- 5.1 Introduction
- 5.2 Lists
- 5.3 Tuples
- 5.4 Unpacking Sequences
- 5.5 Sequence Slicing
- 5.6 del Statement
- 5.7 Passing Lists to Functions
- 5.8 Sorting Lists
- 5.9 Searching Sequences
- 5.10 Other List Methods
- 5.11 Simulating Stacks with Lists
- 5.12 List Comprehensions
- 5.13 Generator Expressions
- 5.14 Filter, Map and Reduce
- 5.15 Other Sequence Processing Functions
- 5.16 Two-Dimensional Lists
- 5.17 Intro to Data Science: Simulation and Static Visualizations
- 5.18 Wrap-Up
This chapter presented more details of the list and tuple sequences. You created lists, accessed their elements and determined their length. You saw that lists are mutable, so you can modify their contents, including growing and shrinking the lists as your programs execute. You saw that accessing a nonexistent element causes an IndexError. You used for statements to iterate through list elements.
We discussed tuples, which like lists are sequences, but are immutable. You unpacked a tuple’s elements into separate variables. You used enumerate to create an iterable of tuples, each with a list index and corresponding element value.
You learned that all sequences support slicing, which creates new sequences with subsets of the original elements. You used the del statement to remove elements from lists and delete variables from interactive sessions. We passed lists, list elements and slices of lists to functions. You saw how to search and sort lists, and how to search tuples. We used list methods to insert, append and remove elements, and to reverse a list’s elements and copy lists.
We showed how to simulate stacks with lists. We used the concise list-comprehension notation to create new lists. We used additional built-in methods to sum list elements, iterate backward through a list, find the minimum and maximum values, filter values and map values to new values. We showed how nested lists can represent two-dimensional tables in which data is arranged in rows and columns. You saw how nested for loops process two-dimensional lists.
The chapter concluded with an Intro to Data Science section that presented a die-rolling simulation and static visualizations. A detailed code example used the Seaborn and Matplotlib visualization libraries to create a static bar plot visualization of the simulation’s final results. In the next Intro to Data Science section, we use a die-rolling simulation with a dynamic bar plot visualization to make the plot “come alive.”
In the next chapter, “Dictionaries and Sets,” we’ll continue our discussion of Python’s built-in collections. We’ll use dictionaries to store unordered collections of key–value pairs that map immutable keys to values, just as a conventional dictionary maps words to definitions. We’ll use sets to store unordered collections of unique elements.
In the “Array-Oriented Programming with NumPy” chapter, we’ll discuss NumPy’s ndarray collection in more detail. You’ll see that while lists are fine for small amounts of data, they are not efficient for the large amounts of data you’ll encounter in big data analytics applications. For such cases, the NumPy library’s highly optimized ndarray collection should be used. ndarray (n-dimensional array) can be much faster than lists. We’ll run Python profiling tests to see just how much faster. As you’ll see, NumPy also includes many capabilities for conveniently and efficiently manipulating arrays of many dimensions. In big data analytics applications, the processing demands can be humongous, so everything we can do to improve performance significantly matters. In our “Big Data: Hadoop, Spark, NoSQL and IoT” chapter, you’ll use one of the most popular high-performance big-data databases—MongoDB.2