Sunday, August 20, 2017

Parsing values from a JSON file?

import json
from pprint import pprint

with open('data.json') as data_file:    
    data = json.load(data_file)

pprint(data)

Saturday, August 5, 2017

quản lý phần mềm của python: pip

Modules

If you quit from the Python interpreter and enter it again, the definitions you have made (functions and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off using a text editor to prepare the input for the interpreter and running it with that file as input instead. This is known as creating a script. As your program gets longer, you may want to split it into several files for easier maintenance. You may also want to use a handy function that you’ve written in several programs without copying its definition into each program.
To support this, Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter. Such a file is called a module; definitions from a module can be imported into other modules or into the main module (the collection of variables that you have access to in a script executed at the top level and in calculator mode).
A module is a file containing Python definitions and statements. The file name is the module name with the suffix .py appended. Within a module, the module’s name (as a string) is available as the value of the global variable __name__. For instance, use your favorite text editor to create a file called fibo.py in the current directory with the following contents:
# Fibonacci numbers module

def fib(n):    # write Fibonacci series up to n
    a, b = 0, 1
    while b < n:
        print(b, end=' ')
        a, b = b, a+b
    print()

def fib2(n):   # return Fibonacci series up to n
    result = []
    a, b = 0, 1
    while b < n:
        result.append(b)
        a, b = b, a+b
    return result
Now enter the Python interpreter and import this module with the following command:
>>>
>>> import fibo
This does not enter the names of the functions defined in fibo directly in the current symbol table; it only enters the module name fibo there. Using the module name you can access the functions:
>>>
>>> fibo.fib(1000)
1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
>>> fibo.fib2(100)
[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
>>> fibo.__name__
'fibo'
If you intend to use a function often you can assign it to a local name:
>>>
>>> fib = fibo.fib
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377

6.1. More on Modules

A module can contain executable statements as well as function definitions. These statements are intended to initialize the module. They are executed only the first time the module name is encountered in an import statement. [1] (They are also run if the file is executed as a script.)
Each module has its own private symbol table, which is used as the global symbol table by all functions defined in the module. Thus, the author of a module can use global variables in the module without worrying about accidental clashes with a user’s global variables. On the other hand, if you know what you are doing you can touch a module’s global variables with the same notation used to refer to its functions, modname.itemname.
Modules can import other modules. It is customary but not required to place all import statements at the beginning of a module (or script, for that matter). The imported module names are placed in the importing module’s global symbol table.
There is a variant of the import statement that imports names from a module directly into the importing module’s symbol table. For example:
>>>
>>> from fibo import fib, fib2
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This does not introduce the module name from which the imports are taken in the local symbol table (so in the example, fibo is not defined).
There is even a variant to import all names that a module defines:
>>>
>>> from fibo import *
>>> fib(500)
1 1 2 3 5 8 13 21 34 55 89 144 233 377
This imports all names except those beginning with an underscore (_). In most cases Python programmers do not use this facility since it introduces an unknown set of names into the interpreter, possibly hiding some things you have already defined.
Note that in general the practice of importing * from a module or package is frowned upon, since it often causes poorly readable code. However, it is okay to use it to save typing in interactive sessions.
Note
For efficiency reasons, each module is only imported once per interpreter session. Therefore, if you change your modules, you must restart the interpreter – or, if it’s just one module you want to test interactively, use importlib.reload(), e.g. import importlib; importlib.reload(modulename).

6.1.1. Executing modules as scripts

When you run a Python module with
python fibo.py <arguments>
the code in the module will be executed, just as if you imported it, but with the __name__ set to "__main__". That means that by adding this code at the end of your module:
if __name__ == "__main__":
    import sys
    fib(int(sys.argv[1]))
you can make the file usable as a script as well as an importable module, because the code that parses the command line only runs if the module is executed as the “main” file:
$ python fibo.py 50
1 1 2 3 5 8 13 21 34
If the module is imported, the code is not run:
>>>
>>> import fibo
>>>
This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite).

6.1.2. The Module Search Path

When a module named spam is imported, the interpreter first searches for a built-in module with that name. If not found, it then searches for a file named spam.py in a list of directories given by the variable sys.path. sys.path is initialized from these locations:
  • The directory containing the input script (or the current directory when no file is specified).
  • PYTHONPATH (a list of directory names, with the same syntax as the shell variable PATH).
  • The installation-dependent default.
Note
On file systems which support symlinks, the directory containing the input script is calculated after the symlink is followed. In other words the directory containing the symlink is not added to the module search path.
After initialization, Python programs can modify sys.path. The directory containing the script being run is placed at the beginning of the search path, ahead of the standard library path. This means that scripts in that directory will be loaded instead of modules of the same name in the library directory. This is an error unless the replacement is intended. See section Standard Modules for more information.

6.1.3. “Compiled” Python files

To speed up loading modules, Python caches the compiled version of each module in the __pycache__ directory under the name module.version.pyc, where the version encodes the format of the compiled file; it generally contains the Python version number. For example, in CPython release 3.3 the compiled version of spam.py would be cached as __pycache__/spam.cpython-33.pyc. This naming convention allows compiled modules from different releases and different versions of Python to coexist.
Python checks the modification date of the source against the compiled version to see if it’s out of date and needs to be recompiled. This is a completely automatic process. Also, the compiled modules are platform-independent, so the same library can be shared among systems with different architectures.
Python does not check the cache in two circumstances. First, it always recompiles and does not store the result for the module that’s loaded directly from the command line. Second, it does not check the cache if there is no source module. To support a non-source (compiled only) distribution, the compiled module must be in the source directory, and there must not be a source module.
Some tips for experts:
  • You can use the -O or -OO switches on the Python command to reduce the size of a compiled module. The -O switch removes assert statements, the -OO switch removes both assert statements and __doc__ strings. Since some programs may rely on having these available, you should only use this option if you know what you’re doing. “Optimized” modules have an opt- tag and are usually smaller. Future releases may change the effects of optimization.
  • A program doesn’t run any faster when it is read from a .pyc file than when it is read from a .py file; the only thing that’s faster about .pyc files is the speed with which they are loaded.
  • The module compileall can create .pyc files for all modules in a directory.
  • There is more detail on this process, including a flow chart of the decisions, in PEP 3147.

6.2. Standard Modules

Python comes with a library of standard modules, described in a separate document, the Python Library Reference (“Library Reference” hereafter). Some modules are built into the interpreter; these provide access to operations that are not part of the core of the language but are nevertheless built in, either for efficiency or to provide access to operating system primitives such as system calls. The set of such modules is a configuration option which also depends on the underlying platform. For example, the winreg module is only provided on Windows systems. One particular module deserves some attention: sys, which is built into every Python interpreter. The variables sys.ps1 and sys.ps2 define the strings used as primary and secondary prompts:
>>>
>>> import sys
>>> sys.ps1
'>>> '
>>> sys.ps2
'... '
>>> sys.ps1 = 'C> '
C> print('Yuck!')
Yuck!
C>
These two variables are only defined if the interpreter is in interactive mode.
The variable sys.path is a list of strings that determines the interpreter’s search path for modules. It is initialized to a default path taken from the environment variable PYTHONPATH, or from a built-in default if PYTHONPATH is not set. You can modify it using standard list operations:
>>>
>>> import sys
>>> sys.path.append('/ufs/guido/lib/python')

6.3. The dir() Function

The built-in function dir() is used to find out which names a module defines. It returns a sorted list of strings:
>>>
>>> import fibo, sys
>>> dir(fibo)
['__name__', 'fib', 'fib2']
>>> dir(sys)  
['__displayhook__', '__doc__', '__excepthook__', '__loader__', '__name__',
 '__package__', '__stderr__', '__stdin__', '__stdout__',
 '_clear_type_cache', '_current_frames', '_debugmallocstats', '_getframe',
 '_home', '_mercurial', '_xoptions', 'abiflags', 'api_version', 'argv',
 'base_exec_prefix', 'base_prefix', 'builtin_module_names', 'byteorder',
 'call_tracing', 'callstats', 'copyright', 'displayhook',
 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix',
 'executable', 'exit', 'flags', 'float_info', 'float_repr_style',
 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags',
 'getfilesystemencoding', 'getobjects', 'getprofile', 'getrecursionlimit',
 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettotalrefcount',
 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info',
 'intern', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path',
 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1',
 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit',
 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout',
 'thread_info', 'version', 'version_info', 'warnoptions']
Without arguments, dir() lists the names you have defined currently:
>>>
>>> a = [1, 2, 3, 4, 5]
>>> import fibo
>>> fib = fibo.fib
>>> dir()
['__builtins__', '__name__', 'a', 'fib', 'fibo', 'sys']
Note that it lists all types of names: variables, modules, functions, etc.
dir() does not list the names of built-in functions and variables. If you want a list of those, they are defined in the standard module builtins:
>>>
>>> import builtins
>>> dir(builtins)  
['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException',
 'BlockingIOError', 'BrokenPipeError', 'BufferError', 'BytesWarning',
 'ChildProcessError', 'ConnectionAbortedError', 'ConnectionError',
 'ConnectionRefusedError', 'ConnectionResetError', 'DeprecationWarning',
 'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False',
 'FileExistsError', 'FileNotFoundError', 'FloatingPointError',
 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError',
 'ImportWarning', 'IndentationError', 'IndexError', 'InterruptedError',
 'IsADirectoryError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
 'MemoryError', 'NameError', 'None', 'NotADirectoryError', 'NotImplemented',
 'NotImplementedError', 'OSError', 'OverflowError',
 'PendingDeprecationWarning', 'PermissionError', 'ProcessLookupError',
 'ReferenceError', 'ResourceWarning', 'RuntimeError', 'RuntimeWarning',
 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError',
 'SystemExit', 'TabError', 'TimeoutError', 'True', 'TypeError',
 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError',
 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning',
 'ValueError', 'Warning', 'ZeroDivisionError', '_', '__build_class__',
 '__debug__', '__doc__', '__import__', '__name__', '__package__', 'abs',
 'all', 'any', 'ascii', 'bin', 'bool', 'bytearray', 'bytes', 'callable',
 'chr', 'classmethod', 'compile', 'complex', 'copyright', 'credits',
 'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'exec', 'exit',
 'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr',
 'hash', 'help', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass',
 'iter', 'len', 'license', 'list', 'locals', 'map', 'max', 'memoryview',
 'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property',
 'quit', 'range', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice',
 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'vars',
 'zip']

6.4. Packages

Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name A.B designates a submodule named B in a package named A. Just like the use of modules saves the authors of different modules from having to worry about each other’s global variable names, the use of dotted module names saves the authors of multi-module packages like NumPy or the Python Imaging Library from having to worry about each other’s module names.
Suppose you want to design a collection of modules (a “package”) for the uniform handling of sound files and sound data. There are many different sound file formats (usually recognized by their extension, for example: .wav, .aiff, .au), so you may need to create and maintain a growing collection of modules for the conversion between the various file formats. There are also many different operations you might want to perform on sound data (such as mixing, adding echo, applying an equalizer function, creating an artificial stereo effect), so in addition you will be writing a never-ending stream of modules to perform these operations. Here’s a possible structure for your package (expressed in terms of a hierarchical filesystem):
sound/                          Top-level package
      __init__.py               Initialize the sound package
      formats/                  Subpackage for file format conversions
              __init__.py
              wavread.py
              wavwrite.py
              aiffread.py
              aiffwrite.py
              auread.py
              auwrite.py
              ...
      effects/                  Subpackage for sound effects
              __init__.py
              echo.py
              surround.py
              reverse.py
              ...
      filters/                  Subpackage for filters
              __init__.py
              equalizer.py
              vocoder.py
              karaoke.py
              ...
When importing the package, Python searches through the directories on sys.path looking for the package subdirectory.
The __init__.py files are required to make Python treat the directories as containing packages; this is done to prevent directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path. In the simplest case, __init__.py can just be an empty file, but it can also execute initialization code for the package or set the __all__ variable, described later.
Users of the package can import individual modules from the package, for example:
import sound.effects.echo
This loads the submodule sound.effects.echo. It must be referenced with its full name.
sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)
An alternative way of importing the submodule is:
from sound.effects import echo
This also loads the submodule echo, and makes it available without its package prefix, so it can be used as follows:
echo.echofilter(input, output, delay=0.7, atten=4)
Yet another variation is to import the desired function or variable directly:
from sound.effects.echo import echofilter
Again, this loads the submodule echo, but this makes its function echofilter() directly available:
echofilter(input, output, delay=0.7, atten=4)
Note that when using from package import item, the item can be either a submodule (or subpackage) of the package, or some other name defined in the package, like a function, class or variable. The import statement first tests whether the item is defined in the package; if not, it assumes it is a module and attempts to load it. If it fails to find it, an ImportError exception is raised.
Contrarily, when using syntax like import item.subitem.subsubitem, each item except for the last must be a package; the last item can be a module or a package but can’t be a class or function or variable defined in the previous item.

6.4.1. Importing * From a Package

Now what happens when the user writes from sound.effects import *? Ideally, one would hope that this somehow goes out to the filesystem, finds which submodules are present in the package, and imports them all. This could take a long time and importing sub-modules might have unwanted side-effects that should only happen when the sub-module is explicitly imported.
The only solution is for the package author to provide an explicit index of the package. The import statement uses the following convention: if a package’s __init__.py code defines a list named __all__, it is taken to be the list of module names that should be imported when from package import * is encountered. It is up to the package author to keep this list up-to-date when a new version of the package is released. Package authors may also decide not to support it, if they don’t see a use for importing * from their package. For example, the file sound/effects/__init__.py could contain the following code:
__all__ = ["echo", "surround", "reverse"]
This would mean that from sound.effects import * would import the three named submodules of the sound package.
If __all__ is not defined, the statement from sound.effects import * does not import all submodules from the package sound.effects into the current namespace; it only ensures that the package sound.effects has been imported (possibly running any initialization code in __init__.py) and then imports whatever names are defined in the package. This includes any names defined (and submodules explicitly loaded) by __init__.py. It also includes any submodules of the package that were explicitly loaded by previous import statements. Consider this code:
import sound.effects.echo
import sound.effects.surround
from sound.effects import *
In this example, the echo and surround modules are imported in the current namespace because they are defined in the sound.effects package when the from...import statement is executed. (This also works when __all__ is defined.)
Although certain modules are designed to export only names that follow certain patterns when you use import *, it is still considered bad practice in production code.
Remember, there is nothing wrong with using from Package import specific_submodule! In fact, this is the recommended notation unless the importing module needs to use submodules with the same name from different packages.

6.4.2. Intra-package References

When packages are structured into subpackages (as with the sound package in the example), you can use absolute imports to refer to submodules of siblings packages. For example, if the module sound.filters.vocoder needs to use the echo module in the sound.effects package, it can use from sound.effects import echo.
You can also write relative imports, with the from module import name form of import statement. These imports use leading dots to indicate the current and parent packages involved in the relative import. From the surround module for example, you might use:
from . import echo
from .. import formats
from ..filters import equalizer
Note that relative imports are based on the name of the current module. Since the name of the main module is always "__main__", modules intended for use as the main module of a Python application must always use absolute imports.

6.4.3. Packages in Multiple Directories

Packages support one more special attribute, __path__. This is initialized to be a list containing the name of the directory holding the package’s __init__.py before the code in that file is executed. This variable can be modified; doing so affects future searches for modules and subpackages contained in the package.
While this feature is not often needed, it can be used to extend the set of modules found in a package.
Footnotes
[1]In fact function definitions are also ‘statements’ that are ‘executed’; the execution of a module-level function definition enters the function name in the module’s global symbol table.

Standard library

 How to get a list of built-in modules in python?

---------------
The compiled-in module names are in sys.builtin_module_names. For all importable modules, see pkgutil.iter_modules.
Run these in a clean virtualenv to get (almost) only the modules that come with Python itself.

Note that a “popularity poll” will necessarily include modules that use old, discouraged naming conventions because they were written before today's guidelines were put in place, and can't change because need to be backwards compatible. It might be useful for something, but not for answering best-practice questions such as “How should I name my functions?”. For that, see the PEP8, the Python style guide, especially the “Naming Conventions” section.

Python - Environment Setup

Python is available on a wide variety of platforms including Linux and Mac OS X. Let's understand how to set up our Python environment.

Local Environment Setup

Open a terminal window and type "python" to find out if it is already installed and which version is installed.
  • Unix (Solaris, Linux, FreeBSD, AIX, HP/UX, SunOS, IRIX, etc.)
  • Win 9x/NT/2000
  • Macintosh (Intel, PPC, 68K)
  • OS/2
  • DOS (multiple versions)
  • PalmOS
  • Nokia mobile phones
  • Windows CE
  • Acorn/RISC OS
  • BeOS
  • Amiga
  • VMS/OpenVMS
  • QNX
  • VxWorks
  • Psion
  • Python has also been ported to the Java and .NET virtual machines

Getting Python

The most up-to-date and current source code, binaries, documentation, news, etc., is available on the official website of Python https://www.python.org/
You can download Python documentation from https://www.python.org/doc/. The documentation is available in HTML, PDF, and PostScript formats.

Installing Python

Python distribution is available for a wide variety of platforms. You need to download only the binary code applicable for your platform and install Python.
If the binary code for your platform is not available, you need a C compiler to compile the source code manually. Compiling the source code offers more flexibility in terms of choice of features that you require in your installation.
Here is a quick overview of installing Python on various platforms −

Unix and Linux Installation

Here are the simple steps to install Python on Unix/Linux machine.
  • Open a Web browser and go to https://www.python.org/downloads/.
  • Follow the link to download zipped source code available for Unix/Linux.
  • Download and extract files.
  • Editing the Modules/Setup file if you want to customize some options.
  • run ./configure script
  • make
  • make install
This installs Python at standard location /usr/local/bin and its libraries at /usr/local/lib/pythonXX where XX is the version of Python.

Windows Installation

Here are the steps to install Python on Windows machine.
  • Open a Web browser and go to https://www.python.org/downloads/.
  • Follow the link for the Windows installer python-XYZ.msi file where XYZ is the version you need to install.
  • To use this installer python-XYZ.msi, the Windows system must support Microsoft Installer 2.0. Save the installer file to your local machine and then run it to find out if your machine supports MSI.
  • Run the downloaded file. This brings up the Python install wizard, which is really easy to use. Just accept the default settings, wait until the install is finished, and you are done.

Macintosh Installation

Recent Macs come with Python installed, but it may be several years out of date. See http://www.python.org/download/mac/ for instructions on getting the current version along with extra tools to support development on the Mac. For older Mac OS's before Mac OS X 10.3 (released in 2003), MacPython is available.
Jack Jansen maintains it and you can have full access to the entire documentation at his website − http://www.cwi.nl/~jack/macpython.html. You can find complete installation details for Mac OS installation.

Setting up PATH

Programs and other executable files can be in many directories, so operating systems provide a search path that lists the directories that the OS searches for executables.
The path is stored in an environment variable, which is a named string maintained by the operating system. This variable contains information available to the command shell and other programs.
The path variable is named as PATH in Unix or Path in Windows (Unix is casesensitive; Windows is not).
In Mac OS, the installer handles the path details. To invoke the Python interpreter from any particular directory, you must add the Python directory to your path.

Setting path at Unix/Linux

To add the Python directory to the path for a particular session in Unix −
  • In the csh shell − type setenv PATH "$PATH:/usr/local/bin/python" and press Enter.
  • In the bash shell (Linux) − type export ATH="$PATH:/usr/local/bin/python" and press Enter.
  • In the sh or ksh shell − type PATH="$PATH:/usr/local/bin/python" and press Enter.
  • Note − /usr/local/bin/python is the path of the Python directory

Setting path at Windows

To add the Python directory to the path for a particular session in Windows −
At the command prompt − type path %path%;C:\Python and press Enter.
Note − C:\Python is the path of the Python directory

Python Environment Variables

Here are important environment variables, which can be recognized by Python −
S.No. Variable & Description
1 PYTHONPATH
It has a role similar to PATH. This variable tells the Python interpreter where to locate the module files imported into a program. It should include the Python source library directory and the directories containing Python source code. PYTHONPATH is sometimes preset by the Python installer.
2 PYTHONSTARTUP
It contains the path of an initialization file containing Python source code. It is executed every time you start the interpreter. It is named as .pythonrc.py in Unix and it contains commands that load utilities or modify PYTHONPATH.
3 PYTHONCASEOK
It is used in Windows to instruct Python to find the first case-insensitive match in an import statement. Set this variable to any value to activate it.
4 PYTHONHOME
It is an alternative module search path. It is usually embedded in the PYTHONSTARTUP or PYTHONPATH directories to make switching module libraries easy.

Running Python

There are three different ways to start Python −

Interactive Interpreter

You can start Python from Unix, DOS, or any other system that provides you a command-line interpreter or shell window.
Enter python the command line.
Start coding right away in the interactive interpreter.
$python # Unix/Linux
or
python% # Unix/Linux
or
C:> python # Windows/DOS
Here is the list of all the available command line options −
S.No. Option & Description
1 -d
It provides debug output.
2 -O
It generates optimized bytecode (resulting in .pyo files).
3 -S
Do not run import site to look for Python paths on startup.
4 -v
verbose output (detailed trace on import statements).
5 -X
disable class-based built-in exceptions (just use strings); obsolete starting with version 1.6.
6 -c cmd
run Python script sent in as cmd string
7 file
run Python script from given file

Script from the Command-line

A Python script can be executed at command line by invoking the interpreter on your application, as in the following −
$python script.py # Unix/Linux

or

python% script.py # Unix/Linux

or 

C: >python script.py # Windows/DOS
Note − Be sure the file permission mode allows execution.

Integrated Development Environment

You can run Python from a Graphical User Interface (GUI) environment as well, if you have a GUI application on your system that supports Python.
  • Unix − IDLE is the very first Unix IDE for Python.
  • Windows − PythonWin is the first Windows interface for Python and is an IDE with a GUI.
  • Macintosh − The Macintosh version of Python along with the IDLE IDE is available from the main website, downloadable as either MacBinary or BinHex'd files.
If you are not able to set up the environment properly, then you can take help from your system admin. Make sure the Python environment is properly set up and working perfectly fine.
Note − All the examples given in subsequent chapters are executed with Python 2.4.3 version available on CentOS flavor of Linux.
We already have set up Python Programming environment online, so that you can execute all the available examples online at the same time when you are learning theory. Feel free to modify any example and execute it online.

Thursday, August 3, 2017

Learn Python Programming The Definitive Guide: What is Python (Programming)? - The Basics


Before getting started, lets get familiarized with the language first.
Python is a general-purpose language. It has wide range of applications from Web development (like: Django and Bottle), scientific and mathematical computing (Orange, SymPy, NumPy) to desktop graphical user Interfaces (Pygame, Panda3D).
The syntax of the language is clean and length of the code is relatively short. It's fun to work in Python because it allows you to think about the problem rather than focusing on the syntax.
More information on Python Language:

History of Python

Features of Python Programming

Applications of Python


4 Reasons to Choose Python as First Language

https://www.programiz.com/

  1. Simple Elegant Syntax

    Programming in Python is fun. It's easier to understand and write Python code. Why? The syntax feels natural. Take this source code for an example:
    a = 2
    b = 3
    sum = a + b
    print(sum)

    Even if you have never programmed before, you can easily guess that this program adds two numbers and prints it.
  2. Not overly strict

    You don't need to define the type of a variable in Python. Also, it's not necessary to add semicolon at the end of the statement.

    Python enforces you to follow good practices (like proper indentation). These small things can make learning much easier for beginners.
  3. Expressiveness of the language

    Python allows you to write programs having greater functionality with fewer lines of code. Here's a link to the source code of Tic-tac-toe game with a graphical interface and a smart computer opponent in less than 500 lines of code. This is just an example. You will be amazed how much you can do with Python once you learn the basics.
  4. Great Community and Support

    Python has a large supporting community. There are numerous active forums online which can be handy if you are stuck. Some of them are:

Run Python on Your Operating System

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You will find the easiest way to run Python on your computer (Windows, Mac OS X or Linux) in this section.

Install and Run Python in Mac OS X

Install and Run Python in Linux (Ubuntu)

  1. Install the following dependencies:
    $ sudo apt-get install build-essential checkinstall
    $ sudo apt-get install libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev
  2. Go to Download Python page on the official site and click Download Python 3.6.0 (You may see different version name).
  3. In the terminal, go to the directory where the file is downloaded and run the command:
    $ tar -xvf Python-3.6.0.tgz
    This will extract your zipped file. Note: The filename will be different if you've downloaded a different version. Use the appropriate filename.
  4. Go to the extracted directory.
    $ cd Python-3.6.0
  5. Issue the following commands to compile Python source code on your Operating system.
    $ ./configure
    $ make
    $ make install
    
  6. We recommend you to install Sublime Text if you are a newbie. To install Sublime Text in Ubuntu (on 14.04). Issue following commands.
    $ sudo add-apt-repository -y ppa:webupd8team/sublime-text-2
    $ sudo apt-get update
    $ sudo apt-get install sublime-text
  7. Open Sublime text. To create a new file, go to File > New File (Shortcut: Ctrl+N).
  8. Save the file with .py file extension like: hello.py or first-program.py
  9. Write the code and save it (Ctrl+S or File > Save) . For starters, you can copy the code below:
    print("Hello, World!")
    This simple program outputs "Hello, World!"
  10. Go to Tool > Build (Shortcut: Ctrl+B). You will see the output at the bottom of Sublime Text. Congratulations, you've successfully run your first Python program.

Install and Run Python in Windows

  1. Go to Download Python page on the official site and click Download Python 3.6.0 (You may see different version name).
  2. When the download is completed, double-click the file and follow the instructions to install it.
    When Python is installed, a program called IDLE is also installed along with it. It provides graphical user interface to work with Python.
  3. Open IDLE, copy the following code below and press enter.
    print("Hello, World!")
    
  4. To create a file in IDLE, go to File > New Window (Shortcut: Ctrl+N).
  5. Write Python code (you can copy the code below for now) and save (Shortcut: Ctrl+S) with .py file extension like: hello.py or your-first-program.py
    print("Hello, World!")
  6. Go to Run > Run module (Shortcut: F5) and you can see the output. Congratulations, you've successfully run your first Python program.

Your First Python Program

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Often, a program called "Hello, World!" is used to introduce a new programming language to beginners. A "Hello, World!" is a simple program that outputs "Hello, World!".
However, Python is one of the easiest language to learn, and creating "Hello, World!" program is as simple as writing print("Hello, World!"). So, we are going to write a different program.

Program to Add Two Numbers

How this program works?

Line 1: # Add two numbers
Any line starting with # in Python programming is a comment.
Comments are used in programming to describe the purpose of the code. This helps you as well as other programmers to understand the intent of the code. Comments are completely ignored by compilers and interpreters.
Line 2: num1 = 3
Here, num1 is a variable. You can store a value in a variable. Here, 3 is stored in this variable.
Line 3: num2 = 5
Similarly, 5 is stored in num2 variable.
Line 4: sum = num1+num2
The variables num1 and num2 are added using + operator. The result of addition is then stored in another variable sum.
Line 5: print(sum)
The print() function prints the output to the screen. In our case, it prints 8 on the screen.

Few Important Things to Remember

To represent a statement in Python, newline (enter) is used. The use of semicolon at the end of the statement is optional (unlike languages like C/C++, JavaScript, PHP). In fact, it's recommended to omit semicolon at the end of the statement in Python.
Instead of curly braces { }, indentations are used to represent a block.
   im_a_parent:
    im_a_child:
        im_a_grand_child
    im_another_child:
        im_another_grand_child

Teach Yourself to Code in Python

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Learn Python from Programiz

Programiz offers dozens of Python tutorials and examples to help you learn Python programming from scratch.
Our tutorials are designed for beginners who do not have any prior knowledge of Python (or, any other programming languages). Each tutorial is written in depth with examples and detailed explanation.
We also encourage you to try our examples and run it. Once you understand the program, modify it and try to create something new. This is the best way to learn programming.

Recommended Books

If you are serious about learning programming, you should get yourself a good book.
Granted, reading a programming book takes a lot of time and patience. But, you will get the big picture of programming concepts in the book which you may not find elsewhere.

Interactive Python Tutorial from DataCamp

Data Science is one field where Python has flourished rapidly in recent years. If you are looking to learn Python for data science, head over to Datacamp and try their free interactive tutorial.

Final Words

Python is a terrific language. The syntax is simple and code length is short which makes is easy to understand and write.
If you are getting started in programming, Python is an awesome choice. You will be amazed how much you can do in Python once you know the basics.
It's easy to overlook the fact that Python is a powerful language. Not only is it good for learning programming, it's also a good language to have in your arsenal. Change your idea into a prototype or create games or get started with data Science, Python can help you in everything to get started.