Python Data Science Handbook, 2nd Edition

Chapter 14. Data Indexing and Selection

In Part II, we looked in detail at methods and tools to access, set, and modify values in NumPy arrays. These included indexing (e.g., arr[2, 1]), slicing (e.g., arr[:, 1:5]), masking (e.g., arr[arr > 0]), fancy indexing (e.g., arr[0, [1, 5]]), and combinations thereof (e.g., arr[:, [1, 5]]). Here we’ll look at similar means of accessing and modifying values in Pandas Series and DataFrame objects. If you have used the NumPy patterns, the corresponding patterns in Pandas will feel very familiar, though there are a few quirks to be aware of.

We’ll start with the simple case of the one-dimensional Series object, and then move on to the more complicated two-dimensional DataFrame object.

Data Selection in Series

As you saw in the previous chapter, a Series object acts in many ways like a one-dimensional NumPy array, and in many ways like a standard Python dictionary. If you keep these two overlapping analogies in mind, it will help you understand the patterns of data indexing and selection in these arrays.

Series as Dictionary

Like a dictionary, the Series object provides a mapping from a collection of keys to a collection of values:

In [1]: import pandas as pd
        data = pd.Series([0.25, 0.5, 0.75, 1.0],
                         index=['a', 'b', 'c', 'd'])
        data
Out[1]: a    0.25
        b    0.50
        c    0.75
        d    1.00
        dtype: float64

In [2]: data['b']
Out[2]: 0.5

We can also use dictionary-like Python expressions and methods to examine the keys/indices and values:

In [3]: 'a' in data
Out[