Another nifty function of pandas is the describe function that gives us a summary statistics of every value inside each column of the Pandas DataFrame object:
>>> print data.describe() EUROSTOXX VSTOXX count 4072.000000 4048.000000 mean 3254.538183 25.305428 std 793.191950 9.924404 min 1809.980000 11.596600 25% 2662.460000 18.429500 50% 3033.880000 23.168600 75% 3753.542500 28.409550 max 5464.430000 87.512700 [8 rows x 2 columns]
Pandas allows the values in the DataFrame object to be visualized as a graph using the plot function. Let's plot the EURO STOXX 50 and VSTOX to see how they look like over the years:
>>> from pylab import * >>> data.plot(subplots=True, ... figsize=(10, 8), ... color="blue", ... grid=True) >>> show() Populating the interactive namespace from numpy and matplotlib array([<matplotlib.axes.AxesSubplot...