Computational and graphics tools

The objects of pandas have a rich set of built-in computational tools. To illustrate some of this functionality, we will use the random data stored in the dframe object defined in the previous section. If you discarded that object, here is how to construct it again:

means = [0, 0, 1, 1, -1, -1, -2, -2]
sdevs = [1, 2, 1, 2,  1,  2,  1,  2]
random_data = {}
nrows = 30
for mean, sdev in zip(means, sdevs):
    label = 'Mean={}, sd={}'.format(mean, sdev)
    random_data[label] = normal(mean, sdev, nrows)
row_labels = ['Row {}'.format(i) for i in range(nrows)]
dframe = DataFrame (random_data, index=row_labels)

Let's explore some of this functionality of the built-in computational tools.

• To get a list of the methods available for the object, start typing the following command in a cell:

  dframe.
  

• Then, press the Tab key. The completion popup allows us to select a method by double clicking on it. For example, double click on mean. The cell text...