This section provides a brief introduction to plotting in pandas using matplotlib. The matplotlib API is imported using the standard convention, as shown in the following command:

In [1]: import matplotlib.pyplot as plt 

Series and DataFrame have a plot method, which is simply a wrapper around plt.plot. Here, we will examine how we can do a simple plot of a sine and cosine function. Suppose we wished to plot the following functions over the interval pi to pi:

• f(x) = cos(x) + sin (x)
• g(x) = cos (x) - sin (x)

This gives the following interval:

    In [51]: import numpy as np
    In [52]: X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
    
    In [54]: f,g = np.cos(X)+np.sin(X), np.sin(X)-np.cos(X)
    In [61]: f_ser=pd.Series(f)
             g_ser=pd.Series(g)
    
    
    In [31]: plotDF=pd.concat([f_ser,g_ser],axis=1)
             plotDF.index=X
             plotDF.columns=['sin(x)+cos(x)','sin(x)-cos(x)']
             plotDF.head()
    Out[31]:  sin(x)+cos(x)  sin(x)-cos(x)
    -3.141593  -1.000000   1.000000
    -3.116953  -1.024334   0.975059
    -3.092313  -1.048046   0.949526
    -3.067673  -1.071122   0.923417
    -3.043033  -1.093547   0.896747
    5 rows × 2 columns

We can now plot the DataFrame using the plot() command and the plt.show() command to display it:

    In [94]: plotDF.plot()
             plt.show()
    
    We can apply a title to the plot as follows:
    In [95]: plotDF.columns=['f(x)','g(x)']
             plotDF.plot(title='Plot of f(x)=sin(x)+cos(x), 
         g(x)=sinx(x)-cos(x)')
             plt.show()

The following is the output of the preceding command:

We can also plot the two series (functions) separately in different subplots, using the following command:

In [96]: plotDF.plot(subplots=True, figsize=(6,6))
           plt.show()

The following is the output of the preceding command:

There is a lot more to using the plotting functionality of matplotlib within pandas. For more information, take a look at the documentation at http://pandas.pydata.org/pandas-docs/dev/visualization.html.

It is often quite useful to visualize all the variables of a multivariate time series data. Let's plot all the variables of the following data in a single plot. Note that the date column is the index here:

The subplot feature in matplotlib lets us plot all of the variables at once:

    axes = plot_ts.plot(figsize=(20,10), title='Timeseries Plot', subplots=True, layout=(plot_ts.shape[1],1), xticks = plot_ts.index)
    # Get current axis from plot
    ax = plt.gca()
    import matplotlib.dates as mdates
    # Set frequency of xticks
    ax.xaxis.set_major_locator(mdates.DayLocator(interval = 1))
    # Format dates in xticks 
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    plt.show()

The following is the output: