Visualization

We use the standard convention for referencing the matplotlib API:

In [1]: import matplotlib.pyplot as plt

We provide the basics in pandas to easily create decent looking plots. See the ecosystem section for visualization libraries that go beyond the basics documented here.

Basic Plotting: plot

We will demonstrate the basics, 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 = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

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

In [4]: ts.plot()
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x7f20d5690710>

If the index consists of dates, it calls gcf().autofmt_xdate() to try to format the x-axis nicely as per above.

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

In [5]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))

In [6]: df = df.cumsum()

In [7]: plt.figure(); df.plot();

You can plot one column versus another using the x and y keywords in plot():

In [8]: df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()

In [9]: df3['A'] = pd.Series(list(range(len(df))))

In [10]: df3.plot(x='A', y='B')
Out[10]: <matplotlib.axes._subplots.AxesSubplot at 0x7f20d5aabf28>

Other Plots

Plotting methods allow for a handful of plot styles other than the default line plot. These methods can be provided as the kind keyword argument to plot(), and include:

• ‘bar’ or ‘barh’ for bar plots
• ‘hist’ for histogram
• ‘box’ for boxplot
• ‘kde’ or ‘density’ for density plots
• ‘area’ for area plots
• ‘scatter’ for scatter plots
• ‘hexbin’ for hexagonal bin plots
• ‘pie’ for pie plots

For example, a bar plot can be created the following way:

In [11]: plt.figure();

In [12]: df.iloc[5].plot(kind='bar');

You can also create these other plots using the methods DataFrame.plot.<kind> instead of providing the kind keyword argument. This makes it easier to discover plot methods and the specific arguments they use:

In [13]: df = pd.DataFrame()

In [14]: df.plot.<TAB>
df.plot.area     df.plot.barh     df.plot.density  df.plot.hist     df.plot.line     df.plot.scatter
df.plot.bar      df.plot.box      df.plot.hexbin   df.plot.kde      df.plot.pie

In addition to these kind s, there are the DataFrame.hist(), and DataFrame.boxplot() methods, which use a separate interface.

Finally, there are several plotting functions in pandas.plotting that take a Series or DataFrame as an argument. These include:

• Scatter Matrix
• Andrews Curves
• Parallel Coordinates
• Lag Plot
• Autocorrelation Plot
• Bootstrap Plot
• RadViz

Plots may also be adorned with errorbars or tables.

Bar plots

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

In [15]: plt.figure();

In [16]: df.iloc[5].plot.bar(); plt.axhline(0, color='k')
Out[16]: <matplotlib.lines.Line2D at 0x7f20d46a4358>

Calling a DataFrame’s plot.bar() method produces a multiple bar plot:

In [17]: df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

In [18]: df2.plot.bar();

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

In [19]: df2.plot.bar(stacked=True);

To get horizontal bar plots, use the barh method:

In [20]: df2.plot.barh(stacked=True);

Histograms

Histograms can be drawn by using the DataFrame.plot.hist() and Series.plot.hist() methods.

In [21]: df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
   ....:                     'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
   ....: 

In [22]: plt.figure();

In [23]: df4.plot.hist(alpha=0.5)
Out[23]: <matplotlib.axes._subplots.AxesSubplot at 0x7f20cf918908>

A histogram can be stacked using stacked=True. Bin size can be changed using the bins keyword.

In [24]: plt.figure();

In [25]: df4.plot.hist(stacked=True, bins=20)
Out[25]: <matplotlib.axes._subplots.AxesSubplot at 0x7f20cf909a58>

You can pass other keywords supported by matplotlib hist. For example, horizontal and cumulative histograms can be drawn by orientation='horizontal' and cumulative=True.

In [26]: plt.figure();

In [27]: df4['a'].plot.hist(orientation='horizontal', cumulative=True)
Out[27]: <matplotlib.axes._subplots.AxesSubplot at 0x7f20cfbceba8>

See the hist method and the matplotlib hist documentation for more.

The existing interface DataFrame.hist to plot histogram still can be used.

In [28]: plt.figure();

In [29]: df['A'].diff().hist()
Out[29]: <matplotlib.axes._subplots.AxesSubplot at 0x7f20d550efd0>

DataFrame.hist() plots the histograms of the columns on multiple subplots:

In [30]: plt.figure()
Out[30]: <Figure size 640x480 with 0 Axes>

In [31]: df.diff().hist(color='k', alpha=0.5, bins=50)