Gaddis Python 6e Chapter 07

Chapter 7: Lists and Tuples

Overview

  • Covered topics include Lists, Tuples, and the matplotlib Package for plotting data.

Sequences

  • Definition: An object that contains multiple items of data.

  • Storage: Items stored in sequence, one after another.

  • Types in Python: Lists (mutable) and Tuples (immutable).

Introduction to Lists

  • List: An object containing multiple data items.

  • Elements: Individual items in a list.

  • Format: list = [item1, item2, ...]

  • Types of Items: Can hold different data types.

  • Display: Use the print function to display a list.

  • Conversion: The list() function converts certain objects to lists.

Repetition Operator and Iterating over a List

  • Repetition Operator: * makes multiple copies of a list.

  • General Format: list * n for n copies.

  • Iteration: Use a for loop to iterate over a list.

    • Format: for x in list:

Indexing

  • Index: Number specifying the position of an element in a list.

  • Access: First element index is 0, second is 1, and n’th is n-1.

  • Negative Indexes: Identify positions from the end of the list.

    • Example: -1 for the last element.

The len Function

  • Purpose: len() returns the length of a sequence.

  • Example: size = len(my_list) gives the number of elements.

  • Prevents IndexError during list iteration.

Lists Are Mutable

  • Mutable Sequence: Can change items in the sequence.

  • Updating Elements: Use list[index] = new_value.

  • Valid index necessary to avoid IndexError.

Concatenating Lists

  • Concatenate: Use the + operator to join two lists.

  • Cannot concatenate with different data types.

  • Augmented Assignment: Use += for concatenation.

List Slicing

  • Slice: A span of items taken from a list.

  • Format: list[start:end] produces a new list.

  • Defaults: If start is not specified, defaults to 0; if end is not specified, defaults to len(list).

  • Can include step value and negative indexes.

Finding Items in Lists with the in Operator

  • Usage: item in list checks if an item is in a list.

  • Returns: True if found, False if not.

  • Not in Operator: Used to check if an item is not in the list.

List Methods and Useful Built-in Functions

  • append(item): Adds an item to the end of the list.

  • count(item): Returns occurrences of an item.

  • index(item): Returns the first index of an item.

  • insert(index, item): Inserts an item at a specified index.

  • sort(): Sorts list elements in ascending order.

  • remove(item): Removes the first occurrence of an item.

  • reverse(): Reverses the list order.

  • del statement: Removes an element from a specific index.

  • Sum, min, max: Built-in functions for totals and extremes.

Copying Lists

  • Methods: To copy a list, use a for loop or concatenate.

  • Creating a new empty list ensures complete copying.

Processing Lists

  • List elements can be utilized in calculations.

  • Total Calculation: Use a loop and an accumulator.

  • Average: Total divided by len(list).

  • Lists can be passed to functions and saved to files.

List Comprehensions

  • Definition: Concise expression for creating new lists.

  • Example: list2 = [item for item in list1] creates a copy of a list.

  • Can include conditional statements: list2 = [item for item in list1 if item < 10].

Two-Dimensional Lists

  • Definition: A list that contains other lists; often referred to as nested lists.

  • Usage: Useful for handling multiple data sets with two indexing levels.

Tuples

  • Definition: An immutable sequence; elements cannot be added, removed, or changed.

  • Creation: Use parentheses my_tuple = (item1, item2).

  • Support various list operations but lack certain methods like append, remove, and sort.

  • Can store mutable objects such as lists within tuples.

Plotting Data with Matplotlib

  • Library: Not part of standard library; requires separate installation.

  • Installation: Example commands provided for Windows, Mac, and Linux.

  • Importing: Use import matplotlib.pyplot as plt for plotting functions.

Plotting Types

  • Line Graphs: Created using plt.plot(x_coords, y_coords).

  • Bar Charts: Created using plt.bar(left_edges, heights); modify bar width and colors can be specified.

  • Pie Charts: Created using plt.pie(values, labels), which visualizes proportions of datasets.

Summary

  • This chapter covered Lists, Tuples, processing techniques, and the matplotlib Package for data visualization.