Let's start by just looking at an example of a  NumPy array:

A=array( [[ 1., 2., 3.],
          [4., 5., 6.]])

It is displayed as:

[[1. 2. 3.]
 [4. 5. 6.]]

And its elements are accessed by using indexes generated simply by counting rows and columns, for example, A[0,1].

This matrix can be converted to the pandas datatype DataFrame by keeping the same data and order but representing and accessing it in a different way:

import pandas as pd
A=array( [[ 1., 2., 3.],
          [ 4., 5., 6.]] )
AF = pd.DataFrame(A)

This DataFrame object, which we will explain in more detail in this chapter, is displayed as

      0   1  2
 0   1.0 2.0 3.0
 1   4.0 5.0 6.0

We see that a pandas dataframe has extra labels for the rows and columns called index and columns. These are the metadata of a dataframe.

Here, they coincide with NumPy's indexing, but that is not always so. The index and columns metadata allows the pandas dataframe to...