01. Introduction and First Steps – Take a Deep Breath
Computers are very powerful tools, but unfortunately, they can't think for themselves. So they need to be told everything. They need to be told how to perform a task, how to evaluate a condition to decide which path to follow, how to handle data that comes from a device such as the network or a disk, and how to react
when something unforeseen happens, say, something is broken or missing.
You can code in many different styles and languages. Is it hard? I would say "yes" and "no". It's a bit like writing. Everybody can learn how to write, and you can too. But what if you wanted to become a poet? Then writing alone is not enough. You have to acquire a whole other set of skills and this will take a longer and greater effort.
In the end, it all comes down to how far you want to go down the road. Coding is
not just putting together some instructions that work. It is so much more!
Good code is short, fast, elegant, easy to read and understand, simple, easy to modify and extend, easy to scale and refactor, and easy to test. It takes time to be able to write code that has all these qualities at the same time, but the good news is that you're taking the first step towards it at this very moment by reading this module. And I have no doubt you can do it. Anyone can, in fact, we all program all the time, only we aren't aware of it.
Would you like an example?
Say you want to make instant coffee. You have to get a mug, the instant coffee jar, a teaspoon, water, and the kettle. Even if you're not aware of it, you're evaluating a lot of data. You're making sure that there is water in the kettle as well as the kettle is plugged-in, that the mug is clean, and that there is enough coffee in the jar. Then, you boil the water and maybe in the meantime you put some coffee in the mug.
When the water is ready, you pour it into the cup, and stir.
So, how is this programming?
Well, we gathered resources (the kettle, coffee, water, teaspoon, and mug) and we verified some conditions on them (kettle is plugged-in, mug is clean, there is enough coffee). Then we started two actions (boiling the water and putting coffee in the mug), and when both of them were completed, we finally ended the procedure by pouring water in the mug and stirring.
Can you see it? I have just described the high-level functionality of a coffee program. It wasn't that hard because this is what the brain does all day long: evaluate conditions, decide to take actions, carry out tasks, repeat some of them, and stop at some point. Clean objects, put them back, and so on.
All you need now is to learn how to deconstruct all those actions you do automatically in real life so that a computer can actually make some sense of them. And you need to learn a language as well, to instruct it.
So this is what this module is for. I'll tell you how to do it and I'll try to do that by
means of many simple but focused examples (my favorite kind).
A proper introduction
I love to make references to the real world when I teach coding; I believe they help people retain the concepts better. However, now is the time to be a bit more rigorous and see what coding is from a more technical perspective.
When we write code, we're instructing a computer on what are the things it has to do. Where does the action happen? In many places: the computer memory, hard drives, network cables, CPU, and so on. It's a whole "world", which most of the time is the representation of a subset of the real world.
If you write a piece of software that allows people to buy clothes online, you will have to represent real people, real clothes, real brands, sizes, and so on and so forth, within the boundaries of a program.
In order to do so, you will need to create and handle objects in the program you're writing. A person can be an object. A car is an object. A pair of socks is an object. Luckily, Python understands objects very well.
The two main features any object has are properties and methods. Let's take a person object as an example. Typically in a computer program, you'll represent people
as customers or employees. The properties that you store against them are things like the name, the SSN, the age, if they have a driving license, their e-mail, gender, and so on. In a computer program, you store all the data you need in order to use an object for the purpose you're serving. If you are coding a website to sell clothes, you probably want to store the height and weight as well as other measures of your customers so that you can suggest the appropriate clothes for them. So, properties are characteristics of an object. We use them all the time: "Could you pass me that pen?" – "Which one?" – "The black one." Here, we used the "black" property of a pen to identify it (most likely amongst a blue and a red one).
Methods are things that an object can do. As a person, I have methods such as speak, walk, sleep, wake-up, eat, dream, write, read, and so on. All the things that I can do could be seen as methods of the objects that represents me.
So, now that you know what objects are and that they expose methods that you can run and properties that you can inspect, you're ready to start coding. Coding in fact is simply about managing those objects that live in the subset of the world that we're reproducing in our software. You can create, use, reuse, and delete objects as you please.
We'll take a closer look at Python objects in the upcoming chapter. For now, all we need to know is that every object in Python has an ID (or identity), a type, and a value.
Once created, the identity of an object is never changed. It's a unique identifier for it, and it's used behind the scenes by Python to retrieve the object when we want to use it.
The type as well, never changes. The type tells what operations are supported by the object and the possible values that can be assigned to it.
The value can either change or not. If it can, the object is said to be mutable, while when it cannot, the object is said to be immutable.
How do we use an object? We give it a name of course! When you give an object a
name, then you can use the name to retrieve the object and use it.
In a more generic sense, objects such as numbers, strings (text), collections, and so on are associated with a name. Usually, we say that this name is the name of a variable. You can see the variable as being like a box, which you can use to hold data.
So, you have all the objects you need: what now? Well, we need to use them, right? We may want to send them over a network connection or store them in a database. Maybe display them on a web page or write them into a file. In order to do so, we need to react to a user filling in a form, or pressing a button, or opening a web page and performing a search. We react by running our code, evaluating conditions to choose which parts to execute, how many times, and under which circumstances.
And to do all this, basically we need a language. That's what Python is for. Python is the language we'll use together throughout this module to instruct the computer to do something for us.
Now, enough of this theoretical stuff, let's get started.
Enter the Python
Python is the marvelous creature of Guido Van Rossum, a Dutch computer scientist and mathematician who decided to gift the world with a project he was playing around with over Christmas 1989. The language appeared to the public somewhere around 1991, and since then has evolved to be one of the leading programming languages used worldwide today.
I started programming when I was 7 years old, on a Commodore VIC 20, which was later replaced by its bigger brother, the Commodore 64. The language was BASIC. Later on, I landed on Pascal, Assembly, C, C++, Java, JavaScript, Visual Basic, PHP, ASP, ASP .NET, C#, and other minor languages I cannot even remember, but only when I landed on Python, I finally had that feeling that you have when you find the right couch in the shop. When all of your body parts are yelling, "Buy this one! This one is perfect for us!"
It took me about a day to get used to it. Its syntax is a bit different from what I was used to, and in general, I very rarely worked with a language that defines scoping with indentation. But after getting past that initial feeling of discomfort (like having new shoes), I just fell in love with it. Deeply. Let's see why.
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About Python
Before we get into the gory details, let's get a sense of why someone would want to use Python (I would recommend you to read the Python page on Wikipedia to get a more detailed introduction).
To my mind, Python exposes the following qualities.
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Portability
Python runs everywhere, and porting a program from Linux to Windows or Mac is usually just a matter of fixing paths and settings. Python is designed for portability and it takes care of operating system (OS) specific quirks behind interfaces that shield you from the pain of having to write code tailored to a specific platform.
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Coherence
Python is extremely logical and coherent. You can see it was designed by a brilliant computer scientist. Most of the time you can just guess how a method is called, if you don't know it.
You may not realize how important this is right now, especially if you are at the beginning, but this is a major feature. It means less cluttering in your head, less skimming through the documentation, and less need for mapping in your brain when you code.
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Developer productivity
According to Mark Lutz (Learning Python, 5th Edition, O'Reilly Media), a Python program is typically one-fifth to one-third the size of equivalent Java or C++ code. This means the job gets done faster. And faster is good. Faster means a faster response on the market. Less code not only means less code to write, but also less code to read (and professional coders read much more than they write), less code to maintain, to debug, and to refactor.
Another important aspect is that Python runs without the need of lengthy and time consuming compilation and linkage steps, so you don't have to wait to see the results of your work.
An extensive library
Python has an incredibly wide standard library (it's said to come with "batteries included"). If that wasn't enough, the Python community all over the world maintains a body of third party libraries, tailored to specific needs, which you can access freely at the Python Package Index (PyPI). When you code Python and you realize that you need a certain feature, in most cases, there is at least one library where that feature has already been implemented for you.
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Software quality
Python is heavily focused on readability, coherence, and quality. The language uniformity allows for high readability and this is crucial nowadays where code is more of a collective effort than a solo experience. Another important aspect of
Python is its intrinsic multi-paradigm nature. You can use it as scripting language, but you also can exploit object-oriented, imperative, and functional programming styles. It is versatile.
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Software integration
Another important aspect is that Python can be extended and integrated with many other languages, which means that even when a company is using a different language as their mainstream tool, Python can come in and act as a glue agent between complex applications that need to talk to each other in some way. This is kind of an advanced topic, but in the real world, this feature is very important.
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Satisfaction and enjoyment
Last but not least, the fun of it! Working with Python is fun. I can code for 8 hours and leave the office happy and satisfied, alien to the struggle other coders have to endure because they use languages that don't provide them with the same amount of well-designed data structures and constructs. Python makes coding fun, no doubt about it. And fun promotes motivation and productivity.
These are the major aspects why I would recommend Python to everyone for.
Of course, there are many other technical and advanced features that I could have talked about, but they don't really pertain to an introductory section like this one. They will come up naturally, chapter after chapter, in this module.
What are the drawbacks?
Probably, the only drawback that one could find in Python, which is not due to personal preferences, is the execution speed. Typically, Python is slower than its compiled brothers. The standard implementation of Python produces, when you run an application, a compiled version of the source code called byte code (with the extension .pyc), which is then run by the Python interpreter. The advantage of this approach is portability, which we pay for with a slowdown due to the fact that Python is not compiled down to machine level as are other languages.
However, Python speed is rarely a problem today, hence its wide use regardless of this suboptimal feature. What happens is that in real life, hardware cost is no longer a problem, and usually it's easy enough to gain speed by parallelizing tasks. When it
comes to number crunching though, one can switch to faster Python implementations, such as PyPy, which provides an average 7-fold speedup by implementing advanced compilation techniques (check https://pypy.org/ for reference).
When doing data science, you'll most likely find that the libraries that you use with Python, such as Pandas and Numpy, achieve native speed due to the way they
are implemented.
If that wasn't a good enough argument, you can always consider that Python is driving the backend of services such as Spotify and Instagram, where performance is a concern. Nonetheless, Python does its job perfectly adequately.
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Who is using Python today?
Not yet convinced? Let's take a very brief look at the companies that are using Python today: Google, YouTube, Dropbox, Yahoo, Zope Corporation, Industrial Light & Magic, Walt Disney Feature Animation, Pixar, NASA, NSA, Red Hat, Nokia, IBM, Netflix, Yelp, Intel, Cisco, HP, Qualcomm, and JPMorgan Chase, just to name a few.
Even games such as Battlefield 2, Civilization 4, and QuArK are implemented using Python.
Python is used in many different contexts, such as system programming, web programming, GUI applications, gaming and robotics, rapid prototyping, system integration, data science, database applications, and much more.
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Setting up the environment
Before we talk about installing Python on your system, let me tell you about which Python version I'll be using in this module.
Python 2 versus Python 3 – The great debate
Python comes in two main versions—Python 2, which is the past—and Python 3, which is the present. The two versions, though very similar, are incompatible on some aspects.
In the real world, Python 2 is actually quite far from being the past. In short, even though Python 3 has been out since 2008, the transition phase is still far from being over. This is mostly due to the fact that Python 2 is widely used in the industry, and of course, companies aren't so keen on updating their systems just for the sake of updating, following the if it ain't broke, don't fix it philosophy. You can read all about the transition between the two versions on the Web.
Another issue that was hindering the transition is the availability of third-party libraries. Usually, a Python project relies on tens of external libraries, and of course, when you start a new project, you need to be sure that there is already a version 3 compatible library for any business requirement that may come up. If that's not the case, starting a brand new project in Python 3 means introducing a potential risk, which many companies are not happy to take.
At the time of writing, the majority of the most widely used libraries have been ported to Python 3, and it's quite safe to start a project in Python 3 for most cases. Many of the libraries have been rewritten so that they are compatible with both versions, mostly harnessing the power of the six (2 x 3) library, which helps introspecting and adapting the behavior according to the version used.
All the examples in this module will be run using this Python 3.4.0. Most of them will run also in Python 2 (I have version 2.7.6 installed as well), and those that won't will just require some minor adjustments to cater for the small incompatibilities between the two versions.
Don't worry about this version thing though: it's not that big an issue in practice.
What you need for this course
As you've seen there are too many requirements to get started, so I've prepared a table that will give you an overview of what you'll need for each module of the course:
Installing Python
Python is a fantastic, versatile, and an easy-to-use language. It's available for all three major operating systems—Microsoft Windows, Mac OS X, and Linux—and the installer, as well as the documentation, can be downloaded from the official Python website: https://www.python.org.
?Once you have Python running on your system, you should be able to open a command prompt and run the following code:
$ python3
Python 3.4.0 (default, Apr 11 2014, 13:05:11) [GCC 4.8.2] on Linux
Type "help", "copyright", "credits" or "license" for more information.
>>> print("Hello, world!") Hello, world!
>>> exit()
Note that we will be using the dollar sign ($) to denote that a command is to be typed into the terminal (also called a shell or cmd on Windows). You do not need to type this character (or the space that follows it). Just type in the rest of the line and press Enter.
After you have the above "Hello, world!" example running, exit the program and move on to installing a more advanced environment to run Python code, the IPython Notebook.
Installing IPython
IPython is a platform for Python development that contains a number of tools and environments for running Python and has more features than the standard interpreter. It contains the powerful IPython Notebook, which allows you to write
programs in a web browser. It also formats your code, shows output, and allows you to annotate your scripts. It is a great tool for exploring datasets.
To install IPython on your computer, you can type the following into a command- line prompt (not into Python):
$ pip install ipython[all]
You will need administrator privileges to install this system-wide. If you do not want to (or can't) make system-wide changes, you can install it for just the current user by running this command:
$ pip install --user ipython[all]
This will install the IPython package into a user-specific location—you will be able to use it, but nobody else on your computer can. If you are having difficulty with the installation, check the official documentation for more detailed installation instructions: https://ipython.org/install.html.
With the IPython Notebook installed, you can launch it with the following:
$ ipython3 notebook
This will do two things. First, it will create an IPython Notebook instance that will run in the command prompt you just used. Second, it will launch your web browser and connect to this instance, allowing you to create a new notebook. It will look something similar to the following screenshot (where home/bob will be replaced by your current working directory):
To stop the IPython Notebook from running, open the command prompt that has the instance running (the one you used earlier to run the IPython command). Then, press Ctrl + C and you will be prompted Shutdown this notebook server (y/[n])?.
Type y and press Enter and the IPython Notebook will shut down.
Installing additional packages
Python 3.4 will include a program called pip, which is a package manager that helps to install new libraries on your system. You can verify that pip is working on your system by running the $ pip3 freeze command, which tells you which packages you have installed on your system.
The additional packages can be installed via the pip installer program, which has been part of the Python standard library since Python 3.3. More information about pip can be found at https://docs.python.org/3/installing/index.html.
After we have successfully installed Python, we can execute pip from the command- line terminal to install additional Python packages:
pip install SomePackage
Already installed packages can be updated via the --upgrade flag:
pip install SomePackage --upgrade
A highly recommended alternative Python distribution for scientific computing is Anaconda by Continuum Analytics. Anaconda is a free—including commercial use—enterprise-ready Python distribution that bundles all the essential Python
packages for data science, math, and engineering in one user-friendly cross-platform distribution. The Anaconda installer can be downloaded at https://continuum. io/downloads#py34, and an Anaconda quick start-guide is available at https:// store.continuum.io/static/img/Anaconda-Quickstart.pdf.
After successfully installing Anaconda, we can install new Python packages using the following command:
conda install SomePackage
Existing packages can be updated using the following command:
conda update SomePackage
The major Python packages that were used for writing this course are listed here:
???????? NumPy
???????? SciPy
???????? scikit-learn
???????? matplotlib
???????? pandas
???????? tables
???????? pymongo
???????? redis
As these packages are all hosted on PyPI, the Python package index, they can be easily installed with pip. To install NumPy, you would run:
$ pip install numpy
To install scikit-learn, you would run:
$pip3install-Uscikit-learn
Most libraries will have an attribute for the version, so if you already have a library installed, you can quickly check its version:
>>> import redis
>>> redis. version ?'2.10.3'
How you can run a Python program
There are a few different ways in which you can run a Python program.
Running Python scripts
Python can be used as a scripting language. In fact, it always proves itself very useful. Scripts are files (usually of small dimensions) that you normally execute to do something like a task. Many developers end up having their own arsenal of tools that they fire when they need to perform a task. For example, you can have scripts to parse data in a format and render it into another different format. Or you can use a script to work with files and folders. You can create or modify configuration files, and much more. Technically, there is not much that cannot be done in a script.
It's quite common to have scripts running at a precise time on a server. For example, if your website database needs cleaning every 24 hours (for example, the table that stores the user sessions, which expire pretty quickly but aren't cleaned automatically), you could set up a cron job that fires your script at 3:00 A.M. every day.
?I have Python scripts to do all the menial tasks that would take me minutes or more to do manually, and at some point, I decided to automate. For example, I have a laptop that doesn't have a Fn key to toggle the touchpad on and off. I find this very annoying, and I don't want to go clicking about through several menus when I need to do it, so I wrote a small script that is smart enough to tell my system to toggle
the touchpad active state, and now I can do it with one simple click from my launcher. Priceless.
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Running the Python interactive shell
Another way of running Python is by calling the interactive shell. This is something we already saw when we typed python on the command line of our console.
So open a console, activate your virtual environment (which by now should be second nature to you, right?), and type python. You will be presented with a couple of lines that should look like this (if you are on Linux):
Python 3.4.0 (default, Apr 11 2014, 13:05:11) [GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
Those >>> are the prompt of the shell. They tell you that Python is waiting for you to type something. If you type a simple instruction, something that fits in one line, that's all you'll see. However, if you type something that requires more than one line of code, the shell will change the prompt to ..., giving you a visual clue that you're typing a multiline statement (or anything that would require more than one line
of code).
Go on, try it out, let's do some basic maths:
>>> 2 + 4
6
>>> 10 / 4
2.5
>>> 2 ** 1024
1797693134862315907729305190789024733617976978942306572734300811577326758
0550096313270847732240753602112011387987139335765878976881441662249284743
0639474124377767893424865485276302219601246094119453082952085005768838150
6823424628814739131105408272371633505106845862982399472459384797163048353
56329624224137216
The last operation is showing you something incredible. We raise 2 to the power of 1024, and Python is handling this task with no trouble at all. Try to do it in Java, C++, or C#. It won't work, unless you use special libraries to handle such big numbers.
I use the interactive shell every day. It's extremely useful to debug very quickly, for example, to check if a data structure supports an operation. Or maybe to inspect or run a piece of code.
When you use Django (a web framework), the interactive shell is coupled with it and allows you to work your way through the framework tools, to inspect the data in the database, and many more things. You will find that the interactive shell will soon become one of your dearest friends on the journey you are embarking on.
Another solution, which comes in a much nicer graphic layout, is to use IDLE (Integrated DeveLopment Environment). It's quite a simple IDE, which is intended mostly for beginners. It has a slightly larger set of capabilities than the naked interactive shell you get in the console, so you may want to explore it. It comes for free in the Windows Python installer and you can easily install it in any other system. You can find information about it on the Python website.
Guido Van Rossum named Python after the British comedy group Monty Python, so it's rumored that the name IDLE has been chosen in honor of Erik Idle, one of Monty Python's founding members.
Running Python as a service
Apart from being run as a script, and within the boundaries of a shell, Python can be coded and run as proper software. We'll see many examples throughout the module about this mode. And we'll understand more about it in a moment, when we'll talk about how Python code is organized and run.
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Running Python as a GUI application
Python can also be run as a GUI (Graphical User Interface). There are several frameworks available, some of which are cross-platform and some others are platform-specific.
Tk is a graphical user interface toolkit that takes desktop application development to a higher level than the conventional approach. It is the standard GUI for Tool Command Language (TCL), but also for many other dynamic languages and
can produce rich native applications that run seamlessly under Windows, Linux,
Mac OS X, and more.
Tkinter comes bundled with Python, therefore it gives the programmer easy access to the GUI world, and for these reasons, I have chosen it to be the framework for the GUI examples that I'll present in this module.
Among the other GUI frameworks, we find that the following are the most
widely used:
???????? PyQt
???????? wxPython
???????? PyGtk
Describing them in detail is outside the scope of this module, but you can find all
the information you need on the Python website in the GUI Programming section. If GUIs are what you're looking for, remember to choose the one you want according to some principles. Make sure they:
???????? Offer all the features you may need to develop your project
???????? Run on all the platforms you may need to support
???????? Rely on a community that is as wide and active as possible
领英推荐
???????? Wrap graphic drivers/tools that you can easily install/access
How is Python code organized
Let's talk a little bit about how Python code is organized. In this paragraph, we'll start going down the rabbit hole a little bit more and introduce a bit more technical names and concepts.
Starting with the basics, how is Python code organized? Of course, you write your code into files. When you save a file with the extension .py, that file is said to be a Python module.
It would be impractical to save all the code that it is required for software to work within one single file. That solution works for scripts, which are usually not longer than a few hundred lines (and often they are quite shorter than that).
A complete Python application can be made of hundreds of thousands of lines of code, so you will have to scatter it through different modules. Better, but not nearly good enough. It turns out that even like this it would still be impractical to work with the code. So Python gives you another structure, called package, which allows you to group modules together. A package is nothing more than a folder, which must contain a special file, ?init .py that doesn't need to hold any code but whose presence is required to tell Python that the folder is not just some folder, but it's actually a package (note that as of Python 3.3 ?init .py is not strictly required any more).
As always, an example will make all of this much clearer. I have created an example structure in my module project, and when I type in my Linux console:
$ tree -v example
I get a tree representation of the contents of the ch1/example folder, which holds the code for the examples of this chapter. Here's how a structure of a real simple application could look like:
example/
├── core.py
├── run.py
└── util
├── ?init .py
├── db.py
├── math.py
└── network.py
You can see that within the root of this example, we have two modules, core.py and run.py, and one package: util. Within core.py, there may be the core logic of our application. On the other hand, within the run.py module, we can probably find the logic to start the application. Within the util package, I expect to find various utility tools, and in fact, we can guess that the modules there are called by the type of tools they hold: db.py would hold tools to work with databases, math.py would of course hold mathematical tools (maybe our application deals with financial data), and network.py would probably hold tools to send/receive data on networks.
As explained before, the ?init .py file is there just to tell Python that util is a package and not just a mere folder.
Had this software been organized within modules only, it would have been much harder to infer its structure. I put a module only example under the ch1/files_only folder, see it for yourself:
$ tree -v files_only
This shows us a completely different picture:
files_only/
├── core.py
├── db.py
├── math.py
├── network.py
└── run.py
It is a little harder to guess what each module does, right? Now, consider that this is just a simple example, so you can guess how much harder it would be to understand a real application if we couldn't organize the code in packages and modules.
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How do we use modules and packages
When a developer is writing an application, it is very likely that they will need to apply the same piece of logic in different parts of it. For example, when writing a parser for the data that comes from a form that a user can fill in a web page, the application will have to validate whether a certain field is holding a number or not. Regardless of how the logic for this kind of validation is written, it's very likely that it will be needed in more than one place. For example in a poll application, where the user is asked many question, it's likely that several of them will require a numeric answer. For example:
???????? What is your age
???????? How many pets do you own
???????? How many children do you have
???????? How many times have you been married
It would be very bad practice to copy paste (or, more properly said: duplicate) the validation logic in every place where we expect a numeric answer. This would violate the DRY (Don't Repeat Yourself) principle, which states that you should never repeat the same piece of code more than once in your application. I feel the need to stress the importance of this principle: you should never repeat the same piece of code more than once in your application (got the irony?).
There are several reasons why repeating the same piece of logic can be very bad, the most important ones being:
???????? There could be a bug in the logic, and therefore, you would have to correct it in every place that logic is applied.
???????? You may want to amend the way you carry out the validation, and again you would have to change it in every place it is applied.
???????? You may forget to fix/amend a piece of logic because you missed it when searching for all its occurrences. This would leave wrong/inconsistent behavior in your application.
???????? Your code would be longer than needed, for no good reason.
Python is a wonderful language and provides you with all the tools you need to apply all the coding best practices. For this particular example, we need to be able to reuse a piece of code. To be able to reuse a piece of code, we need to have a construct that will hold the code for us so that we can call that construct every time we need to repeat the logic inside it. That construct exists, and it's called function.
I'm not going too deep into the specifics here, so please just remember that a function
is a block of organized, reusable code which is used to perform a task. Functions can assume many forms and names, according to what kind of environment they belong to, but for now this is not important. We'll see the details when we are able to appreciate them, later on, in the module. Functions are the building blocks of modularity in your application, and they are almost indispensable (unless you're writing a super simple script, you'll use functions all the time).
Python comes with a very extensive library, as I already said a few pages ago. Now, maybe it's a good time to define what a library is: a library is a collection of functions and objects that provide functionalities that enrich the abilities of a language.
For example, within Python's math library we can find a plethora of functions, one of which is the factorial function, which of course calculates the factorial of a number.
?So, if you wanted to use this function in your code, all you would have to do is to import it and call it with the right input values. Don't worry too much if input values and the concept of calling is not very clear for now, please just concentrate on the import part.
In Python, to calculate the factorial of number 5, we just need the following code:
>>> from math import factorial
>>> factorial(5) 120
In our example, the package util is our utility library. Our custom utility belt that holds all those reusable tools (that is, functions), which we need in our application. Some of them will deal with databases (db.py), some with the network (network.py), and some will perform mathematical calculations (math.py) that are outside the scope of Python's standard math library and therefore, we had to code them for ourselves.
Let's now talk about another very important concept: Python's execution model.
Pythons execution model
In this paragraph, I would like to introduce you to a few very important concepts, such as scope, names, and namespaces. You can read all about Python's execution model in the official Language reference, of course, but I would argue that it is quite technical and abstract, so let me give you a less formal explanation first.
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Names and namespaces
Say you are looking for a module, so you go to the library and ask someone for the module you want to fetch. They tell you something like "second floor, section X, row three". So you go up the stairs, look for section X, and so on.
It would be very different to enter a library where all the books are piled together in random order in one big room. No floors, no sections, no rows, no order. Fetching a module would be extremely hard.
When we write code we have the same issue: we have to try and organize it so that it will be easy for someone who has no prior knowledge about it to find what they're looking for. When software is structured correctly, it also promotes code reuse. On the other hand, disorganized software is more likely to expose scattered pieces of duplicated logic.
First of all, let's start with the module. We refer to a module by its title and in Python lingo, that would be a name. Python names are the closest abstraction to what other languages call variables. Names basically refer to objects and are introduced by name binding operations. Let's make a quick example (notice that anything that follows a # is a comment):
>>> n = 3 # integer number
>>> address = "221b Baker Street, NW1 6XE, London" # S. Holmes
>>> employee = {
...???? 'age': 45,
...???? 'role': 'CTO',
...???? 'SSN': 'AB1234567',
... }
>>> # let's print them
>>> n 3
>>> address
'221b Baker Street, NW1 6XE, London'
>>> employee
{'role': 'CTO', 'SSN': 'AB1234567', 'age': 45}
>>> # what if I try to print a name I didn't define?
>>> other_name
Traceback (most recent call last): File "<stdin>", line 1, in <module>
NameError: name 'other_name' is not defined
We defined three objects in the preceding code (do you remember what are the three features every Python object has?):
???????? An integer number n (type: int, value: 3)
???????? A string address (type: str, value: Sherlock Holmes' address)
???????? A dictionary employee (type: dict, value: a dictionary which holds three key/value pairs)
Don't worry, I know you're not supposed to know what a dictionary is. We'll see in the upcoming chapter that it's the king of Python data structures.
So, what are n, address and employee? They are names. Names that we can use to retrieve data within our code. They need to be kept somewhere so that whenever we need to retrieve those objects, we can use their names to fetch them. We need some space to hold them, hence: namespaces!
A namespace is therefore a mapping from names to objects. Examples are the set of built-in names (containing functions that are always accessible for free in any Python program), the global names in a module, and the local names in a function. Even the set of attributes of an object can be considered a namespace.
The beauty of namespaces is that they allow you to define and organize your names with clarity, without overlapping or interference. For example, the namespace associated with that module we were looking for in the library can be used to import the module itself, like this:
from library.second_floor.section_x.row_three import module
We start from the library namespace, and by means of the dot (.) operator, we walk into that namespace. Within this namespace, we look for second_floor, and again we walk into it with the . operator. We then walk into section_x, and finally within the last namespace, row_tree, we find the name we were looking for: module.
Walking through a namespace will be clearer when we'll be dealing with real code examples. For now, just keep in mind that namespaces are places where names are associated to objects.
There is another concept, which is closely related to that of a namespace, which I'd
like to briefly talk about: the scope.
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Scopes
According to Python's documentation, a scope is a textual region of a Python program, where a namespace is directly accessible. Directly accessible means that when you're looking for an unqualified reference to a name, Python tries to find it in the namespace.
Scopes are determined statically, but actually during runtime they are used dynamically. This means that by inspecting the source code you can tell what the scope of an object is, but this doesn't prevent the software to alter that during runtime. There are four different scopes that Python makes accessible (not necessarily all of them present at the same time, of course):
???????? The local scope, which is the innermost one and contains the local names.
???????? The enclosing scope, that is, the scope of any enclosing function. It contains non-local names and also non-global names.
???????? The global scope contains the global names.
???????? The built-in scope contains the built-in names. Python comes with a set of functions that you can use in a off-the-shelf fashion, such as print, all, abs, and so on. They live in the built-in scope.
The rule is the following: when we refer to a name, Python starts looking for it in the current namespace. If the name is not found, Python continues the search to the enclosing scope and this continue until the built-in scope is searched. If a name
hasn't been found after searching the built-in scope, then Python raises a NameError exception, which basically means that the name hasn't been defined (you saw this in the preceding example).
The order in which the namespaces are scanned when looking for a name is therefore: local, enclosing, global, built-in (LEGB).
This is all very theoretical, so let's see an example. In order to show you Local and Enclosing namespaces, I will have to define a few functions. Just remember that in the following code, when you see def, it means I'm defining a function.
# Local versus Global
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# we define a function, called local def local():
m = 7
print(m)
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m = 5
print(m)
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# we call, or execute the function local local()
In the preceding example, we define the same name m, both in the global scope and in the local one (the one defined by the function local). When we execute this program with the following command (have you activated your virtualenv?):
$ python scopes1.py
We see two numbers printed on the console: 5 and 7.
What happens is that the Python interpreter parses the file, top to bottom. First, it finds a couple of comment lines, which are skipped, then it parses the definition of the function local. When called, this function does two things: it sets up a name to an object representing number 7 and prints it. The Python interpreter keeps going and it finds another name binding. This time the binding happens in the global scope and the value is 5. The next line is a call to the print function, which is executed (and so we get the first value printed on the console: 5).
After this, there is a call to the function local. At this point, Python executes the function, so at this time, the binding m = 7 happens and it's printed.
One very important thing to notice is that the part of the code that belongs to the definition of the function local is indented by four spaces on the right. Python in fact defines scopes by indenting the code. You walk into a scope by indenting and walk out of it by unindenting. Some coders use two spaces, others three, but the suggested number of spaces to use is four. It's a good measure to maximize readability. We'll talk more about all the conventions you should embrace when writing Python code later.
What would happen if we removed that m = 7 line? Remember the LEGB rule. Python would start looking for m in the local scope (function local), and, not finding it, it would go to the next enclosing scope. The next one in this case is the global one because there is no enclosing function wrapped around local. Therefore, we would see two number 5 printed on the console. Let's actually see how the code would
look like:
# Local versus Global
def local():
# m doesn't belong to the scope defined by the local function # so Python will keep looking into the next enclosing scope. # m is finally found in the global scope
print(m, 'printing from the local scope')
m = 5
print(m, 'printing from the global scope') local()
Running scopes2.py will print this:
(.lpvenv)fab@xps:ch1$ python scopes2.py
5 printing from the global scope
5 printing from the local scope
As expected, Python prints m the first time, then when the function local is called, m isn't found in its scope, so Python looks for it following the LEGB chain until m is found in the global scope.
Let's see an example with an extra layer, the enclosing scope:
# Local, Enclosing and Global
def enclosing_func():
m = 13
def local():
# m doesn't belong to the scope defined by the local # function so Python will keep looking into the next
# enclosing scope. This time m is found in the enclosing # scope
print(m, 'printing from the local scope')
# calling the function local local()
m = 5
print(m, 'printing from the global scope') enclosing_func()
Running scopes3.py will print on the console:
(.lpvenv)fab@xps:ch1$ python scopes3.py
5 printing from the global scope
13 printing from the local scope
As you can see, the print instruction from the function local is referring to m as before. m is still not defined within the function itself, so Python starts walking scopes following the LEGB order. This time m is found in the enclosing scope.
Don't worry if this is still not perfectly clear for now. It will come to you as we go through the examples in the module. The Classes section of the Python tutorial (official documentation) has an interesting paragraph about scopes and namespaces. Make sure you read it at some point if you wish for a deeper understanding of the subject.
Before we finish off this chapter, I would like to talk a bit more about objects. After all, basically everything in Python is an object, so I think they deserve a bit more attention.
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Guidelines on how to write good code
Writing good code is not as easy as it seems. As I already said before, good code exposes a long list of qualities that is quite hard to put together. Writing good code is, to some extent, an art. Regardless of where on the path you will be happy to settle, there is something that you can embrace which will make your code instantly better: PEP8.
According to Wikipedia:
"Python's development is conducted largely through the Python Enhancement Proposal (PEP) process. The PEP process is the primary mechanism for proposing major new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python."
Among all the PEPs, probably the most famous one is PEP8. It lays out a simple but effective set of guidelines to define Python aesthetic so that we write beautiful Python code. If you take one suggestion out of this chapter, please let it be this: use it. Embrace it. You will thank me later.
Coding today is no longer a check-in/check-out business. Rather, it's more of a social effort. Several developers collaborate to a piece of code through tools like git and mercurial, and the result is code that is fathered by many different hands.
These days, more than ever, we need to have a consistent way of writing code, so that readability is maximized. When all developers of a company abide with PEP8, it's not uncommon for any of them landing on a piece of code to think they wrote it themselves. It actually happens to me all the time (I always forget the code I write).
This has a tremendous advantage: when you read code that you could have written yourself, you read it easily. Without a convention, every coder would structure the code the way they like most, or simply the way they were taught or are used to, and this would mean having to interpret every line according to someone else's style. It would mean having to lose much more time just trying to understand it. Thanks to PEP8, we can avoid this. I'm such a fan of it that I won't sign off a code review if the code doesn't respect it. So please take the time to study it, it's very important.
In the examples of this module, I will try to respect it as much as I can. Unfortunately, I don't have the luxury of 79 characters (which is the maximum line length suggested by PEP*), and I will have to cut down on blank lines and other things, but I promise you I'll try to layout my code so that it's as readable as possible.
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The Python culture
Python has been adopted widely in all coding industries. It's used by many different companies for many different purposes, and it's also used in education (it's an excellent language for that purpose, because of its many qualities and the fact that it's easy to learn).
One of the reasons Python is so popular today is that the community around it is vast, vibrant, and full of brilliant people. Many events are organized all over the world, mostly either around Python or its main web framework, Django.
Python is open, and very often so are the minds of those who embrace it. Check out
the community page on the Python website for more information and get involved!
There is another aspect to Python which revolves around the notion of being Pythonic. It has to do with the fact that Python allows you to use some idioms that aren't found elsewhere, at least not in the same form or easiness of use (I feel quite claustrophobic when I have to code in a language which is not Python now).
Anyway, over the years, this concept of being Pythonic has emerged and, the way I understand it, is something along the lines of doing things the way they are supposed to be done in Python.
To help you understand a little bit more about Python's culture and about being Pythonic, I will show you the Zen of Python. A lovely Easter egg that is very popular.
Open up a Python console and type import this. What follows is the result of this line:
>>> import this
The Zen of Python, by Tim Peters Beautiful is better than ugly.
Explicit is better than implicit. Simple is better than complex.
Complex is better than complicated. Flat is better than nested.
Sparse is better than dense. Readability counts.
Special cases aren't special enough to break the rules. Although practicality beats purity.
Errors should never pass silently. Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!
There are two levels of reading here. One is to consider it as a set of guidelines that have been put down in a fun way. The other one is to keep it in mind, and maybe read it once in a while, trying to understand how it refers to something deeper.
Some Python characteristics that you will have to understand deeply in order to write Python the way it's supposed to be written. Start with the fun level, and then dig deeper. Always dig deeper.
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A note on the IDEs
Just a few words about Integrated Development Environments (IDEs). To follow the examples in this module you don't need one, any text editor will do fine. If you want to have more advanced features such as syntax coloring and auto completion, you will have to fetch yourself an IDE. You can find a comprehensive list of open source IDEs (just Google "python ides") on the Python website. I personally use Sublime Text editor. It's free to try out and it costs just a few dollars. I have tried many IDEs in my life, but this is the one that makes me most productive.
Two extremely important pieces of advice:
???????? Whatever IDE you will chose to use, try to learn it well so that you can exploit its strengths, but don't depend on it. Exercise yourself to work with VIM (or any other text editor) once in a while, learn to be able to do some work on any platform, with any set of tools.
???????? Whatever text editor/IDE you will use, when it comes to writing Python, indentation is four spaces. Don't use tabs, don't mix them with spaces. Use four spaces, not two, not three, not five. Just use four. The whole world works like that, and you don't want to become an outcast because you were fond of the three-space layout.