Advanced Python Strings


In the previous guide, you learned the basics of handling strings in Python. In this guide, you will explore some of the more complex things that strings are capable of. There are tools built into Python, called string methods, that help you to handle strings and do some very powerful things. Through the use of string methods, you can masterfully manipulate text and use it to its fullest potential without writing a ton of code.

Navigating a String

Strings aren’t words. They aren’t sentences, phrases, and believe it or not, they aren’t even a collection of text. Strings are just a lists of characters. Those characters can be letter, numbers, symbols, spaces, and escape characters. Python sees strings by their parts(the characters) and uses those parts to manipulate strings. This is actually true of almost any programming language. So, that means that you can select individual characters out of a string. Try this:

phrase_string = "This phrase is a string!"

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Python Comments


This guide isn’t about programming. In fact, there is no new code involved at all. However, it does have everything to do with making sure that the code that you write is understandable to both yourself and anyone else that might look at it down the line.

If you’ve looked at any open source projects, you’ve probably noticed notes placed in by the programmers. Those notes are just plain text. The programming language doesn’t compile or interpret them in any way. It just ignores them. It knows that those comments are for humans, not computers.

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Python Lists


Lists are a big deal. It really can’t be overemphasized what a big deal they are. Lists are not only used for iterating through data, but they are also a popular data storage and
categorization method used for handling data as a program is running. For anyone who’s programmed in another language, lists are often known as arrays.

Lists may be either very simple or very complex, but they all follow the same rules. Lists may contain different types of data, but be careful when working with it. If you create a list that mixes, say, strings and floats, be sure not to try to call a string method on a list item containing a float.

Remember when the string guides said that strings were just a list of characters. Well, now’s the time to be glad that you paid attention to strings. You can do many of the same things that you did
with strings with lists, and you can do them in the same way. So, some of this is going to feel like a repeat of the string guide, but don’t break focus. There are differences, and there are list
specific methods, so assuming that strings and lists are the exact same this will get you into trouble.

Creating a List

Creating a list is a bit different than the other variables that you’ve learned about so far. You can create a list with absolutely no values in it at all. This is useful for situations where you
don’t exactly know what will be added to the list because the data isn’t in the program yet. There will also be plenty of situations where you won’t be sure how many entries will be in a list, so
again, creating an empty one and adding data later is the right move.

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Python Multidimensional Lists


Now it’s time to take lists to a new dimension. No, you don’t need to learn how to code in “The Upside Down,” but there are additional degrees of complexity to lists. Lists are used to hold data,
but they are also used to categorize it. Values inside a list can be further broken down into other sets. That’s essentially what a multidimensional list is.

Two Dimensional Lists

What is a list that holds lists? That’s all a two dimensional list is. The list below consists of three lists. Each of the three lists has five elements. Don’t worry about numbers quite yet. Just
focus on the top level elements, the lists. You can access them the way you would any element in a normal list.

number_sets = [[2, 4, 6, 8, 10], [3, 6, 9, 12, 15], [4, 8, 12, 16, 20]]

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Python Tuples


Tuples are immutable data sets made up of data of different types. While tuples are very similar to lists, they are different in those key ways.

Tuples cannot be changed once they are created. The data they hold can be passed to a different tuple, but the original tuple cannot be changed. This means that tuples don’t have methods for
manipulating them like lists do because they cannot be manipulated.

Like lists, tuples can also contain data of different types. Tuples can contain strings, integers, floats, booleans, and
even lists. Because tuples are immutable, they aren’t meant to be manipulated, so data types don’t matter nearly as much.

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Python While Loops


Many times in programming, you will need to repeat the same task many times. In fact, looping through and repeating an operation is one of the cornerstones of programming. After all, one of the things that computers are way better than humans at is performing repetitive tasks without getting tired or making mistakes.

One of the simplest ways to make a program repeat the same task is to use a while loop. A while loop repeats the same block of code while a condition is true. When that condition becomes false, the loop will break, and the regular flow of code will resume.

The structure of a while loop is similar to what you encountered in the last guide with if. A while loop begins with the word while followed by parenthesis containing the condition of the loop and a colon. The following lines are indented and will execute in the loop.

Infinite While

Check out this while loop. Try it yourself in your interpreter to see exactly what it does. You might be somewhat surprised.

# Import time for sleep
import time

# While loop

What happened? Rather, what is happening? If you haven’t figured out how to stop it yet, just press Ctrl+C. A while loop will run indefinitely as long as the condition that it is given remains True. The loop above was given True as its condition, which will never not be true.

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Python For Loops


There is yet another type of loop. That loop is designed for iterating sets of data. That’s right, lists. Unlike while loops, these for loops have a defined length based on the data set that they are iterating over.

Generally, for loops are used to access and modify each element in a list. To do this, they temporarily represent each element as a new variable used only within the loop.

for loops have a slightly different structure than while loops do. They begin with the word for, which is followed by the temporary variable being created for the loop. Then there is the keyword in specifying the set of data being used, followed by the data set itself and, ultimately, a colon.

For With Range

There is a method called range() that either takes a single number and behaves like a list of numbers going from zero until the number before the one specified or takes two numbers separated by a comma and acts like a slice starting at the first number and listing all numbers until the number before the last one.

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Python Introduction and Installation Guide


Python is a dynamically typed, interpreted, general purpose programming language. It’s useful for everything from system scripting, to web applications, to full graphical desktop programs. Because of that, it’s no surprise that demand for Python programming skills is only increasing, and top companies like Google, Mozilla, Instagram(Facebook), and Reddit rely on it as part of their core technology stack. Not only that, but Python is a favorite in both academic and scientific circles and is gaining ground in the financial sector. Top universities are even using it to teach programming in their computer science programs.

With all of that said, you’d probably be thinking that Python is something super difficult to learn and only accessible to the elite in the technology field, but you couldn’t be more wrong. Python is easy. Python is really easy. In fact, Python is one of the first languages used to teach children to program. Python was designed to be very clear and simple to understand. It reads like plain English, and its syntax makes use of spaces rather than brackets and semicolons, so it always looks clean and uncluttered. It’s very difficult, if not impossible, to wright messy Python. This helps out new programmers and programmers new to Python big time because you can always tell what you’re looking at, or at least, get a decent sense of what it does. This way, you can look at code examples from established open source projects to get an idea of what professional grade Python looks like and how it’s used.

Python and Linux work incredibly well together. It wasn’t all that long ago that Python supplanted Perl as the de facto scripting and “glue” language on Linux systems. This means that many scripts and utilities that ship with modern Linux systems are written in Python. As a result, most Linux distributions have Python installed by default, but there is a bit of a catch. There are two current versions of Python. Python 2.7.X and Python 3.X.X are both current. Syntactically, they are very similar, but Python 3 has some features that Python 2 doesn’t. That means that they are not entirely compatible and many distributions package them separately. So, your system may have Python 2, but not Python 3 or vice versa. This guide and the others in the series are going to cover Python 3. It is the future of Python, and it’s not so bad to go back to Python 2 after you’ve worked with Python 3.

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Python Dictionaries


Somebody hit lists with gamma rays. Okay, so Dictionaries aren’t the Incredible Hulk, but they are supercharged in what they can do. In other languages, dictionaries are referred to as hashes, associative arrays, and associative lists. It’s probably best to think of them as associative lists because that’s exactly what they are. Dictionaries are lists that associate two values with one another. To think of it in terms of an actual dictionary, they associate a word, or key with a definition, or value. They function sort of like a list with custom indexes.

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Python Files and the Interpreter


Python is an interpreted language, meaning that it is compiled every time that it is run. There are a number of pros and cons when talking about an interpreted language like this.

First, on a positive note, they tend to be easier to debug. They fail immediately when they are run, and tell you what went wrong, which is nice compared to compiled languages like C/C++, which can compile just fine, but fail silently when run.

Interpreted languages are also very portable. All you have to do is install the interpreter on a system, and most code written in that language can run fine, regardless of the operating system. There are some exceptions when dealing with operating system specific code and libraries, but if you’ve planned for portability, you can work around those situations.

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Python Advanced Dictionaries


You’ve already gotten acquainted with dictionaries, but just like the other data structures Python supports, there are methods and more powerful ways to use them. There aren’t as many methods for working with dictionaries as there are for lists, but that’s because dictionaries just don’t need them. Plus, many of the ones that do exist, work to break down dictionaries into lists and tuples to make them easier to manage. So, those list methods can be used in conjunction with the dictionary ones to create an efficient machine for handling data.

Dictionary Methods

Items, Keys, and Values

These methods work to break down dictionaries into other data structures to make working with them much more manageable. Doing so also gives access to the methods of those data structures. Through these combinations of methods and loops, you can access and manipulate data with ease.

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Experimenting With Numbers and Text In Python


You probably want to jump in and start coding right away. That’s a great attitude to have, but it’s much better to experiment with the language and your programming environment first. If you’ve never programmed or never worked with an interpreted language like Python before, it’s important to get a feel for the way Python works and start to develop a workflow. One great aspect of Python being interpreted is the ability to write a couple of quick lines of code and test them out in real time. There really isn’t much setup beyond what you’ve already done.

Playing With Numbers

Without knowing anything about the language, you can use Python like a basic calculator. Open up either your .py file or the interpreter. Type in a basic math problem and run it.

>>> 10+25

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