Visualization

We use the standard convention for referencing the matplotlib API:

In [1]: import matplotlib.pyplot as plt

In [2]: plt.close('all')

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 [3]: ts = pd.Series(np.random.randn(1000),
   ...:                index=pd.date_range('1/1/2000', periods=1000))
   ...: 

In [4]: ts = ts.cumsum()

In [5]: ts.plot()
Out[5]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b22f16550>

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

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

In [8]: plt.figure();

In [9]: df.plot();

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

In [10]: df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()

In [11]: df3['A'] = pd.Series(list(range(len(df))))

In [12]: df3.plot(x='A', y='B')
Out[12]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b29a8af98>

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 [13]: plt.figure();

In [14]: 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 [15]: df = pd.DataFrame()

In [16]: df.plot.<TAB>  # noqa: E225, E999
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 [17]: plt.figure();

In [18]: df.iloc[5].plot.bar()
Out[18]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b2830d048>

In [19]: plt.axhline(0, color='k');

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

In [20]: df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

In [21]: df2.plot.bar();

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

In [22]: df2.plot.bar(stacked=True);

To get horizontal bar plots, use the barh method:

In [23]: df2.plot.barh(stacked=True);

Histograms

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

In [24]: 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 [25]: plt.figure();

In [26]: df4.plot.hist(alpha=0.5)
Out[26]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b23e905f8>

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

In [27]: plt.figure();

In [28]: df4.plot.hist(stacked=True, bins=20)
Out[28]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b23ef2208>

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 [29]: plt.figure();

In [30]: df4['a'].plot.hist(orientation='horizontal', cumulative=True)
Out[30]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b280a0978>

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

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

In [31]: plt.figure();

In [32]: df['A'].diff().hist()
Out[32]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b222aab00>

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

In [33]: plt.figure()
Out[33]: <Figure size 640x480 with 0 Axes>

In [34]: df.diff().hist(color='k', alpha=0.5, bins=50)