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Introduction to Computation and Programming Using Python pdf, Though each programming language is different (though not as different as their designers would have us believe), there are some dimensions along which they can be related.
• Low-level versus high-level refers to whether we program using instructions and data objects at the level of the machine (e.g., move 64 bits of data from this location to that location) or whether we program using more abstract operations (e.g., pop up a menu on the screen) that have been provided by the language designer.
• General versus targeted to an application domain refers to whether the primitive operations of the programming language are widely applicable or are fine-tuned to a domain. For example, Adobe Flash is designed to facilitate adding animation and interactivity to Web pages, but you wouldn’t want to use it build a stock portfolio analysis program.
• Interpreted versus compiled refers to whether the sequence of instructions written by the programmer, called source code, is executed directly (by an interpreter) or whether it is first converted (by a compiler) into a sequence of machine-level primitive operations. (In the early days of computers, people had to write source code in a language that was very close to the machine code that could be directly interpreted by the computer hardware.) There are advantages to both approaches. It is often easier to debug programs written in languages that are designed to be interpreted because the interpreter can produce error messages that are easy to correlate with the source code. Compiled languages usually produce programs that run more quickly and use less space.
In this book (Introduction to Computation and Programming Using Python pdf ), we use Python. However, this book is not about Python. It will certainly help readers learn Python, and that’s a good thing. What is much more important, however, is that careful readers will learn something about how to write programs that solve problems. This skill can be transferred to any programming language
Introduction to Computation and Programming Using Python Pdf Chapters And Sections
Table Of Contents For Introduction to Computation and Programming Using Python Pdf
1 GETTING STARTED
2 INTRODUCTION TO PYTHON
The Basic Elements of Python
Objects, Expressions, and Numerical Types
Variables and Assignment
Strings and Input
3 SOME SIMPLE NUMERICAL PROGRAMS
Approximate Solutions and Bisection Search
A Few Words About Using Floats
4 FUNCTIONS, SCOPING, and ABSTRACTION
Functions and Scoping
Keyword Arguments and Default Values
5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS
Sequences and Multiple Assignment
Lists and Mutability
Functions as Objects
Strings, Tuples, and Lists
6 TESTING AND DEBUGGING
Learning to Debug
Designing the Experiment
When the Going Gets Tough
And When You Have Found “The” Bug
7 EXCEPTIONS AND ASSERTIONS
Exceptions as a Control Flow Mechanism
8 CLASSES AND OBJECT-ORIENTED PROGRAMMING
Abstract Data Types and Classes
Designing Programs Using Abstract Data Types
Using Classes to Keep Track of Students and Faculty
Multiple Levels of Inheritance
The Substitution Principle
Encapsulation and Information Hiding
Mortgages, an Extended Example
9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY
Thinking About Computational Complexity
Some Important Complexity Classes
Comparisons of Complexity Classes
10 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES
Linear Search and Using Indirection to Access Elements
Binary Search and Exploiting Assumptions
Exploiting Functions as Parameters
Sorting in Python
11 PLOTTING AND MORE ABOUT CLASSES
Plotting Using PyLab
Plotting Mortgages, an Extended Example
12 STOCHASTIC PROGRAMS, PROBABILITY, AND STATISTICS
Inferential Statistics and Simulation
Normal Distributions and Confidence Levels
Exponential and Geometric Distributions
How Often Does the Better Team Win?
Hashing and Collisions
13 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION
The Drunkard’s Walk
Biased Random Walks
14 MONTE CARLO SIMULATION
Pass or Don’t Pass?
Using Table Lookup to Improve Performance
Some Closing Remarks About Simulation Models
15 UNDERSTANDING EXPERIMENTAL DATA
The Behavior of Springs
Using Linear Regression to Find a Fit
The Behavior of Projectiles
Coefficient of Determination
Using a Computational Model
Fitting Exponentially Distributed Data
When Theory Is Missing
16 LIES, DAMNED LIES, AND STATISTICS
Garbage In Garbage Out (GIGO)
Pictures Can Be Deceiving
Cum Hoc Ergo Propter Hoc
Statistical Measures Don’t Tell the Whole Story
Beware of Extrapolation
The Texas Sharpshooter Fallacy
Percentages Can Confuse
17 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS
An Optimal Solution to the 0/1 Knapsack Problem
Graph Optimization Problems
Some Classic Graph-Theoretic Problems
The Spread of Disease and Min Cut
Shortest Path: Depth-First Search and Breadth-First Search
18 DYNAMIC PROGRAMMING
Fibonacci Sequences, Revisited
Dynamic Programming and the 0/1 Knapsack Problem
Dynamic Programming and Divide-and-Conquer
19 A QUICK LOOK AT MACHINE LEARNING
Types Example and Cluster
A Contrived Example
A Less Contrived Example
PYTHON 2.7 QUICK REFERENCE
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