Python Data Science Handbook, 2nd Edition

Chapter 13. Introducing Pandas Objects

At a very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices. As we will see during the course of this chapter, Pandas provides a host of useful tools, methods, and functionality on top of the basic data structures, but nearly everything that follows will require an understanding of what these structures are. Thus, before we go any further, let’s take a look at these three fundamental Pandas data structures: the Series, DataFrame, and Index.

We will start our code sessions with the standard NumPy and Pandas imports:

In [1]: import numpy as np
        import pandas as pd

The Pandas Series Object

A Pandas Series is a one-dimensional array of indexed data. It can be created from a list or array as follows:

In [2]: data = pd.Series([0.25, 0.5, 0.75, 1.0])
        data
Out[2]: 0    0.25
        1    0.50
        2    0.75
        3    1.00
        dtype: float64

The Series combines a sequence of values with an explicit sequence of indices, which we can access with the values and index attributes. The values are simply a familiar NumPy array:

In [3]: data.values
Out[3]: array([0.25, 0.5 , 0.75, 1.  ])

The index is an array-like object of type pd.Index, which we’ll discuss in more detail momentarily:

In [4]: data.index
Out[4]: RangeIndex(start=0, stop=4, step=1)

Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation: ...