Examples

• 213

  Getting Started
  213

  Python is a high-level, structured, open-source, dynamically typed programming language that can be used for a wide variety of tasks.

  Two major versions of Python are currently in active use:

  • Python 2.x is the legacy version and will only be updated for security fixes until 2020. No new features will be implemented. Note that many projects still use Python 2 because transitioning is difficult.
  • Python 3.x is the current version and is under active development.

  You can download and install the latest version of Python 2 or 3 here. See Python 2 vs. Python 3 for a comparison of the differences between Python 2 and 3. In addition, some third-parties offer re-packaged versions of Python that add commonly used libraries and other features to ease setup for common use cases—such as math, data analysis or scientific use. See the list at the official site.

  Hello World Python file

  Create a new file hello.py that contains the following line:

  Python 3.x3.0

  print('Hello, World')
  

  Python 2.x2.6

  You can use the Python 3 print function in Python 2 with the following import:

  from __future__ import print_function
  

  Python 2 has a number of functionalities that can be optionally ported from Python 3 using the __future__ module, as discussed here.

  Python 2.x2.7

  If using Python 2, you may also type the line below. Note that this is not valid in Python 3 and thus not recommended because it reduces cross-version code compatibility.

  print 'Hello, World'
  

  Hello, World in Python using IDLE

  IDLE is a simple editor for Python, that comes bundled with Python.

  How to create Hello, World program in IDLE

  • Open IDLE on your system of choice.
    • In older versions of Windows, it lives in All Programs under the Windows menu.
    • In Windows 8+, search for IDLE or find it in the apps that are present in your system.
    • On unix-based (including Mac) systems you can open it from the shell by typing $ idle python_file.py.
  • You will find a shell with options along the top.

  A prompt of three right angle brackets should appear:

  >>>
  

  Now write the following code in the prompt:

  >>> print("Hello, World")
  

  Hit Enter

  >>> print("Hello, World")
  Hello, World
  

  The main purposes of the IDLE are:

  • Multi-window text editor with syntax highlighting, autocompletion, and smart indent
  • Python shell with syntax highlighting
  • Integrated debugger with stepping, persistent breakpoints, and call stack visibility
  • Automatic indentation (useful for beginners learning about Python's indentation)
  • Saving the Python program as .py files and run them and edit them later at any them using IDLE.

  Execute with Python

  Verify that you have Python installed by running the following command in your favorite terminal:

  $ python --version
  

  Python 2.x2.7

  If you have Python 2 installed, and it is your default version (see Troubleshooting for more details) you should see something like this:

  $ python --version
  Python 2.7.12
  

  Python 3.x3.0

  If you have Python 3 installed, and it is your default version (see Troubleshooting for more details) you should see something like this:

  $ python --version
  Python 3.5.2
  

  Python 3.x3.0

  If using Python 3, you may run across the issue that $ python --version leaves you with a Python 2.x version, in that case, use $ python3 instead of $ python. This is because Python 2.x is installed on your system (this is the case by default on MacOS and many Linux distributions). Python 3.x doesn't override that behavior by default for legacy reasons. If you have multiple Python installations you can determine which you have by using the which command:

  $ which python
  

  This command will show you the path to the python executable you're using.

  In your terminal, navigate to the directory containing the file hello.py.

  For those who aren't familiar with the terminal, use cd <dir> to change directories. On most platforms, users can also Shift-right-click and select Open command window/terminal here from file manager when in a folder where hello.py is contained. MacOS users can drag any folder from a Finder window into the Terminal to copy the path to that folder.

  Type python hello.py, then hit the Enter key.

  $ python hello.py
  Hello, World
  

  You can also substitute hello.py with the path to your file. For example, if you have the file in your home directory and your user is "user" on Linux, you can type python /home/user/hello.py.

  You should see Hello, World printed to the console.

  Launch an interactive Python shell

  By executing (running) the python command in your terminal, you are presented with an interactive Python shell (also known as a REPL)

  $ python
  Python 2.7.12 (default, Jun 28 2016, 08:46:01) 
  [GCC 6.1.1 20160602] on linux
  Type "help", "copyright", "credits" or "license" for more information.
  >>> print 'Hello, World'
  Hello, World
  >>>
  

  If you want to run python3 from your terminal command python3.

  $ python
  Python 3.5.2 (default, Jun 28 2016, 08:46:01) 
  [GCC 6.1.1 20160602] on linux
  Type "help", "copyright", "credits" or "license" for more information.
  >>> print('Hello, World')
  Hello, World
  >>>
  

  Alternatively, start the interactive prompt and load file with python -i <file.py>.

  In command line, run:

  $ python -i hello.py
  "Hello World"
  >>>
  

  The interactive Python shell is a very practical tool for learning and testing the features of the Python language while playing with the values and syntax interactively.

  Try an interactive Python shell online.

  Run commands as a string

  Python can be passed arbitrary code as a string in the shell:

  $ python -c 'print("Hello, World)'
  Hello, World
  

  This can be useful when concatenating the results of scripts together in the shell.

  Shells and Beyond

  Shells - So far, we have discussed different ways to run code using Python's native interactive shell. Shells use Python's interpretive power for experimenting with code real-time. Alternative shells include IDLE - a pre-bundled GUI, IPython - known for extending the interactive experience, etc.

  Programs - For long-term storage you can save content to .py files and edit/execute them as scripts or programs with external tools e.g. shell, IDEs (such as PyCharm), Jupyter notebooks, etc. Intermediate users may use these tools; however, the methods discussed here are sufficient for getting started.

  PEP8 defines guidelines for formatting Python code. Formatting code well is important so you can quickly read what the code does.

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• 83

  Creating variables and assigning values
  83

  To create a variable in Python, all you need to do is specify the variable name, and then assign a value to it. Python uses = to assign values to variables.

  The following paragraph only applies to programmers that have previously used a statically typed language.

  There's no need to declare a variable in advance (or to assign a data type to it), assigning a value to a variable itself declares and initializes the variable with that value. You can't declare a variable without assigning it an initial value. You do not need to allocate memory.

  a = 2
  print(a)
  # Out: 2
  
  b = 9223372036854775807
  print(b)
  # Out: 9223372036854775807
  
  pi = 3.14
  print(pi)
  # Out: 3.14
  
  c = 'A'
  print(c)
  # Out: A
  
  name = 'John Doe'
  print(name)
  # Out: John Doe
  
  q = True
  print(q)
  # Out: True
  
  x = None
  print(x)
  # Out: None
  

  Python automatically assigns the appropriate type to a variable.

  a = 2
  print(type(a))
  # Out: <type 'int'>
  
  b = 9223372036854775807
  print(type(b))
  # Out: <type 'int'>
  
  pi = 3.14
  print(type(pi))
  # Out: <type 'float'>
  
  c = 'A'
  print(type(c))
  # Out: <type 'str'>
  
  name = 'John Doe'
  print(type(name))
  # Out: <type 'str'>
  
  q = True
  print(type(q))
  # Out: <type 'bool'>
  
  x = None
  print(type(x))
  # Out: <type 'NoneType'>
  

  However, note that changing the contents of an object that is referred to by a variable will be reflected through any other variables that reference the same object. That is, if x and y point at the same mutable object(e.g., lists, dictionaries, sets, or byte arrays), changing the contents of one will be seen through the other:

  x = y = [7, 8, 9]   # x and y refer to the same list; i.e., they refer to same memory location
  x = [13, 8, 9]      # now we are assigning a brand new list to x (memory location for x changed!)
  print(y)            # y is unchanged, so it's OK
  # Out: [7, 8, 9]
  

  But:

  x = y = [7, 8, 9]   # x and y refer to the same list i.e. refer to same memory location
  x[0] = 13           # now we are replacing first element of x with 13 (memory location for x unchanged)
  print(y)            # this time y changed!
  # Out: [13, 8, 9]
  

  In plain English:

  x = y = [7, 8, 9]   # creates hardware level bonds between x and y because [7, 8, 9] is a mutable object
  x = y = 7           # does not create any bonds, just assigns because 7 is an immutable object
  

  In other words, python variables are references.

  Nested lists are valid in Python. This means that a list can contain another list as an element.

  x = [1, 2, [3, 4, 5], 6, 7] # this is nested list
  print x[2]
  # Out: [3, 4, 5]
  print x[2][1]
  # Out: 4
  

  Lists in python can contain multiple data types unlike static typed language where the list can be of certain types only e.g. int or string

  For example

  vlist = [1, 'a', 30.5, "Strings"]
  print(vlist)
  # Out: [1, 'a', 30.5, "Strings"]
  

  Just like individual variables, we can check the type of individual list elements

  print(type(vlist[0]))
  # Out: <type 'int'>
  print(type(vlist[1]))
  # Out: <type 'str'>
  print(type(vlist[2]))
  # Out: <type 'float'>
  print(type(vlist[3]))
  # Out: <type 'str'>
  

  Lastly, variables in Python do not have to stay the same type as which they were first defined -- you can simply use = to assign a new value to a variable, even if that value is of a different type.

  a = 2 
  print(a)
  # Out: 2
  
  a = "New value"
  print(a)
  # Out: New value
  

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• 51

  Block Indentation
  51

  Unlike many other programming languages, indentation in Python is mandatory. Indentation seamlessly defines code blocks for the purpose of code readability. However, this rule can cause you some troubles if your code editor is miscalibrated.

  Python uses the colon symbol (:) and indentation for showing where blocks of code begin and end (if you come from another language, do not confuse this with somehow being related to the ternary operator). That is, blocks in Python, such as functions, loops, if clauses and other constructs, have no ending identifiers. All blocks start with a colon and then contain the lines indented below it.

  For example:

  def my_function():    # This is a function definition. Note the colon (:)
      a = 2             # This line belongs to the function because it's indented
      return a          # This line also belongs to the same function
  print(my_function())  # This line is OUTSIDE the function block
  

  or

  if a > b:             # if block starts here
      print(a)          # This is part of the if block
  else:                 # else must be at the same level as if
      print(b)          # This line is part of the else block
  

  Blocks that contain exactly one single-line statement may be put on the same line, though this form is generally not considered good style:

  if a > b: print(a)
  else: print(b)  
  

  Attempting to do this with more than a single statement will not work:

  if x > y: y = x
      print(y) # IndentationError: unexpected indent
  
  if x > y: while y != z: y -= 1  # SyntaxError: invalid syntax
  

  An empty block causes an IndentationError. Use pass (a command that does nothing) when you have a block with no content:

  def will_be_implemented_later():
      pass
  

  It is, however, possible, although most of the time a bad practice, to put several statements on a single line using ;

  For instance:

  print('Hello world'); print('Hello world')
  

  Spaces vs. Tabs

  tl;dr: always use 4 spaces for indentation.

  Using tabs exclusively is possible but PEP 8, the style guide for Python code, states that spaces are preferred.

  Python 3.x3.0

  Python 3 disallows mixing the use of tabs and spaces for indentation. In such case a compile-time error is generated: Inconsistent use of tabs and spaces in indentation and the program will not run.

  Python 2.x2.7

  Python 2 allows mixing tabs and spaces in indentation; this is strongly discouraged. The tab character completes the previous indentation to be a multiple of 8 spaces. Since it is common that editors are configured to show tabs as multiple of 4 spaces, this can cause subtle bugs.

  Citing PEP 8:

  When invoking the Python 2 command line interpreter with the -t option, it issues warnings about code that illegally mixes tabs and spaces. When using -tt these warnings become errors. These options are highly recommended!

  Many editors have "tabs to spaces" configuration. When configuring the editor, one should differentiate between the tab character ('\t') and the Tab key.

  • The Tab key may be configured to insert 4 spaces for indentation, to match the language's convention.
  • The tab character should be configured to show 8 spaces (not 4!), to match the semantics of the language as described above; note that these differ from the convention: a single tab character can match two 4-space indentation levels, which may lead to subtle bugs. This is more important in Python2, since mixed indentation is forbidden in Python3. Of course, it is preferable to physically replace tab indentation with 4-space indentation when possible; editors can help with that too.

  Python source code written with a mix of tabs and spaces, or with non-standard number of indentation spaces can be made PEP8-conformant using autopep8.

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• 22

  Datatypes
  22

  Built-in Types

  Booleans

  bool: A boolean value of either True or False.

  In Python 2, a boolean is also an int:

  isinstance(True, int) == True
  
  int(True) == 1
  int(False) == 0
  

  Numbers

  • int: Integer number

    a = 2
    b = 100
    c = 123456789
    d = 38563846326424324
    

    Since version 3.0, integers in Python are of arbitrary size.

    Note: in older versions of Python, a long type was available and this was distinct from int. The two have been unified.

  • float: Floating point number; precision depends on the implementation and system architecture, for CPython the Float datatype corresponds to a C double.

    a = 2.0
    b = 100.e0
    c = 123456789.e1
    

  • complex: Complex numbers

    a = 2 + 1j
    b = 100 + 10j
    

  Sequences and collections

  Python differentiates between ordered sequences and unordered collections (such as set and dict).

  • str: Character string; in Python 3 it is a unicode string, while in Python 2 it is a byte string.

  • unicode: In Python 3 this type does not exist, str replaces it; in Python 2 it represents an unicode encoded string.

  • bytes: In Python 3 this represents a string of bytes, without encoding defined; in Python 2 this is a synonym of str.

  • tuple: An ordered collection of n values of any type (n >= 0); supports indexing; immutable; hashable if all its members are hashable.

    a = (1, 2, 3)
    b = ('a', 1, 'python', (1, 2))
    b[2] = 'something else' # returns a TypeError
    

  • list: An ordered collection of n values (n >= 0); not hashable; mutable.

    a = [1, 2, 3]
    b = ['a', 1, 'python', (1, 2), [1, 2]]
    b[2] = 'something else' # allowed
    

  • set: An unordered collection of unique values.

    a = {1, 2, 'a'}
    

  • reversed: A reversed order of str with reversed function

    a = reversed('hello')
    

  • dict: An unordered collection of unique key-value pairs; keys must be hashable.

  An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equality must have the same hash value.

      a = {1: 'one',
           2: 'two'}
  
      b = {'a': [1, 2, 3],
           'b': 'a string'}
  

  Built-in constants

  In conjunction with the built-in datatypes there are a small number of built-in constants in the built-in namespace:

  • True: The true value of the built-in type bool
  • False: The false value of the built-in type bool
  • None: A singleton object used to signal that a value is absent.
  • Ellipsis or ...: used in core Python3+ anywhere and limited usage in Python2.7+ as part of array notation. numpy and related packages use this as a 'include everything' reference in arrays.
  • NotImplemented: a singleton used to indicate to Python that a special method doesn't support the specific arguments, and Python will try alternatives if available.

  a = None # No value will be assigned. Any valid datatype can be assigned later
  

  Python 3.x3.0

  None doesn't have any natural ordering. Using ordering comparison operators (<, <=, >=, >) isn't supported anymore and will raise a TypeError.

  Python 2.x2.7

  None is always less than any number (None < -32 evaluates toTrue`).

  Testing the type of variables

  In python, we can check the datatype of an object using the built-in function type.

  a = '123'
  print(type(a))
  #Out: <class 'str'>
  b = 123
  print(type(b))
  #Out: <class 'int'>
  

  In conditional statements it is possible to test the datatype with isinstance. However, it is usually not encouraged to rely on the type of the variable.

  i = 7
  if isinstance(i, int):
      i += 1
  elif isinstance(i, str):
      i = int(i)
      i += 1
  

  For information on the differences between type() and isinstance() read: Differences between isinstance and type in Python

  To test if something is of NoneType:

  x = None
  if x is None:
      print('Not a surprise, I just defined x as None.')
  

  Converting between datatypes

  You can perform explicit datatype conversion.

  For example, '123' is of str type and it can be converted to integer using int function.

  a = '123'
  b = int(a)
  

  Converting from a float string such as '123.456' can be done using float function.

  a = '123.456'
  b = float(a)
  

  You can also convert sequence or collection types

  a = 'hello'
  list(a) # returns ['h', 'e', 'l', 'l', 'o']
  set(a)  # returns {'o', 'e', 'l', 'h'}
  

  Explicit string type at definition of literals

  With one letter labels just in front of the quotes you can tell what type of string you want to define.

  • b'foo bar': results bytes in Python 3, str in Python 2
  • u'foo bar': results str in Python 3, unicode in Python 2
  • 'foo bar': results str
  • r'foo bar': results so called raw string, where escaping special characters is not necessary, everything is taken verbatim as you typed

  To see how raw string makes a difference, try this:

  normal = 'foo\nbar'
  escaped = 'foo\\nbar'
  raw = r'foo\nbar'
  print(normal)
  # Out: foo
  #      bar
  print(escaped)
  # Out: foo\nbar
  print(raw)
  # Out: foo\nbar
  

  Mutable and Immutable Data Types

  In Python everything is an object. When define a variable like below

  a = 5 
  

  We are creating an object 5 and tagging it with name a so that we can later use it with name a.

  Now, we re-assign a like below

  a = 6
  

  Here, we are not modifying a rather creating a new object 6 and tagging it with a. So, it means in python we can never modify an int object rather create a new object and tag it with same name. So, int is an immutable data type.

  Mutable data types are those in which we can modify the object.

  Immutable Data Types::

  • int, long, float, complex
  • str
  • byte
  • tuple
  • frozen set

  Mutable Data Types::

  • byte array
  • list
  • set
  • dict

  You can use id to check the identity of object, to verify whether same object is modified or new object is created.

  Example:
  << Immutable int >>
  >>> a=5
  >>> id(a)
  154463584
  >>> a=6
  >>> id(a)
  154463572
  
  Mutable list Object::
  
  >>> list1=[1,2,3]
  >>> id(list1)
  3078128012L
  >>> list1[0]=5
  >>> id(list1)
  3078128012L
  >>> list1.append(6)
  >>> id(list1)
  3078128012L
  

  Datatype related exceptions (errors)

  If the original value is not valid value for target type, then a ValueError exception will be raised.

  int('a')
  '''
  Out: Traceback (most recent call last):
       File "<stdin>", line 1, in <module>
       ValueError: invalid literal for int() with base 10: 'a'
  '''
  

  This method is a simple solution for example if you want to the user input (which, in Python 3, is always str) to a number:

  value = int(input()) # Read user input and convert to integer
  

  (However, in a real application, not a bad idea to add some checking and error handling.)

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• 18

  IDLE - Python GUI
  18

  IDLE is Python’s Integrated Development and Learning Environment and is an alternative to the command line. As the name may imply, IDLE is very useful for developing new code or learning python. On Windows this comes with the Python interpreter, but in other operating systems you may need to install it through your package manager.

  In IDLE, hit F5 or run Python Shell to launch an interpreter. Using IDLE can be a better learning experience for new users because code is interpreted as the user writes.

  Troubleshooting

  • Windows

    If you're on Windows, the default command is python. If you receive a "'python' is not recognized" error, the most likely cause is that Python's location is not in your system's PATH environment variable. This can be accessed by right-clicking on 'My Computer' and selecting 'Properties' or by navigating to 'System' through 'Control Panel'. Click on 'Advanced system settings' and then 'Environment Variables...'. Edit the PATH variable to include the directory of your Python installation, as well as the Script folder (usually C:\Python27;C:\Python27\Scripts). This requires administrative privileges and may require a restart.

    When using multiple versions of Python on the same machine, a possible solution is to rename one of the python.exe files. For example, naming one version python27.exe would cause python27 to become the Python command for that version.

    You can also use the Python Launcher for Windows, which is available through the installer and comes by default. It allows you to select the version of Python to run by using py -[x.y] instead of python[x.y]. You can use the latest version of Python 2 by running scripts with py -2 and the latest version of Python 3 by running scripts with py -3.

  • Debian/Ubuntu/MacOS

    This section assumes that the location of the python executable has been added to the PATH environment variable.

    If you're on Debian/Ubuntu/MacOS, open the terminal and type python for Python 2.x or python3 for Python 3.x.

    Type which python to see which Python interpreter will be used.

  • Arch Linux

    The default Python on Arch Linux (and descendants) is Python 3, so use python or python3 for Python 3.x and python2 for Python 2.x.

  • Other systems

    Python 3 is sometimes bound to python instead of python3. To use Python 2 on these systems where it is installed, you can use python2.

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• 12

  Collection Types
  12

  There are a number of collection types in Python. While types such as int and str hold a single value, collection types hold multiple values.

  Lists

  The list type is probably the most commonly used collection type in Python. Despite its name, a list is more like an array in other languages, mostly JavaScript. In Python, a list is merely an ordered collection of valid Python values. A list can be created by enclosing values, separated by commas, in square brackets:

  int_list = [1, 2, 3]
  string_list = ['abc', 'defghi']
  

  A list can be empty:

  empty_list = []
  

  The elements of a list are not restricted to a single data type, which makes sense given that Python is a dynamic language:

  mixed_list = [1, 'abc', True, 2.34, None]
  

  A list can contain another list as its element:

  nested_list = [['a', 'b', 'c'], [1, 2, 3]]
  

  The elements of a list can be accessed via an index, or numeric representation of their position. Lists in Python are zero-indexed meaning that the first element in the list is at index 0, the second element is at index 1 and so on:

  names = ['Alice', 'Bob', 'Craig', 'Diana', 'Eric']
  print(names[0]) # Alice
  print(names[2]) # Craig
  

  Indices can also be negative which means counting from the end of the list (-1 being the index of the last element). So, using the list from the above example:

  print(names[-1]) # Eric
  print(names[-4]) # Bob
  

  Lists are mutable, so you can change the values in a list:

  names[0] = 'Ann'
  print(names) # Outputs ['Ann', 'Bob', 'Craig', 'Diana', 'Eric']
  

  Besides, it is possible to add and/or remove elements from a list:

  Append object to end :- L.append(object) -> None

  names = ['Alice', 'Bob', 'Craig', 'Diana', 'Eric']
  names.append("Sia")
  print(names) # Outputs ['Alice', 'Bob', 'Craig', 'Diana', 'Eric', 'Sia']
  

  Add a new element to list on a specific index. L.insert(index, object) -- insert object before index

  names.insert(1,"Nikki")
  print(names)
  ['Alice', 'Nikki', 'Bob', 'Craig', 'Diana', 'Eric', 'Sia'] Outputs ['Ben', 'Alice', 'Bob', 'Craig', 'Diana', 'Eric']
  

  Remove the first occurrence of value:- L.remove(value) -> None

  names.remove("Bob")
  print(names) # Outputs ['Alice', 'Nikki', 'Craig', 'Diana', 'Eric', 'Sia']
  

  Remove and return item at index (default last) :- L.pop([index]) -> item

  names.pop() # Outputs 'Sia'
  

  You can iterate over the list elements like below:

  for element in my_list:
      print (element)
  

  Tuples

  A tuple is similar to a list except that is it fixed-length and immutable. So the values in the tuple cannot be changed nor the values be added to or removed from the tuple. Tuples are commonly used for small collections of values that will not need to change, such as an IP address and port. Tuples are represented with parentheses instead of square brackets:

  ip_address = ('10.20.30.40', 8080)
  

  The same indexing rules for lists also apply to tuples. Tuples can also be nested and the values can be any valid Python valid.

  A tuple with only one member must be defined (note the comma) this way:

  one_member_tuple = ('Only member',)
  

  or

  one_member_tuple = 'Only member',   # No brackets
  

  or just using tuple syntax

  one_member_tuple = tuple(['Only member'])
  

  Dictionaries

  A dictionary in Python is a collection of key-value pairs. The dictionary is surrounded by curly braces. Each pair is separated by a comma and the key and value are separated by a colon. Here is an example:

  state_capitols = {
      'Arkanasas': 'Little Rock',
      'Colorado': 'Denver',
      'California': 'Sacremento', 
      'Georgia': 'Atlanta'
  }
  

  To get a value, refer to it by its key:

  ca_capitol = state_capitols['California']
  

  You can also get all of the keys in a dictionary and then iterate over them:

  for k in state_capitols.keys():
      print('{} is the capitol of {}'.format(state_capitols[k], k))
  

  If dictionaries remind you of JSON, you're not dreaming! The native json module in the Python standard library converts between JSON and dictionaries. In fact, JSON is one of the most common uses of dictionaries.

  set

  A set is a collection of elements with no repeats and without insertion order but sorted order.They are used in situations where it is only important that some things are grouped together, and not what order they were included. For large groups of data, it is much faster to check whether or not an element is in a set than it is to do the same for a list.

  Defining a set is very similar to defining a dictionary:

  first_names = {'Adam', 'Beth', 'Charlie'}
  

  Or you can build a set using an existing list:

  my_list = [1,2,3]
  my_set = set(my_list)
  

  Check membership of the set using in:

  if name in first_names:
      print(name)
  

  You can iterate over a set exactly like a list, but remember: the values will be in a arbitrary, implementation-defined order.

  defaultdict

  A defaultdict is a dictionary with a default value for keys, so that keys for which no value has been explicitly defined can be accessed without errors. defaultdict is especially useful when the values in the dictionary are collections(lists, dicts etc) in the sense that it doesnt need to be initialized everytime when a new key is used.

  A defaultdict will never raise a KeyError. Any key that does not exist gets the default value returned.

  For example, consider the following dictionary

  >>> state_capitols = {
      'Arkanasas': 'Little Rock',
      'Colorado': 'Denver',
      'California': 'Sacremento', 
      'Georgia': 'Atlanta'
  }
  

  If we try to access a non-existent key, python returns us an error as follows

  >>> state_capitols['Alabama']
  Traceback (most recent call last):
  
    File "<ipython-input-61-236329695e6f>", line 1, in <module>
      state_capitols['Alabama']
  
  KeyError: 'Alabama'
  

  Let us try with a defaultdict. It can be found in the collections module.

  >>> from collections import defaultdict
  >>> state_capitols = defaultdict(lambda: 'Boston')
  

  What we did here is to set a default value (Boston) incase the give key doesnt exist. let us try to populate the dict as before

  >>> state_capitols['Arkanasas'] = 'Little Rock'
  >>> state_capitols['California'] = 'Sacremento'
  >>> state_capitols['Colorado'] = 'Denver'
  >>> state_capitols['Georgia'] = 'Atlanta'
  

  If we try to access the dict with a non-existent key, python will return us the default value i.e. Boston

  >>> state_capitols['Alabama']
  'Boston'
  

  and returns the created values for existing key just like a normal dictionary

  >>> state_capitols['Arkanasas']
  'Little Rock'
  

  edited 2 days ago

  

  

  

  

  

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• 12

  User Input
  12

  Interactive input

  To get input from the user, use the input function (note: in Python 2.x, the function is called raw_input instead, although Python 2.x has its own version of input that is completely different):

  Python 2.x2.3

  name = raw_input("What is your name? ")
  # Out: What is your name? _
  

  Security Remark Do not use input() in Python2 - the entered text will be evaluated as if it were a Python expression (equivalent to eval(input()) in Python3), which might easily become a vulnerability. See this article for further information on the risks of using this function.

  Python 3.x3.0

  name = input("What is your name? ")
  # Out: What is your name? _
  

  The remainder of this example will be using Python 3 syntax.

  The function takes a string argument, which it displays as a prompt, and returns a string. The above provides a prompt, waiting for me to type my name in:

  name = input("What is your name? ")
  # Out: What is your name?
  

  If I type "Bob" and hit enter, now the variable name contains the string "Bob":

  name = input("What is your name? ")
  # Out: What is your name? Bob
  print(name)
  # Out: Bob
  

  Note that the input is always of type str, which is important if you want the user to enter numbers. Therefore, you need to convert the str before trying to use it as a number:

  x = input("Write a number:")
  # Out: Write a number: 10
  x / 2
  # Out: TypeError: unsupported operand type(s) for /: 'str' and 'int'
  float(x) / 2
  # Out: 5.0
  

  ---

  NB: It's recommended to use try/except blocks to catch exceptions when dealing with user inputs. For instance, if your code wants to cast a raw_input into an int, and what the user writes is uncastable, it raises a ValueError.

  edited Nov 14 '16 at 19:24

  

  

  

  

  

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• 4

  Built in Modules and Functions
  4

  A module is a file containing Python definitions and statements. Function is a piece of code which execute some logic.

  >>> pow(2,3)
      8
  

  To check the built in function in python

  >>> dir(__builtins__)
  [
      'ArithmeticError', 
      'AssertionError', 
      'AttributeError', 
      'BaseException', 
      'BufferError', 
      'BytesWarning', 
      'DeprecationWarning', 
      'EOFError', 
      'Ellipsis', 
      'EnvironmentError', 
      'Exception', 
      'False', 
      'FloatingPointError', 
      'FutureWarning', 
      'GeneratorExit', 
      'IOError', 
      'ImportError', 
      'ImportWarning', 
      'IndentationError', 
      'IndexError', 
      'KeyError', 
      'KeyboardInterrupt', 
      'LookupError', 
      'MemoryError', 
      'NameError', 
      'None', 
      'NotImplemented', 
      'NotImplementedError', 
      'OSError', 
      'OverflowError', 
      'PendingDeprecationWarning', 
      'ReferenceError', 
      'RuntimeError', 
      'RuntimeWarning', 
      'StandardError', 
      'StopIteration', 
      'SyntaxError', 
      'SyntaxWarning', 
      'SystemError', 
      'SystemExit', 
      'TabError', 
      'True', 
      'TypeError', 
      'UnboundLocalError', 
      'UnicodeDecodeError', 
      'UnicodeEncodeError', 
      'UnicodeError', 
      'UnicodeTranslateError', 
      'UnicodeWarning', 
      'UserWarning', 
      'ValueError', 
      'Warning', 
      'ZeroDivisionError', 
      '__debug__', 
      '__doc__', 
      '__import__', 
      '__name__', 
      '__package__', 
      'abs', 
      'all', 
      'any', 
      'apply', 
      'basestring', 
      'bin', 
      'bool', 
      'buffer', 
      'bytearray', 
      'bytes', 
      'callable', 
      'chr', 
      'classmethod', 
      'cmp', 
      'coerce', 
      'compile', 
      'complex', 
      'copyright', 
      'credits', 
      'delattr', 
      'dict', 
      'dir', 
      'divmod', 
      'enumerate', 
      'eval', 
      'execfile', 
      'exit', 
      'file', 
      'filter', 
      'float', 
      'format', 
      'frozenset', 
      'getattr', 
      'globals', 
      'hasattr', 
      'hash', 
      'help', 
      'hex', 
      'id', 
      'input', 
      'int', 
      'intern', 
      'isinstance', 
      'issubclass', 
      'iter', 
      'len', 
      'license', 
      'list', 
      'locals', 
      'long', 
      'map', 
      'max', 
      'memoryview', 
      'min', 
      'next', 
      'object', 
      'oct', 
      'open', 
      'ord', 
      'pow', 
      'print', 
      'property', 
      'quit', 
      'range', 
      'raw_input', 
      'reduce', 
      'reload', 
      'repr', 
      'reversed', 
      'round', 
      'set', 
      'setattr', 
      'slice', 
      'sorted', 
      'staticmethod', 
      'str', 
      'sum', 
      'super', 
      'tuple', 
      'type', 
      'unichr', 
      'unicode', 
      'vars', 
      'xrange', 
      'zip'
  ]
  

  To know the functionality of any function, we can use built in function help .

  >>> help(max)
  Help on built-in function max in module __builtin__:
  max(...)
      max(iterable[, key=func]) -> value
      max(a, b, c, ...[, key=func]) -> value
      With a single iterable argument, return its largest item.
      With two or more arguments, return the largest argument.
  

  Built in modules contains extra functionalities.For example to get square of a number we need to include math module.

  >>> import math
  >>> math.sqrt(16) #to get the square root of number we use 'sqrt'
      4.0
  

  Integers are simply converted to string using a builtin function called str

  >>> str(123)
      "123"
  

  edited Nov 3 '16 at 13:41

  

  

  

  

  

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• 4

  Creating a module
  4

  A module is an importable file containing definitions and statements.

  A module can be created by creating a .py file.

  # hello.py
  def say_hello():
      print("Hello!")
  

  Functions in a module can be used by importing the module.

  For modules that you have made, they will need to be in the same directory as the file that you are importing them into. (However, you can also put them into the Python lib directory with the pre-included modules, but should be avoided if possible.)

  $ python
  >>> import hello
  >>> hello.say_hello()
  => "Hello!"
  

  Modules can be imported by other modules.

  # greet.py
  import hello
  hello.say_hello()
  

  Specific functions of a module can be imported.

  # greet.py
  from hello import say_hello
  say_hello()
  

  Modules can be aliased.

  # greet.py
  import hello as ai
  ai.say_hello()
  

  A module can be stand-alone runnable script.

  # run_hello.py
  if __name__ == '__main__':
      import hello
  

  Run it!

  $ python run_hello.py
  => "Hello!"
  

  If module inside a directory and need to detect by python, directory should contain a file called __init__.py.

  edited Nov 23 '16 at 4:33

  

  

  

  

  

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• 4

  Installing external modules using pip
  4

  pip is your friend when you need to install any package from the plethora of choices available at the python package index (PyPI). pip is already installed if you're using Python 2 >= 2.7.9 or Python 3 >= 3.4 downloaded from python.org. For computers running Linux or another *Nix with a native package manager, pip must often be manually installed.

  On instances with both Python 2 and Python 3 installed, pip often refers to Python 2 and pip3 to Python 3. Using pip will only install packages for Python 2 and pip3 will only install packages for Python 3.

  Searching for a package is as simple as typing

  $ pip search <query>
  # Searches for packages whose name or summary contains <query>
  

  Installing a package is as simple as typing

  $ pip install [package_name] # latest version of the package

  $ pip install [package_name]==x.x.x # specific version of the package

  $ pip install '[package_name]>=x.x.x' # minimum version of the package

  where x.x.x is the version number of the package you want to install.

  When your server is behind proxy, you can install package by using below command:

  pip --proxy http://<server address>:<port> install

  You can upgrade your existing pip installation by using the following commands

  On Linux or OS X: pip install -U pip

  You may need to use sudo with pip on some Linux Systems

  On Windows: py -m pip install -U pip or python -m pip install -U pip

  For more information regarding pip do read here.

  edited Jan 19 at 4:12

  

  

  

  

  

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• 4

  List the identifiers that an object defines
  4

  The identifiers of the built-in function dir() are the functions, classes and variables defined in that object. The dir() function returns a list of strings.

  If called without an argument, return the names in the current scope.

  >>> dir()
  ['__builtins__', '__doc__', '__name__', '__package__']
  
  
  >>> import os
  >>> dir()
  ['__builtins__', '__doc__', '__name__', '__package__', 'os']
  

  Else, return an alphabetized list of names comprising (some of) the attributes of the given object, and of attributes reachable from it.

  >>> dir(os)
  ['EX_CANTCREAT', 'EX_CONFIG', 'EX_DATAERR', 'EX_IOERR', 'EX_NOHOST', 'EX_NOINPUT','EX_NOPERM', 'EX_NOUSER', 'EX_OK', 'EX_OSERR', 'EX_OSFILE', 'EX_PROTOCOL', 'EX_SOFTWARE', 'EX_TEMPFAIL', 'EX_UNAVAILABLE', 'EX_USAGE', 'F_OK', 'NGROUPS_MAX', 'O_APPEND', 'O_ASYNC', 'O_CREAT', 'O_DIRECT', 'O_DIRECTORY', 'O_DSYNC', 'O_EXCL', 'O_LARGEFILE', 'O_NDELAY', 'O_NOATIME', 'O_NOCTTY', 'O_NOFOLLOW', 'O_NONBLOCK', 'O_RDONLY', 'O_RDWR', 'O_RSYNC', 'O_SYNC', 'O_TRUNC', 'O_WRONLY', 'P_NOWAIT', 'P_NOWAITO', 'P_WAIT', 'R_OK', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', 'ST_APPEND', 'ST_MANDLOCK', 'ST_NOATIME', 'ST_NODEV', 'ST_NODIRATIME', 'ST_NOEXEC', 'ST_NOSUID', 'ST_RDONLY', 'ST_RELATIME', 'ST_SYNCHRONOUS', 'ST_WRITE', 'TMP_MAX', 'UserDict', 'WCONTINUED', 'WCOREDUMP', 'WEXITSTATUS', 'WIFCONTINUED', 'WIFEXITED', 'WIFSIGNALED', 'WIFSTOPPED', 'WNOHANG', 'WSTOPSIG', 'WTERMSIG', 'WUNTRACED', 'W_OK', 'X_OK', '_Environ', '__all__', '__builtins__', '__doc__', '__file__', '__name__', '__package__', '_copy_reg', '_execvpe', '_exists', '_exit', '_get_exports_list', '_make_stat_result', '_make_statvfs_result', '_pickle_stat_result', '_pickle_statvfs_result', '_spawnvef', 'abort', 'access', 'altsep', 'chdir', 'chmod', 'chown', 'chroot', 'close', 'closerange', 'confstr', 'confstr_names', 'ctermid', 'curdir', 'defpath', 'devnull', 'dup', 'dup2', 'environ', 'errno', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'extsep', 'fchdir', 'fchmod', 'fchown', 'fdatasync', 'fdopen', 'fork', 'forkpty', 'fpathconf', 'fstat', 'fstatvfs', 'fsync', 'ftruncate', 'getcwd', 'getcwdu', 'getegid', 'getenv', 'geteuid', 'getgid', 'getgroups', 'getloadavg', 'getlogin', 'getpgid', 'getpgrp', 'getpid', 'getppid', 'getresgid', 'getresuid', 'getsid', 'getuid', 'initgroups', 'isatty', 'kill', 'killpg', 'lchown', 'linesep', 'link', 'listdir', 'lseek', 'lstat', 'major', 'makedev', 'makedirs', 'minor', 'mkdir', 'mkfifo', 'mknod', 'name', 'nice', 'open', 'openpty', 'pardir', 'path', 'pathconf', 'pathconf_names', 'pathsep', 'pipe', 'popen', 'popen2', 'popen3', 'popen4', 'putenv', 'read', 'readlink', 'remove', 'removedirs', 'rename', 'renames', 'rmdir', 'sep', 'setegid', 'seteuid', 'setgid', 'setgroups', 'setpgid', 'setpgrp', 'setregid', 'setresgid', 'setresuid', 'setreuid', 'setsid', 'setuid', 'spawnl', 'spawnle', 'spawnlp', 'spawnlpe', 'spawnv', 'spawnve', 'spawnvp', 'spawnvpe', 'stat', 'stat_float_times', 'stat_result', 'statvfs', 'statvfs_result', 'strerror', 'symlink', 'sys', 'sysconf', 'sysconf_names', 'system', 'tcgetpgrp', 'tcsetpgrp', 'tempnam', 'times', 'tmpfile', 'tmpnam', 'ttyname', 'umask', 'uname', 'unlink', 'unsetenv', 'urandom', 'utime', 'wait', 'wait3', 'wait4', 'waitpid', 'walk', 'write']
  

  If the object supplies a method named __dir__, it will be used;

  >>> os.__dir__
  Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
  

  Otherwise the default dir() logic is used and returns:

  • For a module object: the module's attributes.

  >>> dir(os)
  

  • For a class object: its attributes, and recursively the attributes of its bases.

  >>> dir(dict)
  ['__class__', '__cmp__', '__contains__', '__delattr__', '__delitem__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__iter__', '__le__', '__len__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'clear', 'copy', 'fromkeys', 'get', 'has_key', 'items', 'iteritems', 'iterkeys', 'itervalues', 'keys', 'pop', 'popitem', 'setdefault', 'update', 'values', 'viewitems', 'viewkeys', 'viewvalues']
  

  • For any other object: its attributes, its class's attributes, and recursively the attributes of its class's base classes.

  >>> class MyClassObject(object):
  ...     pass
  ... 
  >>> dir(MyClassObject)
  ['__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']
  

  Requesting a subidentifier:

  >>> dir(os.close)
  ['__call__', '__class__', '__cmp__', '__delattr__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__self__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']
  

  edited Jul 28 '16 at 10:35

  

  
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• 4

  String function - str() and repr()
  4

  There are two functions that can be used to obtain a readable representation of an object.

  repr(x) calls x.__repr__(): a representation of x. eval will usually convert the result of this function back to the original object.

  str(x) calls x.__str__(): a human-readable string that describes the object. This may elide some technical detail.

  ---

  repr()

  For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval(). Otherwise, the representation is a string enclosed in angle brackets that contains the name of the type of the object along with additional information. This often includes the name and address of the object.

  str()

  For strings, this returns the string itself. The difference between this and repr(object) is that str(object) does not always attempt to return a string that is acceptable to eval(). Rather, its goal is to return a printable or 'human readable' string. If no argument is given, this returns the empty string, ''.

  ---

  Example 1:

  s = """w'o"w"""
  repr(s) # Output: '\'w\\\'o"w\''  
  str(s)  # Output: 'w\'o"w'
  eval(str(s)) == s  # Gives a SyntaxError 
  eval(repr(s)) == s # Output: True
  

  Example 2:

  import datetime
  today = datetime.datetime.now()
  str(today)  # Output: '2016-09-15 06:58:46.915000'
  repr(today) # Output: 'datetime.datetime(2016, 9, 15, 6, 58, 46, 915000)'
  

  When writing a class, you can override these methods to do whatever you want:

  class Represent(object):
  
      def __init__(self, x, y):
          self.x, self.y = x, y
  
      def __repr__(self):
          return "Represent(x={},y=\"{}\")".format(self.x, self.y)
  
      def __str__(self):
          return "Representing x as {} and y as {}".format(self.x, self.y)
  

  Using the above class we can see the results:

  r = Represent(1, "Hopper")
  print(r)  # prints __str__
  print(r.__repr__)  # prints __repr__: '<bound method Represent.__repr__ of Represent(x=1,y="Hopper")>'
  rep = r.__repr__()  # sets the execution of __repr__ to a new variable
  print(rep)  # prints 'Represent(x=1,y="Hopper")'
  r2 = eval(rep) # evaluates rep
  print(r2)  # prints __str__ from new object
  print(r2 == r)  # prints 'False' because they are different objects
  

  edited Dec 7 '16 at 4:07

  

  

  

  
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• 3

  Installation of Python 2.7.x and 3.x
  3

  Note: Following instructions are written for Python 2.7 (unless specified): instructions for Python 3.x are similar.

  WINDOWS

  First, download the latest version of Python 2.7 from the official Website (https://www.python.org/downloads/). Version is provided as an MSI package. To install it manually, just double-click the file.

  By default, Python installs to a directory:

   C:\Python27\
  

  Warning: installation does not automatically modify the PATH environment variable.

  Assuming that your Python installation is in C:\Python27, add this to your PATH:

  C:\Python27\;C:\Python27\Scripts\
  

  Now to check if Python installation is valid write in cmd:

  python --version
  

  LINUX

  The latest versions of CentOS, Fedora, Redhat Enterprise (RHEL) and Ubuntu come with Python 2.7.

  To install Python 2.7 on linux manually, just do the following in terminal:

  wget --no-check-certificate https://www.python.org/ftp/python/2.7.X/Python-2.7.X.tgz
  tar -xzf Python-2.7.X.tgz  
  cd Python-2.7.X
  ./configure  
  make  
  sudo make install
  

  Also add the path of new python in PATH environment variable. If new python is in /root/python-2.7.X then run "export PATH = $PATH:/root/python-2.7.X"

  Now to check if Python installation is valid write in terminal:

  python --version
  

  macOS

  As we speak, macOS comes installed with Python 2.7.10, but this version is outdated and slightly modified from the regular Python.

  The version of Python that ships with OS X is great for learning but it’s not good for development. The version shipped with OS X may be out of date from the official current Python release, which is considered the stable production version. (source)

  Install Homebrew:

  /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
  

  Install Python 2.7:

  brew install python
  

  For Python 3.x, use the command brew install python3 instead.

  edited Aug 10 '16 at 13:40

  

  
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• 2

  Help Utility
  2

  Python has several functions built into the interpreter. If you want to get information of keywords, built-in functions, modules or topics open a Python console and enter:

  >>> help()
  

  You will receive information by entering keywords directly:

  >>> help(help)
  

  or within the utility:

  help> help
  

  which will show an explanation:

  Help on _Helper in module _sitebuiltins object:
  
  class _Helper(builtins.object)
   |  Define the builtin 'help'.
   |  
   |  This is a wrapper around pydoc.help that provides a helpful message
   |  when 'help' is typed at the Python interactive prompt.
   |  
   |  Calling help() at the Python prompt starts an interactive help session.
   |  Calling help(thing) prints help for the python object 'thing'.
   |  
   |  Methods defined here:
   |  
   |  __call__(self, *args, **kwds)
   |  
   |  __repr__(self)
   |  
   |  ----------------------------------------------------------------------
   |  Data descriptors defined here:
   |  
   |  __dict__
   |      dictionary for instance variables (if defined)
   |  
   |  __weakref__
   |      list of weak references to the object (if defined)
  

  You can also request subclasses of modules:

  help(pymysql.connections)
  

  Close the helper with 'quit'

  created Jul 22 '16 at 17:42

  
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