max function.

.. ipython:: python
   :suppress:

   plt.figure()

.. ipython:: python

   df = DataFrame(randn(1000, 2), columns=['a', 'b'])
   df['b'] = df['b'] = df['b'] + np.arange(1000)
   df['z'] = np.random.uniform(0, 3, 1000)

   @savefig hexbin_plot_agg.png
   df.plot(kind='hexbin', x='a', y='b', C='z', reduce_C_function=np.max,
           gridsize=25)

See the matplotlib hexbin documenation for more.

Andrews Curves

Andrews curves allow one to plot multivariate data as a large number of curves that are created using the attributes of samples as coefficients for Fourier series. By coloring these curves differently for each class it is possible to visualize data clustering. Curves belonging to samples of the same class will usually be closer together and form larger structures.

Note: The "Iris" dataset is available here.

.. ipython:: python

   from pandas import read_csv
   from pandas.tools.plotting import andrews_curves

   data = read_csv('data/iris.data')

   plt.figure()

   @savefig andrews_curves.png
   andrews_curves(data, 'Name')

Parallel Coordinates

Parallel coordinates is a plotting technique for plotting multivariate data. It allows one to see clusters in data and to estimate other statistics visually. Using parallel coordinates points are represented as connected line segments. Each vertical line represents one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together.

.. ipython:: python

   from pandas import read_csv
   from pandas.tools.plotting import parallel_coordinates

   data = read_csv('data/iris.data')

   plt.figure()

   @savefig parallel_coordinates.png
   parallel_coordinates(data, 'Name')

Lag Plot

Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random.

.. ipython:: python

   from pandas.tools.plotting import lag_plot

   plt.figure()

   data = Series(0.1 * rand(1000) +
      0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))

   @savefig lag_plot.png
   lag_plot(data)

Autocorrelation Plot

Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags. If time series is random, such autocorrelations should be near zero for any and all time-lag separations. If time series is non-random then one or more of the autocorrelations will be significantly non-zero. The horizontal lines displayed in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band.

.. ipython:: python

   from pandas.tools.plotting import autocorrelation_plot

   plt.figure()

   data = Series(0.7 * rand(1000) +
      0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))

   @savefig autocorrelation_plot.png
   autocorrelation_plot(data)

Bootstrap Plot

Bootstrap plots are used to visually assess the uncertainty of a statistic, such as mean, median, midrange, etc. A random subset of a specified size is selected from a data set, the statistic in question is computed for this subset and the process is repeated a specified number of times. Resulting plots and histograms are what constitutes the bootstrap plot.

.. ipython:: python

   from pandas.tools.plotting import bootstrap_plot

   data = Series(rand(1000))

   @savefig bootstrap_plot.png
   bootstrap_plot(data, size=50, samples=500, color='grey')

.. ipython:: python
   :suppress:

    plt.close('all')

RadViz

RadViz is a way of visualizing multi-variate data. It is based on a simple spring tension minimization algorithm. Basically you set up a bunch of points in a plane. In our case they are equally spaced on a unit circle. Each point represents a single attribute. You then pretend that each sample in the data set is attached to each of these points by a spring, the stiffness of which is proportional to the numerical value of that attribute (they are normalized to unit interval). The point in the plane, where our sample settles to (where the forces acting on our sample are at an equilibrium) is where a dot representing our sample will be drawn. Depending on which class that sample belongs it will be colored differently.

Note: The "Iris" dataset is available here.

.. ipython:: python

   from pandas import read_csv
   from pandas.tools.plotting import radviz

   data = read_csv('data/iris.data')

   plt.figure()

   @savefig radviz.png
   radviz(data, 'Name')

Colormaps

A potential issue when plotting a large number of columns is that it can be difficult to distinguish some series due to repetition in the default colors. To remedy this, DataFrame plotting supports the use of the colormap= argument, which accepts either a Matplotlib colormap or a string that is a name of a colormap registered with Matplotlib. A visualization of the default matplotlib colormaps is available here.

As matplotlib does not directly support colormaps for line-based plots, the colors are selected based on an even spacing determined by the number of columns in the DataFrame. There is no consideration made for background color, so some colormaps will produce lines that are not easily visible.

To use the jet colormap, we can simply pass 'jet' to colormap=

.. ipython:: python

   df = DataFrame(randn(1000, 10), index=ts.index)
   df = df.cumsum()

   plt.figure()

   @savefig jet.png
   df.plot(colormap='jet')

or we can pass the colormap itself

.. ipython:: python

   from matplotlib import cm

   plt.figure()

   @savefig jet_cm.png
   df.plot(colormap=cm.jet)

Colormaps can also be used other plot types, like bar charts:

.. ipython:: python

   dd = DataFrame(randn(10, 10)).applymap(abs)
   dd = dd.cumsum()

   plt.figure()

   @savefig greens.png
   dd.plot(kind='bar', colormap='Greens')

Parallel coordinates charts:

.. ipython:: python

   plt.figure()

   @savefig parallel_gist_rainbow.png
   parallel_coordinates(data, 'Name', colormap='gist_rainbow')

Andrews curves charts:

.. ipython:: python

   plt.figure()

   @savefig andrews_curve_winter.png
   andrews_curves(data, 'Name', colormap='winter')

Plotting directly with matplotlib

In some situations it may still be preferable or necessary to prepare plots directly with matplotlib, for instance when a certain type of plot or customization is not (yet) supported by pandas. Series and DataFrame objects behave like arrays and can therefore be passed directly to matplotlib functions without explicit casts.

Pandas also automatically registers formatters and locators that recognize date indices, thereby extending date and time support to practically all plot types available in matplotlib. Although this formatting does not provide the same level of refinement you would get when plotting via pandas, it can be faster when plotting a large number of points.

Note

The speed up for large data sets only applies to pandas 0.14.0 and later.

.. ipython:: python

   price = Series(randn(150).cumsum(),
                  index=date_range('2000-1-1', periods=150, freq='B'))
   ma = pd.rolling_mean(price, 20)
   mstd = pd.rolling_std(price, 20)

   plt.figure()

   plt.plot(price.index, price, 'k')
   plt.plot(ma.index, ma, 'b')
   @savefig bollinger.png
   plt.fill_between(mstd.index, ma-2*mstd, ma+2*mstd, color='b', alpha=0.2)

.. ipython:: python
   :suppress:

    plt.close('all')