Python How to Think Like a Computer Scientist? See the document (4_parts_to_this_Module_4.1.2_) it has the instruction for part A (reflection) and part B (

Python How to Think Like a Computer Scientist? See the document (4_parts_to_this_Module_4.1.2_) it has the instruction for part A (reflection) and part B (response). make it good! THIS HAS 4 PARTS A, B, C, and D all of them have its own requirement
and please label each part
Required Reading
Programming Knowledge. (2014). Python tutorial for beginners 7: Python
Lists. . Retrieved
from https://www.youtube.com/watch?v=dV9K6QMrIn4
Programming Knowledge. (2014). Python tutorial for beginners 6: Python
Strings. . Retrieved
from https://www.youtube.com/watch?v=xBDGux27Qrg
Programming Knowledge. (2014). Python tutorial for beginners 15: Classes
and self. . Retrieved
from https://www.youtube.com/watch?v=cJq_kuAKPCs
Programming Knowledge. (2014). Python tutorial for beginners 17:
Subclasses , super classes, and Inheritance. . Retrieved
from https://www.youtube.com/watch?v=JIVHiXXTUHQ
Disqus. (2011). An introduction to Classes and Inheritance.
Retrieved from http://www.jesshamrick.com/2011/05/18/anintroduction-to-classes-and-inheritance-in-python/
Jones, J. (2014). Python class and object. .
Retrieved
from https://www.youtube.com/watch?v=AaIdperUu-A
Kindy, M. (2008). Chapter 11: Lists. Python 2: For Beginners Only. Edition1.0.
Retrieved from http://cs118.kindy.net/p2fbo_20131230.pdf
Kindy, M. (2008). Chapter 12: Tuples. Python 2: For Beginners Only. Edition1.0.
Retrieved from http://cs118.kindy.net/p2fbo_20131230.pdf
Kindy, M. (2008). Chapter 13: Strings. Python 2: For Beginners Only. Edition1.0.
Retrieved from http://cs118.kindy.net/p2fbo_20131230.pdf
How to think
like a computer scientist: Learning with Python 3 documentation ,
Wentworth, P., Elkner, J., Downey, A. B., & Meyers, C. (2017).
Release 3rd Edition. (n.p.). ( attached a copy of this )
*THIS IS PARTS A: it has its own requirement and please
label each part*
Initial posting reveals Demonstrates mastery covering all key elements of the assignment
in a substantive way clear understanding of all aspects of the threaded discussion question;
Demonstrates mastery conceptualizing issues raised in the discussion uses factual and
relevant information; and demonstrates full development of concepts. The postings were
well written with very few errors. Appropriate supporting information was properly cited
and referenced.
The post is at least 300+ words in length.)
Assignment:
Reflection
Given the readings and assignments in the course, identify and briefly
discuss two important concepts applicable to your professional
discipline.
PART B:
(Replies to peers. Demonstrated analysis of others’ posts; extends meaningful discussions by
building on previous peer posts and offering alternative perspectives. Bring in ideas/comments
and/or research not mentioned yet. Appropriate supporting information was properly cited
and referenced. 200 words per posting).
Reflection
Sabrenia Little posted Jan 28, 2019 4:26 AM
In this course I’ve learned a lot about Python. I am familiar with java,
java script, css, and html programming languages but Python initially
was a bit challenging for me. I think it is always difficult to get familiar
with the syntax of a new language but once you overcome the learning
curve you notice most languages are very similar in how they work. For
example, stings and lists serve the same purpose in different ways in
java script and python. What I enjoyed most about this course is
learning about tuples and how to pass them as arguments within
functions. I also enjoyed learning about was using turtle to draw in
python. I was unaware of how powerful programming could actually be,
so coding a program to draw snowflakes and turtles amazed me.
Overall, I really enjoyed this course and all the material presented. I am
eager to see where this python journey will take me but I am confident
that I will be able to successfully use what I learned here in my
professional career.
How to Think Like a Computer
Scientist: Learning with Python 3
Documentation
Release 3rd Edition
Peter Wentworth, Jeffrey Elkner,
Allen B. Downey and Chris Meyers
Jul 06, 2017
Contents
1
The way of the program
3
2
Variables, expressions and statements
13
3
Program Flow
29
4
Functions
77
5
Data Types
109
6
Numpy
159
7
Files
167
8
Modules
175
9
More datatypes
189
10 Recursion
193
11 Classes and Objects
209
12 Exceptions
255
13 Fitting
261
14 PyGame
267
15 Copyright Notice
291
16 Contributions
293
A Modules
297
B More datatypes
311
i
C Recursion
315
D Classes and Objects
331
E Exceptions
377
F Fitting
383
G PyGame
389
H Plotting data with matplotlib
413
ii
How to Think Like a Computer Scientist: Learning with Python 3
Documentation, Release 3rd Edition
3rd Edition (Using Python 3.x)
by Jeffrey Elkner, Peter Wentworth, Allen B. Downey, and Chris Meyers
illustrated by Dario Mitchell
• Copyright Notice
• Contributor List
• Chapter 1 The way of the program
• Chapter 2 Variables, expressions, and statements
• Chapter 3 Program Flow
• Chapter 4 Functions
• Chapter 5 Datatypes
• Chapter 6 Numpy
• Chapter 7 File I/O
• Appendix A Writing Your Own Modules
• Appendix B More datatypes
• Appendix C Recursion
• Appendix D Object Oriented Programming
• Appendix E Exceptions
• Appendix F Fitting and Scientific Data Handling
• Appendix G PyGame
• Appendix H Plotting with matplotlib
• GNU Free Document License
Contents
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Contents
CHAPTER 1
The way of the program
The goal of this book is to teach you to think like a computer scientist. This way of thinking
combines some of the best features of mathematics, engineering, and natural science. Like
mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems,
form hypotheses, and test predictions.
The single most important skill for a computer scientist is problem solving. Problem solving
means the ability to formulate problems, think creatively about solutions, and express a solution
clearly and accurately. As it turns out, the process of learning to program is an excellent
opportunity to practice problem-solving skills. That’s why this chapter is called, The way of
the program.
On one level, you will be learning to program, a useful skill by itself. On another level, you
will use programming as a means to an end. As we go along, that end will become clearer.
The Python programming language
The programming language you will be learning is Python. Python is an example of a highlevel language; other high-level languages you might have heard of are C++, PHP, Pascal, C#,
and Java.
As you might infer from the name high-level language, there are also low-level languages,
sometimes referred to as machine languages or assembly languages. Loosely speaking, computers can only execute programs written in low-level languages. Thus, programs written in a
high-level language have to be translated into something more suitable before they can run.
Almost all programs are written in high-level languages because of their advantages. It is much
easier to program in a high-level language so programs take less time to write, they are shorter
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and easier to read, and they are more likely to be correct. Second, high-level languages are
portable, meaning that they can run on different kinds of computers with few or no modifications.
The engine that translates and runs Python is called the Python Interpreter: There are two
ways to use it: immediate mode and script mode. In immediate mode, you type Python expressions into the Python Interpreter window, and the interpreter immediately shows the result:
The >>> is called the Python prompt. The interpreter uses the prompt to indicate that it is
ready for instructions. We typed 2 + 2, and the interpreter evaluated our expression, and
replied 4, and on the next line it gave a new prompt, indicating that it is ready for more input.
Alternatively, you can write a program in a file and use the interpreter to execute the contents
of the file. Such a file is called a script. Scripts have the advantage that they can be saved to
disk, printed, and so on.
Working directly in the interpreter is convenient for testing short bits of code because you get
immediate feedback. Think of it as scratch paper used to help you work out problems. Anything
longer than a few lines should be put into a script.
What is a program?
A program is a sequence of instructions that specifies how to perform a computation. The
computation might be something mathematical, such as solving a system of equations or finding
the roots of a polynomial, but it can also be a symbolic computation, such as searching and
replacing text in a document or (strangely enough) compiling a program.
The details look different in different languages, but a few basic instructions appear in just
about every language:
input Get data from the keyboard, a file, or some other device such as a sensor.
output Display data on the screen or send data to a file or other device such as a motor.
math Perform basic mathematical operations like addition and multiplication.
conditional execution Check for certain conditions and execute the appropriate sequence of
statements.
repetition Perform some action repeatedly, usually with some variation.
Believe it or not, that’s pretty much all there is to it. Every program you’ve ever used, no matter
how complicated, is made up of instructions that look more or less like these. Thus, we can
describe programming as the process of breaking a large, complex task into smaller and smaller
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subtasks until the subtasks are simple enough to be performed with sequences of these basic
instructions.
That may be a little vague, but we will come back to this topic later when we talk about algorithms.
What is debugging?
Programming is a complex process, and because it is done by human beings, it often leads to
errors. Programming errors are called bugs and the process of tracking them down and correcting them is called debugging. Use of the term bug to describe small engineering difficulties
dates back to at least 1889, when Thomas Edison had a bug with his phonograph.
Three kinds of errors can occur in a program: syntax errors, runtime errors, and semantic errors.
It is useful to distinguish between them in order to track them down more quickly.
Syntax errors
Python can only execute a program if the program is syntactically correct; otherwise, the process fails and returns an error message. Syntax refers to the structure of a program and the
rules about that structure. For example, in English, a sentence must begin with a capital letter
and end with a period. this sentence contains a syntax error. So does this one
For most readers, a few syntax errors are not a significant problem, which is why we can read
the poetry of E. E. Cummings without problems. Python is not so forgiving. If there is a single
syntax error anywhere in your program, Python will display an error message and quit, and you
will not be able to run your program. During the first few weeks of your programming career,
you will probably spend a lot of time tracking down syntax errors. As you gain experience,
though, you will make fewer errors and find them faster.
Runtime errors
The second type of error is a runtime error, so called because the error does not appear until
you run the program. These errors are also called exceptions because they usually indicate that
something exceptional (and bad) has happened.
Runtime errors are rare in the simple programs you will see in the first few chapters, so it might
be a while before you encounter one.
Semantic errors
The third type of error is the semantic error. If there is a semantic error in your program, it
will run successfully, in the sense that the computer will not generate any error messages, but
1.3. What is debugging?
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it will not do the right thing. It will do something else. Specifically, it will do what you told it
to do.
The problem is that the program you wrote is not the program you wanted to write. The
meaning of the program (its semantics) is wrong. Identifying semantic errors can be tricky
because it requires you to work backward by looking at the output of the program and trying to
figure out what it is doing.
Experimental debugging
One of the most important skills you will acquire is debugging. Although it can be frustrating,
debugging is one of the most intellectually rich, challenging, and interesting parts of programming.
In some ways, debugging is like detective work. You are confronted with clues, and you have
to infer the processes and events that led to the results you see.
Debugging is also like an experimental science. Once you have an idea what is going wrong,
you modify your program and try again. If your hypothesis was correct, then you can predict the
result of the modification, and you take a step closer to a working program. If your hypothesis
was wrong, you have to come up with a new one. As Sherlock Holmes pointed out, When you
have eliminated the impossible, whatever remains, however improbable, must be the truth. (A.
Conan Doyle, The Sign of Four)
For some people, programming and debugging are the same thing. That is, programming is
the process of gradually debugging a program until it does what you want. The idea is that
you should start with a program that does something and make small modifications, debugging
them as you go, so that you always have a working program.
For example, Linux is an operating system kernel that contains millions of lines of code, but
it started out as a simple program Linus Torvalds used to explore the Intel 80386 chip. According to Larry Greenfield, one of Linus’s earlier projects was a program that would switch
between displaying AAAA and BBBB. This later evolved to Linux (The Linux Users’ Guide
Beta Version 1).
Later chapters will make more suggestions about debugging and other programming practices.
Formal and natural languages
Natural languages are the languages that people speak, such as English, Spanish, and French.
They were not designed by people (although people try to impose some order on them); they
evolved naturally.
Formal languages are languages that are designed by people for specific applications. For
example, the notation that mathematicians use is a formal language that is particularly good
at denoting relationships among numbers and symbols. Chemists use a formal language to
represent the chemical structure of molecules. And most importantly:
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Programming languages are formal languages that have been designed to express
computations.
Formal languages tend to have strict rules about syntax. For example, 3+3=6 is a syntactically
correct mathematical statement, but 3=+6$ is not. H2 O is a syntactically correct chemical
name, but 2 Zz is not.
Syntax rules come in two flavors, pertaining to tokens and structure. Tokens are the basic elements of the language, such as words, numbers, parentheses, commas, and so on. In Python, a
statement like print(“Happy New Year for “,2013) has 6 tokens: a function name,
an open parenthesis (round bracket), a string, a comma, a number, and a close parenthesis.
It is possible to make errors in the way one constructs tokens. One of the problems with 3=+6$
is that $ is not a legal token in mathematics (at least as far as we know). Similarly, 2 Zz is not a
legal token in chemistry notation because there is no element with the abbreviation Zz.
The second type of syntax rule pertains to the structure of a statement— that is, the way the
tokens are arranged. The statement 3=+6$ is structurally illegal because you can’t place a plus
sign immediately after an equal sign. Similarly, molecular formulas have to have subscripts
after the element name, not before. And in our Python example, if we omitted the comma,
or if we changed the two parentheses around to say print)”Happy New Year for “,
2013( our statement would still have six legal and valid tokens, but the structure is illegal.
When you read a sentence in English or a statement in a formal language, you have to figure
out what the structure of the sentence is (although in a natural language you do this subconsciously). This process is called parsing.
For example, when you hear the sentence, “The other shoe fell”, you understand that the other
shoe is the subject and fell is the verb. Once you have parsed a sentence, you can figure out
what it means, or the semantics of the sentence. Assuming that you know what a shoe is and
what it means to fall, you will understand the general implication of this sentence.
Although formal and natural languages have many features in common — tokens, structure,
syntax, and semantics — there are many differences:
ambiguity Natural languages are full of ambiguity, which people deal with by using contextual clues and other information. Formal languages are designed to be nearly or completely unambiguous, which means that any statement has exactly one meaning, regardless of context.
redundancy In order to make up for ambiguity and reduce misunderstandings, natural languages employ lots of redundancy. As a result, they are often verbose. Formal languages
are less redundant and more concise.
literalness Formal languages mean exactly what they say. On the other hand, natural languages are full of idiom and metaphor. If someone says, “The other shoe fell”, there is
probably no shoe and nothing falling. You’ll need to find the original joke to understand
the idiomatic meaning of the other shoe falling. Yahoo! Answers thinks it knows!
People who grow up speaking a natural language—everyone—often have a hard time adjusting
to formal languages. In some ways, the difference between formal and natural language is like
the difference between poetry and prose, but more so:
poetry Words are used for their sounds as well as for their meaning, and the whole poem
1.8. Formal and natural languages
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together creates an effect or emotional response. Ambiguity is not only common but
often deliberate.
prose The literal meaning of words is more important, and the structure contributes more
meaning. Prose is more amenable to analysis than poetry but still often ambiguous.
program The meaning of a computer program is unambiguous and literal, and can be understood entirely by analysis of the tokens and structure.
Here are some suggestions for reading programs (and other formal languages). First, remember
that formal languages are much more dense than natural languages, so it takes longer to read
them. Also, the structure is very important, so it is usually not a good idea to read from top to
bottom, left to right. Instead, learn to parse the program in your head, identifying the tokens
and interpreting the structure. Finally, the details matter. Little things like spelling errors and
bad punctuation, which you can get away with in natural languages, can make a big difference
in a formal language.
The first program
Traditionally, the first program written in a new language is called Hello, World! because all it
does is display the words, Hello, World! In Python, the script looks like this: (For scripts, we’ll
show line numbers to the left of the Python statements.)
1
print(“Hello, World!”)
…
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