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:

In [1]: import matplotlib.pyplot as plt

Basic plotting: plot

See the cookbook for some advanced strategies

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

In [2]: ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))

In [3]: ts = ts.cumsum()

In [4]: ts.plot()
<matplotlib.axes.AxesSubplot at 0x112ff8150>

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:

In [5]: plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()
<matplotlib.legend.Legend at 0x117c41590>

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

In [6]: df = DataFrame(randn(1000, 4), index=ts.index, columns=list('ABCD'))

In [7]: df = df.cumsum()

In [8]: plt.figure(); df.plot(); plt.legend(loc='best')
<matplotlib.legend.Legend at 0x117c6bb10>

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

In [9]: df.plot(legend=False)
<matplotlib.axes.AxesSubplot at 0x118243d90>

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

In [10]: df.plot(subplots=True, figsize=(6, 6)); plt.legend(loc='best')
<matplotlib.legend.Legend at 0x11563ead0>

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

In [11]: plt.figure();
In [11]: ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))

In [12]: ts = np.exp(ts.cumsum())

In [13]: ts.plot(logy=True)
<matplotlib.axes.AxesSubplot at 0x118c94090>

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

In [14]: plt.figure()
<matplotlib.figure.Figure at 0x118c98d50>

In [15]: df3 = DataFrame(randn(1000, 2), columns=['B', 'C']).cumsum()

In [16]: df3['A'] = Series(range(len(df)))

In [17]: df3.plot(x='A', y='B')
<matplotlib.axes.AxesSubplot at 0x118cc9d90>

Plotting on a Secondary Y-axis

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

In [18]: plt.figure()
<matplotlib.figure.Figure at 0x118c84c50>

In [19]: df.A.plot()
<matplotlib.axes.AxesSubplot at 0x118c7e0d0>

In [20]: df.B.plot(secondary_y=True, style='g')
<matplotlib.axes.AxesSubplot at 0x118dfec90>

Selective Plotting on Secondary Y-axis

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

In [21]: plt.figure()
<matplotlib.figure.Figure at 0x118c851d0>

In [22]: ax = df.plot(secondary_y=['A', 'B'])

In [23]: ax.set_ylabel('CD scale')
<matplotlib.text.Text at 0x114ccaad0>

In [24]: ax.right_ax.set_ylabel('AB scale')
<matplotlib.text.Text at 0x117ee8f90>

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:

In [25]: plt.figure()
<matplotlib.figure.Figure at 0x118ddf750>

In [26]: df.plot(secondary_y=['A', 'B'], mark_right=False)
<matplotlib.axes.AxesSubplot at 0x1194b4090>

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:

In [27]: plt.figure()
<matplotlib.figure.Figure at 0x1194b0b50>

In [28]: df.A.plot()
<matplotlib.axes.AxesSubplot at 0x1185f9250>

Using the x_compat parameter, you can suppress this behavior:

In [29]: plt.figure()
<matplotlib.figure.Figure at 0x117efdf10>

In [30]: df.A.plot(x_compat=True)
<matplotlib.axes.AxesSubplot at 0x118c12e10>

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:

In [31]: import pandas as pd

In [32]: plt.figure()
<matplotlib.figure.Figure at 0x118a73110>

In [33]: 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:

In [34]: fig, axes = plt.subplots(nrows=2, ncols=2)

In [35]: df['A'].plot(ax=axes[0,0]); axes[0,0].set_title('A')
<matplotlib.text.Text at 0x118628ed0>

In [36]: df['B'].plot(ax=axes[0,1]); axes[0,1].set_title('B')
<matplotlib.text.Text at 0x1191e0210>

In [37]: df['C'].plot(ax=axes[1,0]); axes[1,0].set_title('C')
<matplotlib.text.Text at 0x1191f5610>

In [38]: df['D'].plot(ax=axes[1,1]); axes[1,1].set_title('D')
<matplotlib.text.Text at 0x11920dad0>

Other plotting features

Bar plots

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

In [39]: plt.figure();
In [39]: df.ix[5].plot(kind='bar'); plt.axhline(0, color='k')
<matplotlib.lines.Line2D at 0x118c7d990>

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

In [40]: df2 = DataFrame(rand(10, 4), columns=['a', 'b', 'c', 'd'])

In [41]: df2.plot(kind='bar');

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

In [41]: df2.plot(kind='bar', stacked=True);

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

In [41]: df2.plot(kind='barh', stacked=True);

Histograms

In [41]: plt.figure();
In [41]: df['A'].diff().hist()
<matplotlib.axes.AxesSubplot at 0x11a3e27d0>

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

In [42]: plt.figure()
<matplotlib.figure.Figure at 0x11a3e1dd0>

In [43]: df.diff().hist(color='k', alpha=0.5, bins=50)

array([[<matplotlib.axes.AxesSubplot object at 0x11a4d3fd0>,
        <matplotlib.axes.AxesSubplot object at 0x11a5b5410>],
       [<matplotlib.axes.AxesSubplot object at 0x11a5d5c90>,
        <matplotlib.axes.AxesSubplot object at 0x11a5ecfd0>]], dtype=object)

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

In [44]: data = Series(randn(1000))

In [45]: data.hist(by=randint(0, 4, 1000))

array([[<matplotlib.axes.AxesSubplot object at 0x11a7323d0>,
        <matplotlib.axes.AxesSubplot object at 0x11a750310>],
       [<matplotlib.axes.AxesSubplot object at 0x11a4ca750>,
        <matplotlib.axes.AxesSubplot object at 0x11a847050>]], dtype=object)

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).

In [46]: df = DataFrame(rand(10,5))

In [47]: plt.figure();
In [47]: bp = df.boxplot()

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

In [48]: df = DataFrame(rand(10,2), columns=['Col1', 'Col2'] )

In [49]: df['X'] = Series(['A','A','A','A','A','B','B','B','B','B'])

In [50]: plt.figure();
In [50]: bp = df.boxplot(by='X')

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

In [51]: df = DataFrame(rand(10,3), columns=['Col1', 'Col2', 'Col3'])

In [52]: df['X'] = Series(['A','A','A','A','A','B','B','B','B','B'])

In [53]: df['Y'] = Series(['A','B','A','B','A','B','A','B','A','B'])

In [54]: plt.figure();
In [54]: 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:

In [55]: from pandas.tools.plotting import scatter_matrix

In [56]: df = DataFrame(randn(1000, 4), columns=['a', 'b', 'c', 'd'])

In [57]: scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')

array([[<matplotlib.axes.AxesSubplot object at 0x11aedc450>,
        <matplotlib.axes.AxesSubplot object at 0x11afcba10>,
        <matplotlib.axes.AxesSubplot object at 0x11aeea0d0>,
        <matplotlib.axes.AxesSubplot object at 0x11b001d10>],
       [<matplotlib.axes.AxesSubplot object at 0x11b023350>,
        <matplotlib.axes.AxesSubplot object at 0x11b00bad0>,
        <matplotlib.axes.AxesSubplot object at 0x11b058bd0>,
        <matplotlib.axes.AxesSubplot object at 0x11b07b210>],
       [<matplotlib.axes.AxesSubplot object at 0x11b062ad0>,
        <matplotlib.axes.AxesSubplot object at 0x11b0ada50>,
        <matplotlib.axes.AxesSubplot object at 0x11b0d1090>,
        <matplotlib.axes.AxesSubplot object at 0x11b0b91d0>],
       [<matplotlib.axes.AxesSubplot object at 0x11b104990>,
        <matplotlib.axes.AxesSubplot object at 0x11b124ed0>,
        <matplotlib.axes.AxesSubplot object at 0x11b1118d0>,
        <matplotlib.axes.AxesSubplot object at 0x11b15c910>]], dtype=object)

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

In [58]: ser = Series(randn(1000))

In [59]: ser.plot(kind='kde')
<matplotlib.axes.AxesSubplot at 0x11b249110>

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.

In [60]: from pandas import read_csv

In [61]: from pandas.tools.plotting import andrews_curves

In [62]: data = read_csv('data/iris.data')

In [63]: plt.figure()
<matplotlib.figure.Figure at 0x11b26fc90>

In [64]: andrews_curves(data, 'Name')
<matplotlib.axes.AxesSubplot at 0x11b287a50>

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.

In [65]: from pandas import read_csv

In [66]: from pandas.tools.plotting import parallel_coordinates

In [67]: data = read_csv('data/iris.data')

In [68]: plt.figure()
<matplotlib.figure.Figure at 0x11b278e10>

In [69]: parallel_coordinates(data, 'Name')
<matplotlib.axes.AxesSubplot at 0x1178fff90>

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.

In [70]: from pandas.tools.plotting import lag_plot

In [71]: plt.figure()
<matplotlib.figure.Figure at 0x11b512390>

In [72]: data = Series(0.1 * rand(1000) +
   ....:    0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
   ....: 

In [73]: lag_plot(data)
<matplotlib.axes.AxesSubplot at 0x11b535e10>

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.

In [74]: from pandas.tools.plotting import autocorrelation_plot

In [75]: plt.figure()
<matplotlib.figure.Figure at 0x11b54c450>

In [76]: data = Series(0.7 * rand(1000) +
   ....:    0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))
   ....: 

In [77]: autocorrelation_plot(data)
<matplotlib.axes.AxesSubplot at 0x11b54c310>

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.

In [78]: from pandas.tools.plotting import bootstrap_plot

In [79]: data = Series(rand(1000))

In [80]: bootstrap_plot(data, size=50, samples=500, color='grey')
<matplotlib.figure.Figure at 0x11b711ed0>

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.

In [81]: from pandas import read_csv

In [82]: from pandas.tools.plotting import radviz

In [83]: data = read_csv('data/iris.data')

In [84]: plt.figure()
<matplotlib.figure.Figure at 0x11b554350>

In [85]: radviz(data, 'Name')
<matplotlib.axes.AxesSubplot at 0x11b8df650>

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=

In [86]: df = DataFrame(randn(1000, 10), index=ts.index)

In [87]: df = df.cumsum()

In [88]: plt.figure()
<matplotlib.figure.Figure at 0x11b904610>

In [89]: df.plot(colormap='jet')
<matplotlib.axes.AxesSubplot at 0x11b911d90>

or we can pass the colormap itself

In [90]: from matplotlib import cm

In [91]: plt.figure()
<matplotlib.figure.Figure at 0x11baecf50>

In [92]: df.plot(colormap=cm.jet)
<matplotlib.axes.AxesSubplot at 0x11b911250>

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

In [93]: dd = DataFrame(randn(10, 10)).applymap(abs)

In [94]: dd = dd.cumsum()

In [95]: plt.figure()
<matplotlib.figure.Figure at 0x11bc06d90>

In [96]: dd.plot(kind='bar', colormap='Greens')
<matplotlib.axes.AxesSubplot at 0x11bc0e210>

Parallel coordinates charts:

In [97]: plt.figure()
<matplotlib.figure.Figure at 0x11bd5de10>

In [98]: parallel_coordinates(data, 'Name', colormap='gist_rainbow')
<matplotlib.axes.AxesSubplot at 0x11bd6d350>

Andrews curves charts:

In [99]: plt.figure()
<matplotlib.figure.Figure at 0x11bd4df50>

In [100]: andrews_curves(data, 'Name', colormap='winter')
<matplotlib.axes.AxesSubplot at 0x11bed40d0>