.. currentmodule:: pandas
.. _visualization:
.. 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
<http://matplotlib.sourceforge.net>`__ as time goes on.
We use the standard convention for referencing the matplotlib API:
.. ipython:: python
import matplotlib.pyplot as plt
.. _visualization.basic:
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=date_range('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 ``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 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=date_range('1/1/2000', periods=1000))
ts = np.exp(ts.cumsum())
@savefig series_plot_logy.png width=4.5in
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(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()
df3['A'] = Series(range(len(df)))
@savefig df_plot_xy.png width=4.5in
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 width=4.5in
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()
@savefig frame_plot_secondary_y.png width=4.5in
df.plot(secondary_y=['A', 'B'])
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.png width=4.5in
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 width=4.5in
df.A.plot()
Using the ``x_compat`` parameter, you can suppress this bevahior:
.. ipython:: python
plt.figure()
@savefig ser_plot_suppress_parm.png width=4.5in
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 width=4.5in
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
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')
.. _visualization.other:
Other plotting features
-----------------------
.. _visualization.barplot:
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)
.. _visualization.box:
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'])
.. _visualization.scatter_matrix:
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_kde.png width=6in
scatter_matrix(df, alpha=0.2, figsize=(8, 8), diagonal='kde')
.. _visualization.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(np.random.randn(1000))
@savefig kde_plot.png width=6in
ser.plot(kind='kde')
.. _visualization.andrews_curves:
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 <https://raw.github.com/pydata/pandas/master/pandas/tests/data/iris.csv>`__.
.. 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 width=6in
andrews_curves(data, 'Name')
.. _visualization.parallel_coordinates:
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 width=6in
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 * np.random.random(1000) +
0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
@savefig lag_plot.png width=6in
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 * np.random.random(1000) +
0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))
@savefig autocorrelation_plot.png width=6in
autocorrelation_plot(data)
.. _visualization.bootstrap:
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(np.random.random(1000))
@savefig bootstrap_plot.png width=8in
bootstrap_plot(data, size=50, samples=500, color='grey')
.. _visualization.radviz:
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 <https://raw.github.com/pydata/pandas/master/pandas/tests/data/iris.csv>`__.
.. ipython:: python
from pandas import read_csv
from pandas.tools.plotting import radviz
data = read_csv('data/iris.data')
plt.figure()
@savefig radviz.png width=6in
radviz(data, 'Name')