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Docstrings

Docstrings (documentation strings) serve a similar purpose to comments, as they are designed to explain code.
However, they are more specific and have a different syntax. They are created by putting a multiline string
containing an explanation of the function below the function's first line.
def shout(word):
"""
Print a word with an
exclamation mark following it.
"""
print(word + "!")

shout("spam")

Result:
>>>
spam!
>>>

Unlike conventional comments, docstrings are retained throughout the runtime of the program. This allows
the programmer to inspect these comments at run time.
----------------------------------------------------------------------------------------------------------------------------
Functions

Although they are created differently from normal variables, functions are just like any other kind of value.
They can be assigned and reassigned to variables, and later referenced by those names.
def multiply(x, y):
return x * y

a = 4
b = 7
operation = multiply
print(operation(a, b))

Result:
>>>
28
>>>

The example above assigned the function multiply to a variable operation. Now, the name operation can
also be used to call the function.
----------------------------------------------------------------------------------------------------------------------------
Functions

Functions can also be used as arguments of other functions.
def add(x, y):
return x + y

def do_twice(func, x, y):
return func(func(x, y), func(x, y))

a = 5
b = 10

print(do_twice(add, a, b))

Result:
>>>
30
>>>

As you can see, the function do_twice takes a function as its argument and calls it in its body.
-----------------------------------------------------------------------------------------------------------------------------
Modules

Modules are pieces of code that other people have written to fulfill common tasks, such as generating random numbers,
performing mathematical operations, etc.

The basic way to use a module is to add import module_name at the top of your code, and then using module_name.var to
access functions and values with the name var in the module.
For example, the following example uses the random module to generate random numbers:
import random

for i in range(5):
value = random.randint(1, 6)
print(value)

Result:
>>>
2
3
6
5
4
>>>

The code uses the randint function defined in the random module to print 5 random numbers in the range 1 to 6.
------------------------------------------------------------------------------------------------------------------------------
Modules

There is another kind of import that can be used if you only need certain functions from a module.
These take the form from module_name import var, and then var can be used as if it were defined normally in your code.
For example, to import only the pi constant from the math module:
from math import pi

print(pi)

Result:
>>>
3.141592653589793
>>>

Use a comma separated list to import multiple objects. For example:
from math import pi, sqrt

* imports all objects from a module. For example: from math import *
This is generally discouraged, as it confuses variables in your code with variables in the external module.
-------------------------------------------------------------------------------------------------------------------------------
Modules

Trying to import a module that isn't available causes an ImportError.
import some_module

Result:
>>>
ImportError: No module named 'some_module'
>>>
--------------------------------------------------------------------------------------------------------------------------------
Modules

You can import a module or object under a different name using the as keyword. This is mainly used when a module or object
has a long or confusing name.
For example:
from math import sqrt as square_root
print(square_root(100))

Result:
>>>
10.0
>>>
---------------------------------------------------------------------------------------------------------------------------------
Modules

There are three main types of modules in Python, those you write yourself, those you install from external sources,
and those that are preinstalled with Python.
The last type is called the standard library, and contains many useful modules. Some of the standard library's useful
modules include string, re, datetime, math, random, os, multiprocessing, subprocess, socket, email, json, doctest, unittest,
pdb, argparse and sys.

Tasks that can be done by the standard library include string parsing, data serialization, testing, debugging
and manipulating dates, emails, command line arguments, and much more!
Python's extensive standard library is one of its main strengths as a language.
----------------------------------------------------------------------------------------------------------------------------------
The Standard Library

Some of the modules in the standard library are written in Python, and some are written in C.
Most are available on all platforms, but some are Windows or Unix specific.
We won't cover all of the modules in the standard library; there are simply too many. The complete documentation for the standard
library is available online at http://www.python.org.
----------------------------------------------------------------------------------------------------------------------------------
Modules

Many third-party Python modules are stored on the Python Package Index (PyPI).
The best way to install these is using a program called pip. This comes installed by default with modern distributions of Python.
If you don't have it, it is easy to install online. Once you have it, installing libraries from PyPI is easy. Look up the name
of the library you want to install, go to the command line (for Windows it will be the Command Prompt), and enter pip install
library_name. Once you've done this, import the library and use it in your code.

Using pip is the standard way of installing libraries on most operating systems, but some libraries have prebuilt binaries
for Windows. These are normal executable files that let you install libraries with a GUI the same way you would install
other programs.
It's important to enter pip commands at the command line, not the Python interpreter.
-----------------------------------------------------------------------------------------------------------------------------------
Exceptions

You have already seen exceptions in previous code. They occur when something goes wrong, due to incorrect code or input.
When an exception occurs, the program immediately stops.
The following code produces the ZeroDivisionError exception by trying to divide 7 by 0.
num1 = 7
num2 = 0
print(num1/num2)

Result:
>>>
ZeroDivisionError: division by zero
>>>
-----------------------------------------------------------------------------------------------------------------------------------
Exceptions

Different exceptions are raised for different reasons.
Common exceptions:
ImportError: an import fails;
IndexError: a list is indexed with an out-of-range number;
NameError: an unknown variable is used;
SyntaxError: the code can't be parsed properly;
TypeError: a function is called on a value of an inappropriate type;
ValueError: a function is called on a value of the correct type, but with an inappropriate value.
Python has several other built-in exceptions, such as ZeroDivisionError and OSError. Third-party libraries also
often define their own exceptions.
------------------------------------------------------------------------------------------------------------------------------------
Exception Handling

To handle exceptions, and to call code when an exception occurs, you can use a try/except statement.
The try block contains code that might throw an exception. If that exception occurs, the code in the try block stops being executed, and the code in the except block is run. If no error occurs, the code in the except block doesn't run.
For example:
try:
num1 = 7
num2 = 0
print (num1 / num2)
print("Done calculation")
except ZeroDivisionError:
print("An error occurred")
print("due to zero division")

Result:
>>>
An error occurred
due to zero division
>>>

In the code above, the except statement defines the type of exception to handle (in our case, the ZeroDivisionError).
--------------------------------------------------------------------------------------------------------------------------------
Exception Handling

A try statement can have multiple different except blocks to handle different exceptions.
Multiple exceptions can also be put into a single except block using parentheses, to have the except block handle all of them.
try:
variable = 10
print(variable + "hello")
print(variable / 2)
except ZeroDivisionError:
print("Divided by zero")
except (ValueError, TypeError):
print("Error occurred")

Result:
>>>
Error occurred
>>>
---------------------------------------------------------------------------------------------------------------------------------
Exception Handling

An except statement without any exception specified will catch all errors. These should be used sparingly, as they can
catch unexpected errors and hide programming mistakes.
For example:
try:
word = "spam"
print(word / 0)
except:
print("An error occurred")


Result:
>>>
An error occurred
>>>

Exception handling is particularly useful when dealing with user input.
-----------------------------------------------------------------------------------------------------------------------------------
finally

To ensure some code runs no matter what errors occur, you can use a finally statement. The finally statement is placed
at the bottom of a try/except statement. Code within a finally statement always runs after execution of the code in
the try, and possibly in the except, blocks.
try:
print("Hello")
print(1 / 0)
except ZeroDivisionError:
print("Divided by zero")
finally:
print("This code will run no matter what")

Result:
>>>
Hello
Divided by zero
This code will run no matter what
>>>
-----------------------------------------------------------------------------------------------------------------------------------
finally

Code in a finally statement even runs if an uncaught exception occurs in one of the preceding blocks.
try:
print(1)
print(10 / 0)
except ZeroDivisionError:
print(unknown_var)
finally:
print("This is executed last")

Result:
>>>
1
This is executed last

ZeroDivisionError: division by zero
During handling of the above exception, another exception occurred:
NameError: name 'unknown_var' is not defined
>>>

------------------------------------------------------------------------------------------------------------------------
Raising Exceptions

You can raise exceptions by using the raise statement.
print(1)
raise ValueError
print(2)

Result:
>>>
1
ValueError
>>>

You need to specify the type of the exception raised.
-------------------------------------------------------------------------------------------------------------------------
Raising Exceptions

Exceptions can be raised with arguments that give detail about them.
For example:
name = "123"
raise NameError("Invalid name!")

Result:
>>>
NameError: Invalid name!
>>>

---------------------------------------------------------------------------------------------------------------------------
Raising Exceptions

In except blocks, the raise statement can be used without arguments to re-raise whatever exception occurred.
For example:
try:
num = 5 / 0
except:
print("An error occurred")
raise

Result:
>>>
An error occurred

ZeroDivisionError: division by zero
>>>
----------------------------------------------------------------------------------------------------------------------------
Assertions

An assertion is a sanity-check that you can turn on or turn off when you have finished testing the program.
An expression is tested, and if the result comes up false, an exception is raised.
Assertions are carried out through use of the assert statement.
print(1)
assert 2 + 2 == 4
print(2)
assert 1 + 1 == 3
print(3)

Result:
>>>
1
2
AssertionError
>>>

Programmers often place assertions at the start of a function to check for valid input, and after a
function call to check for valid output.
-----------------------------------------------------------------------------------------------------------------------------
Assertions

The assert can take a second argument that is passed to the AssertionError raised if the assertion fails.
temp = -10
assert (temp >= 0), "Colder than absolute zero!"

Result:
>>>
AssertionError: Colder than absolute zero!
>>>

AssertionError exceptions can be caught and handled like any other exception using the try-except
statement, but if not handled, this type of exception will terminate the program.
------------------------------------------------------------------------------------------------------------------------------
Opening Files

You can use Python to read and write the contents of files.
Text files are the easiest to manipulate. Before a file can be edited, it must be opened, using the open function.
myfile = open("filename.txt")

The argument of the open function is the path to the file. If the file is in the same directory as
the program, you can specify only its name.
------------------------------------------------------------------------------------------------------------------------------
Opening Files

You can specify the mode used to open a file by applying a second argument to the open function.
Sending "r" means open in read mode, which is the default.
Sending "w" means write mode, for rewriting the contents of a file.
Sending "a" means append mode, for adding new content to the end of the file.

Adding "b" to a mode opens it in binary mode, which is used for non-text files (such as image and sound files).
For example:
# write mode
open("filename.txt", "w")

# read mode
open("filename.txt", "r")
open("filename.txt")

# binary write mode
open("filename.txt", "wb")
-------------------------------------------------------------------------------------------------------------------------------
Opening Files

Once a file has been opened and used, you should close it.
This is done with the close method of the file object.
file = open("filename.txt", "w")
# do stuff to the file
file.close()

We will read/write content to files in the upcoming lessons.
-------------------------------------------------------------------------------------------------------------------------------
Reading Files

The contents of a file that has been opened in text mode can be read using the read method.
file = open("filename.txt", "r")
cont = file.read()
print(cont)
file.close()

This will print all of the contents of the file "filename.txt".
--------------------------------------------------------------------------------------------------------------------------------
Reading Files

To read only a certain amount of a file, you can provide a number as an argument to the read function. This determines
the number of bytes that should be read.
You can make more calls to read on the same file object to read more of the file byte by byte. With no argument,
read returns the rest of the file.
file = open("filename.txt", "r")
print(file.read(16))
print(file.read(4))
print(file.read(4))
print(file.read())
file.close()
----------------------------------------------------------------------------------------------------------------------------------
Reading Files

After all contents in a file have been read, any attempts to read further from that file will return an empty string,
because you are trying to read from the end of the file.
file = open("filename.txt", "r")
file.read()
print("Re-reading")
print(file.read())
print("Finished")
file.close()

Result:
>>>
Re-reading

Finished
>>>
--------------------------------------------------------------------------------------------------------------------------------
Reading Files

To retrieve each line in a file, you can use the readlines method to return a list in which each element is a line in the file.
For example:
file = open("filename.txt", "r")
print(file.readlines())
file.close()

Result:
>>>
['Line 1 text \n', 'Line 2 text \n', 'Line 3 text']
>>>

You can also use a for loop to iterate through the lines in the file:
file = open("filename.txt", "r")

for line in file:
print(line)

file.close()

Result:
>>>
Line 1 text

Line 2 text

Line 3 text
>>>

In the output, the lines are separated by blank lines, as the print function automatically adds a new line at the end of its output.
------------------------------------------------------------------------------------------------------------------------------
Writing Files

To write to files you use the write method, which writes a string to the file.
For example:
file = open("newfile.txt", "w")
file.write("This has been written to a file")
file.close()

file = open("newfile.txt", "r")
print(file.read())
file.close()

Result:
>>>
This has been written to a file
>>>

The "w" mode will create a file, if it does not already exist.
--------------------------------------------------------------------------------------------------------------------------------
Writing Files

When a file is opened in write mode, the file's existing content is deleted.
file = open("newfile.txt", "r")
print("Reading initial contents")
print(file.read())
print("Finished")
file.close()

file = open("newfile.txt", "w")
file.write("Some new text")
file.close()

file = open("newfile.txt", "r")
print("Reading new contents")
print(file.read())
print("Finished")
file.close()

Result:
>>>
Reading initial contents
some initial text
Finished
Reading new contents
Some new text
Finished
>>>

As you can see, the content of the file has been overwritten.
-------------------------------------------------------------------------------------------------------------------------------
Writing Files

The write method returns the number of bytes written to a file, if successful.
msg = "Hello world!"
file = open("newfile.txt", "w")
amount_written = file.write(msg)
print(amount_written)
file.close()

Result:
>>>
12
>>>
---------------------------------------------------------------------------------------------------------------------------------
Working with Files

It is good practice to avoid wasting resources by making sure that files are always closed after they have been used.
One way of doing this is to use try and finally.
try:
f = open("filename.txt")
print(f.read())
finally:
f.close()

This ensures that the file is always closed, even if an error occurs.
----------------------------------------------------------------------------------------------------------------------------------
Working with Files

An alternative way of doing this is using with statements. This creates a temporary variable (often called f), which is only
accessible in the indented block of the with statement.
with open("filename.txt") as f:
print(f.read())

The file is automatically closed at the end of the with statement, even if exceptions occur within it.
-----------------------------------------------------------------------------------------------------------------------------------
None

The None object is used to represent the absence of a value.
It is similar to null in other programming languages.
Like other "empty" values, such as 0, [] and the empty string, it is False when converted to a Boolean variable.
When entered at the Python console, it is displayed as the empty string.
>>> None == None
True
>>> None
>>> print(None)
None
>>>
------------------------------------------------------------------
None

The None object is returned by any function that doesn't explicitly return anything else.
def some_func():
print("Hi!")

var = some_func()
print(var)

Result:
>>>
Hi!
None
>>>

--------------------------------------------------------------------

Dictionaries

Dictionaries are data structures used to map arbitrary keys to values.
Lists can be thought of as dictionaries with integer keys within a certain range.
Dictionaries can be indexed in the same way as lists, using square brackets containing keys.
Example:
ages = {"Dave": 24, "Mary": 42, "John": 58}
print(ages["Dave"])
print(ages["Mary"])

Result:
>>>
24
42
>>>

Each element in a dictionary is represented by a key:value pair.
----------------------------------------------------------------------

Dictionaries

Trying to index a key that isn't part of the dictionary returns a KeyError.
Example:
primary = {
"red": [255, 0, 0],
"green": [0, 255, 0],
"blue": [0, 0, 255],
}

print(primary["red"])
print(primary["yellow"])

Result:
>>>
[255, 0, 0]

KeyError: 'yellow'
>>>

As you can see, a dictionary can store any types of data as values.
An empty dictionary is defined as {}.
-------------------------------------------------------------------------
Dictionaries

Only immutable objects can be used as keys to dictionaries. Immutable objects are those
that can't be changed. So far, the only mutable objects you've come across are lists and
dictionaries. Trying to use a mutable object as a dictionary key causes a TypeError.
bad_dict = {
[1, 2, 3]: "one two three",
}

Result:
>>>
TypeError: unhashable type: 'list'
>>>
--------------------------------------------------------------------------
Dictionaries

Just like lists, dictionary keys can be assigned to different values.
However, unlike lists, a new dictionary key can also be assigned a value, not just ones that already exist.
squares = {1: 1, 2: 4, 3: "error", 4: 16,}
squares[8] = 64
squares[3] = 9
print(squares)

Result:
{8: 64, 1: 1, 2: 4, 3: 9, 4: 16}
-----------------------------------------------------------------------------
Dictionaries

To determine whether a key is in a dictionary, you can use in and not in, just
as you can for a list.
Example:
nums = {
1: "one",
2: "two",
3: "three",
}
print(1 in nums)
print("three" in nums)
print(4 not in nums)

Result:
>>>
True
False
True
>>>
-------------------------------------------------------------------------------
Dictionaries

A useful dictionary method is get. It does the same thing as indexing, but if the key
is not found in the dictionary it returns another specified value instead ('None', by default).
Example:
pairs = {1: "apple",
"orange": [2, 3, 4],
True: False,
None: "True",
}

print(pairs.get("orange"))
print(pairs.get(7))
print(pairs.get(12345, "not in dictionary"))


Result:
>>>
[2, 3, 4]
None
not in dictionary
>>>
---------------------------------------------------------------------------------
Tuples

Tuples are very similar to lists, except that they are immutable (they cannot be changed).
Also, they are created using parentheses, rather than square brackets.
Example:
words = ("spam", "eggs", "sausages",)

You can access the values in the tuple with their index, just as you did with lists:
print(words[0])

Trying to reassign a value in a tuple causes a TypeError.
words[1] = "cheese"

Result:
>>>
TypeError: 'tuple' object does not support item assignment
>>>

Like lists and dictionaries, tuples can be nested within each other.
--------------------------------------------------------------------------------------

Fill in the blanks to create a list, dictionary, and tuple:
# list
list = ["one", "two"]
# dictionary
dict = {1:"one", 2:"two"}
# tuple
tp = ("one", "two")
------------------------------------------------------------------
Tuples

Tuples can be created without the parentheses, by just separating
the values with commas.
Example:
my_tuple = "one", "two", "three"
print(my_tuple[0])

Result:
>>>
one
>>>

An empty tuple is created using an empty parenthesis pair.
tpl = ()
Tuples are faster than lists, but they cannot be changed.
----------------------------------------------------------------------
List Slices

List slices provide a more advanced way of retrieving values from a
list. Basic list slicing involves indexing a list with two
colon-separated integers. This returns a new list containing all
the values in the old list between the indices.

Example:
squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
print(squares[2:6])
print(squares[3:8])
print(squares[0:1])

Result:
>>>
[4, 9, 16, 25]
[9, 16, 25, 36, 49]
[0]
>>>

Like the arguments to range, the first index provided in a slice is
included in the result, but the second isn't.
------------------------------------------------------------------------
List Slices

If the first number in a slice is omitted, it is taken to be the start of the list.
If the second number is omitted, it is taken to be the end.
Example:
squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
print(squares[:7])
print(squares[7:])

Result:
>>>
[0, 1, 4, 9, 16, 25, 36]
[49, 64, 81]
>>>

Slicing can also be done on tuples.
---------------------------------------------------------------------------
List Slices

List slices can also have a third number, representing the step, to include
only alternate values in the slice.
squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
print(squares[::2])
print(squares[2:8:3])

Result:
>>>
[0, 4, 16, 36, 64]
[4, 25]
>>>

[2:8:3] will include elements starting from the 2nd index up to the 8th with a step of 3.
----------------------------------------------------------------------------
List Slices

Negative values can be used in list slicing (and normal list indexing). When negative
values are used for the first and second values in a slice (or a normal index), they
count from the end of the list.
squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
print(squares[1:-1])

Result:
>>>
[1, 4, 9, 16, 25, 36, 49, 64]
>>>

If a negative value is used for the step, the slice is done backwards.
Using [::-1] as a slice is a common and idiomatic way to reverse a list.
------------------------------------------------------------------------------
List Comprehensions

List comprehensions are a useful way of quickly creating lists whose contents
obey a simple rule.
For example, we can do the following:
# a list comprehension
cubes = [i**3 for i in range(5)]

print(cubes)

Result:
>>>
[0, 1, 8, 27, 64]
>>>

List comprehensions are inspired by set-builder notation in mathematics.
----------------------------------------------------------------------------
List Comprehensions

A list comprehension can also contain an if statement to enforce a condition on values in the list.
Example:
evens=[i**2 for i in range(10) if i**2 % 2 == 0]

print(evens)

Result:
>>>
[0, 4, 16, 36, 64]
>>>
---------------------------------------------------------------------------
List Comprehensions

Trying to create a list in a very extensive range will result in a MemoryError.
This code shows an example where the list comprehension runs out of memory.
even = [2*i for i in range(10**100)]

Result:
>>>
MemoryError
>>>

This issue is solved by generators, which are covered in the next module.
-----------------------------------------------------------------------------
String Formatting

So far, to combine strings and non-strings, you've converted the non-strings to strings and added them.
String formatting provides a more powerful way to embed non-strings within strings. String formatting
uses a string's format method to substitute a number of arguments in the string.
Example:
# string formatting
nums = [4, 5, 6]
msg = "Numbers: {0} {1} {2}". format(nums[0], nums[1], nums[2])
print(msg)

Result:
>>>
Numbers: 4 5 6
>>>

Each argument of the format function is placed in the string at the corresponding position, which is
determined using the curly braces { }.
-----------------------------------------------------------------------------
What is the result of this code?
print("{0}{1}{0}".format("abra", "cad"))

abracadabra
------------------------------------------------------------------------------
String Formatting

String formatting can also be done with named arguments.
Example:
a = "{x}, {y}".format(x=5, y=12)
print(a)

Result:
>>>
5, 12
>>>
------------------------------------------------------------------------------
String Functions

Python contains many useful built-in functions and methods to accomplish common tasks.
join - joins a list of strings with another string as a separator.
replace - replaces one substring in a string with another.
startswith and endswith - determine if there is a substring at the start and end of a string, respectively.
To change the case of a string, you can use lower and upper.
The method split is the opposite of join, turning a string with a certain separator into a list.
Some examples:
print(", ".join(["spam", "eggs", "ham"]))
#prints "spam, eggs, ham"

print("Hello ME".replace("ME", "world"))
#prints "Hello world"

print("This is a sentence.".startswith("This"))
# prints "True"

print("This is a sentence.".endswith("sentence."))
# prints "True"

print("This is a sentence.".upper())
# prints "THIS IS A SENTENCE."

print("AN ALL CAPS SENTENCE".lower())
#prints "an all caps sentence"

print("spam, eggs, ham".split(", "))
#prints "['spam', 'eggs', 'ham']"
-----------------------------------------------------------------------------------
Fill in the blanks to turn the string uppercase.
a = "Spam"
b = a.upper()
-----------------------------------------------------------------------------------
Numeric Functions

To find the maximum or minimum of some numbers or a list, you can use max or min.
To find the distance of a number from zero (its absolute value), use abs.
To round a number to a certain number of decimal places, use round.
To find the total of a list, use sum.
Some examples:
print(min(1, 2, 3, 4, 0, 2, 1))
print(max([1, 4, 9, 2, 5, 6, 8]))
print(abs(-99))
print(abs(42))
print(sum([1, 2, 3, 4, 5]))

Result:
>>>
0
9
99
42
15
>>>
-------------------------------------------------------------------------------------
List Functions

Often used in conditional statements, all and any take a list as an argument, and
return True if all or any (respectively) of their arguments evaluate to True (and False otherwise).
The function enumerate can be used to iterate through the values and indices of a list simultaneously.
Example:
nums = [55, 44, 33, 22, 11]

if all([i > 5 for i in nums]):
print("All larger than 5")

if any([i % 2 == 0 for i in nums]):
print("At least one is even")

for v in enumerate(nums):
print(v)


Result:
>>>
All larger than 5
At least one is even
(0, 55)
(1, 44)
(2, 33)
(3, 22)
(4, 11)
>>>
--------------------------------------------------------------------------------------
Text Analyzer

This is an example project, showing a program that analyzes a sample file to find
what percentage of the text each character occupies.
This section shows how a file could be open and read.
filename = input("Enter a filename: ")

with open(filename) as f:
text = f.read()

print(text)

Result:
>>>
Enter a filename: test.txt
Ornhgvshy vf orggre guna htyl.
Rkcyvpvg vf orggre guna vzcyvpvg.
Fvzcyr vf orggre guna pbzcyvpngrq.
Syng vf orggre guna arfgrq.
Fcenfr fv orggre guna qrafr.
Ernqnovyvgl pbhagf.
Fcrpvny pnfrf nera'g fcrpvny rabthu gb oernx gur ehyrf.
Nygubhtu cenpgvpnyvgl orgnf chevgl.
Reebef fubhyq arire cnff fvyragyl.
Hayrff rkcyvpvgyl fvyraprq.
Va gur snpr bs nzovthvgl, ershfr gur grzcgngvba bg thrff.
Gurer fubhyq or bar-- naq cersrenoylbayl bar --boivbhf jnl gb qb vg.
Nygubhtu gung jnl znl abg or boivbhf ng svefg hayrff lbh'er Qhgpu.
Abj vf orggre guna arrire.
Nygubhtu arire vf bsgra orggre guna *evtug* abj.
Vs gur vzcyrzragngvba vf uneq gb rkcynva, vg'f n onq vqrn.
Vs gur vzcyrzragngvba vf rnfl gb rkcynva, vg znl or n tbbq vqrn.
Anzrfcnprf ner bar ubaxvat terng vqrn -- yrg'f qb zber bs gubfr!
This is sample content for demonstration purposes only.
---------------------------------------------------------------------------------------
Text Analyzer

This part of the program shows a function that counts how many times a character
occurs in a string.
def count_char(text, char):
count = 0
for c in text:
if c == char:
count += 1
return count

This function takes as its arguments the text of the file and one character,
returning the number of times that character appears in the text.
Now we can call it for our file.
filename = input("Enter a filename: ")
with open(filename) as f:
text = f.read()

print(count_char(text, "r"))

Result:
>>>
Enter a filename: test.txt
83
>>>

The character "r" appears 83 times in the file.
-----------------------------------------------------------------------------------------
Text Analyzer

The next part of the program finds what percentage of the text each character of the
alphabet occupies.
for char in "abcdefghijklmnopqrstuvwxyz":
perc = 100 * count_char(text, char) / len(text)
print("{0} - {1}%".format(char, round(perc, 2)))

Let's put it all together and run the program:
def count_char(text, char):
count = 0
for c in text:
if c == char:
count += 1
return count

filename = input("Enter a filename: ")
with open(filename) as f:
text = f.read()

for char in "abcdefghijklmnopqrstuvwxyz":
perc = 100 * count_char(text, char) / len(text)
print("{0} - {1}%".format(char, round(perc, 2)))
Try It Yourself

Result:
Enter a filename: test.txt
a - 4.68%
b - 4.94%
c - 2.28%
----------------------------------------------------------------------------------------
Functional Programming

Functional programming is a style of programming that (as the name suggests) is based around functions.
A key part of functional programming is higher-order functions. We have seen this idea briefly in
the previous lesson on functions as objects. Higher-order functions take other functions as
arguments, or return them as results.

Example:
def apply_twice(func, arg):
return func(func(arg))

def add_five(x):
return x + 5

print(apply_twice(add_five, 10))

Result:
>>>
20
>>>

The function apply_twice takes another function as its argument, and calls
it twice inside its body.
---------------------------------------------------------------------------------------
Pure Functions

Functional programming seeks to use pure functions. Pure functions have no side effects, and
return a value that depends only on their arguments.
This is how functions in math work: for example, The cos(x) will, for the same value of x,
always return the same result.
Below are examples of pure and impure functions.
Pure function:
def pure_function(x, y):
temp = x + 2*y
return temp / (2*x + y)

Impure function:
some_list = []

def impure(arg):
some_list.append(arg)

The function above is not pure, because it changed the state of some_list.
--------------------------------------------------------------------------------------
Pure Functions

Using pure functions has both advantages and disadvantages.
Pure functions are:
- easier to reason about and test.
- more efficient. Once the function has been evaluated for an input, the result
can be stored and referred to the next time the function of that input is needed, reducing
the number of times the function is called. This is called memoization.
- easier to run in parallel.
The main disadvantage of using only pure functions is that they majorly complicate
the otherwise simple task of I/O, since this appears to inherently require side effects.
They can also be more difficult to write in some situations.
---------------------------------------------------------------------------------------
Lambdas

Creating a function normally (using def) assigns it to a variable automatically.
This is different from the creation of other objects - such as strings and
integers - which can be created on the fly, without assigning them to a variable.
The same is possible with functions, provided that they are created using lambda
syntax. Functions created this way are known as anonymous.
This approach is most commonly used when passing a simple function as an argument
to another function. The syntax is shown in the next example and consists of the
lambda keyword followed by a list of arguments, a colon, and the expression to evaluate and return.
def my_func(f, arg):
return f(arg)

my_func(lambda x: 2*x*x, 5)

Lambda functions get their name from lambda calculus, which is a model of
computation invented by Alonzo Church.
----------------------------------------------------------------------------------------
Lambdas

Lambda functions aren't as powerful as named functions.
They can only do things that require a single expression - usually equivalent
to a single line of code.
Example:
#named function
def polynomial(x):
return x**2 + 5*x + 4
print(polynomial(-4))

#lambda
print((lambda x: x**2 + 5*x + 4) (-4))

Result:
>>>
0
0
>>>

In the code above, we created an anonymous function on the fly and called
it with an argument.
-----------------------------------------------------------------------------------------
Lambdas

Lambda functions can be assigned to variables, and used like normal functions.
Example:
double = lambda x: x * 2
print(double(7))

Result:
>>>
14
>>>

However, there is rarely a good reason to do this - it is usually better to define
a function with def instead.
--------------------------------------------------------------------------------------------
map

The built-in functions map and filter are very useful higher-order functions that operate
on lists (or similar objects called iterables).
The function map takes a function and an iterable as arguments, and returns a new iterable
with the function applied to each argument.
Example:
def add_five(x):
return x + 5

nums = [11, 22, 33, 44, 55]
result = list(map(add_five, nums))
print(result)

Result:
>>>
[16, 27, 38, 49, 60]
>>>

We could have achieved the same result more easily by using lambda syntax.
nums = [11, 22, 33, 44, 55]

result = list(map(lambda x: x+5, nums))
print(result)

To convert the result into a list, we used list explicitly.
---------------------------------------------------------------------------------------------
filter

The function filter filters an iterable by removing items that don't match a predicate
(a function that returns a Boolean).
Example:
nums = [11, 22, 33, 44, 55]
res = list(filter(lambda x: x%2==0, nums))
print(res)
Try It Yourself

Result:
>>>
[22, 44]
>>>

Like map, the result has to be explicitly converted to a list if you want to print it.
-----------------------------------------------------------------------------------------------
Generators

Generators are a type of iterable, like lists or tuples.
Unlike lists, they don't allow indexing with arbitrary indices, but they can still be iterated
through with for loops.
They can be created using functions and the yield statement.
Example:
def countdown():
i=5
while i > 0:
yield i
i -= 1

for i in countdown():
print(i)

Result:
>>>
5
4
3
2
1

The yield statement is used to define a generator, replacing the return of a function
to provide a result to its caller without destroying local variables.
-------------------------------------------------------------------------------------------------
Generators

Due to the fact that they yield one item at a time, generators don't have the memory
restrictions of lists.
In fact, they can be infinite!
def infinite_sevens():
while True:
yield 7

for i in infinite_sevens():
print(i)

Result:
>>>
7
7
7
7
7
7
7
...

In short, generators allow you to declare a function that behaves like an iterator,
i.e. it can be used in a for loop.
---------------------------------------------------------------------------------------------------
Generators

Finite generators can be converted into lists by passing them as arguments to the list function.
def numbers(x):
for i in range(x):
if i % 2 == 0:
yield i

print(list(numbers(11)))

Result:
>>>
[0, 2, 4, 6, 8, 10]
>>>

Using generators results in improved performance, which is the result of the lazy (on demand)
generation of values, which translates to lower memory usage. Furthermore, we do not need to wait
until all the elements have been generated before we start to use them.
-------------------------------------------------------------------------------------------------------
Decorators

Decorators provide a way to modify functions using other functions.
This is ideal when you need to extend the functionality of functions that you don't want to modify.
Example:
def decor(func):
def wrap():
print("============")
func()
print("============")
return wrap

def print_text():
print("Hello world!")

decorated = decor(print_text)
decorated()

We defined a function named decor that has a single parameter func. Inside decor, we defined a
nested function named wrap. The wrap function will print a string, then call func(), and print
another string. The decor function returns the wrap function as its result.
We could say that the variable decorated is a decorated version of print_text - it's print_text
plus something.
In fact, if we wrote a useful decorator we might want to replace print_text with the decorated
version altogether so we always got our "plus something" version of print_text.
This is done by re-assigning the variable that contains our function:
print_text = decor(print_text)
print_text()

Now print_text corresponds to our decorated version.
--------------------------------------------------------------------------------------------------------
Decorators

In our previous example, we decorated our function by replacing the variable containing the function
with a wrapped version.
def print_text():
print("Hello world!")

print_text = decor(print_text)


This pattern can be used at any time, to wrap any function.
Python provides support to wrap a function in a decorator by pre-pending the function definition
with a decorator name and the @ symbol.
If we are defining a function we can "decorate" it with the @ symbol like:
@decor
def print_text():
print("Hello world!")


This will have the same result as the above code.
A single function can have multiple decorators.
---------------------------------------------------------------------------------------------------------
Recursion

Recursion is a very important concept in functional programming.
The fundamental part of recursion is self-reference - functions calling themselves. It is used to
solve problems that can be broken up into easier sub-problems of the same type.

A classic example of a function that is implemented recursively is the factorial function,
which finds the product of all positive integers below a specified number.
For example, 5! (5 factorial) is 5 * 4 * 3 * 2 * 1 (120). To implement this recursively,
notice that 5! = 5 * 4!, 4! = 4 * 3!, 3! = 3 * 2!, and so on. Generally, n! = n * (n-1)!.
Furthermore, 1! = 1. This is known as the base case, as it can be calculated without
performing any more factorials.
Below is a recursive implementation of the factorial function.
def factorial(x):
if x == 1:
return 1
else:
return x * factorial(x-1)

print(factorial(5))
Try It Yourself

Result:
>>>
120
>>>

The base case acts as the exit condition of the recursion.
---------------------------------------------------------------------------------------------------------
Recursion

Recursive functions can be infinite, just like infinite while loops. These often occur when you
forget to implement the base case.
Below is an incorrect version of the factorial function. It has no base case, so it
runs until the interpreter runs out of memory and crashes.
def factorial(x):
return x * factorial(x-1)

print(factorial(5))

Result:
>>>
RuntimeError: maximum recursion depth exceeded
>>>
-----------------------------------------------------------------------------------------------------------
Recursion

Recursion can also be indirect. One function can call a second, which calls the first, which
calls the second, and so on. This can occur with any number of functions.
Example:
def is_even(x):
if x == 0:
return True
else:
return is_odd(x-1)

def is_odd(x):
return not is_even(x)


print(is_odd(17))
print(is_even(23))
Try It Yourself

Result:
>>>
True
False
>>>
--------------------------------------------------------------------------------------------------------------
Sets

Sets are data structures, similar to lists or dictionaries. They are created using curly braces,
or the set function. They share some functionality with lists, such as the use of in to check
whether they contain a particular item.
num_set = {1, 2, 3, 4, 5}
word_set = set(["spam", "eggs", "sausage"])

print(3 in num_set)
print("spam" not in word_set)

Result:
>>>
True
False
>>>

To create an empty set, you must use set(), as {} creates an empty dictionary.
---------------------------------------------------------------------------------------------------------------
Sets

Sets differ from lists in several ways, but share several list operations such as len.
They are unordered, which means that they can't be indexed.
They cannot contain duplicate elements.
Due to the way they're stored, it's faster to check whether an item is part of a set, rather
than part of a list.
Instead of using append to add to a set, use add.
The method remove removes a specific element from a set; pop removes an arbitrary element.
nums = {1, 2, 1, 3, 1, 4, 5, 6}
print(nums)
nums.add(-7)
nums.remove(3)
print(nums)
Try It Yourself

Result:
>>>
{1, 2, 3, 4, 5, 6}
{1, 2, 4, 5, 6, -7}
>>>

Basic uses of sets include membership testing and the elimination of duplicate entries.
----------------------------------------------------------------------------------------------------------------
Sets

Sets can be combined using mathematical operations.
The union operator | combines two sets to form a new one containing items in
either.
The intersection operator & gets items only in both.
The difference operator - gets items in the first set but not in the second.
The symmetric difference operator ^ gets items in either set, but not both.
first = {1, 2, 3, 4, 5, 6}
second = {4, 5, 6, 7, 8, 9}

print(first | second)
print(first & second)
print(first - second)
print(second - first)
print(first ^ second)

Result:
>>>
{1, 2, 3, 4, 5, 6, 7, 8, 9}
{4, 5, 6}
{1, 2, 3}
{8, 9, 7}
{1, 2, 3, 7, 8, 9}
>>>
------------------------------------------------------------------------------------
Data Structures

As we have seen in the previous lessons, Python supports the following data
structures: lists, dictionaries, tuples, sets.

When to use a dictionary:
- When you need a logical association between a key:value pair.
- When you need fast lookup for your data, based on a custom key.
- When your data is being constantly modified. Remember, dictionaries are mutable.

When to use the other types:
- Use lists if you have a collection of data that does not need random access.
Try to choose lists when you need a simple, iterable collection that is modified frequently.
- Use a set if you need uniqueness for the elements.
- Use tuples when your data cannot change.
Many times, a tuple is used in combination with a dictionary, for example, a tuple
might represent a key, because it's immutable.
--------------------------------------------------------------------------------------
itertools

The module itertools is a standard library that contains several functions that
are useful in functional programming.
One type of function it produces is infinite iterators.
The function count counts up infinitely from a value.
The function cycle infinitely iterates through an iterable (for instance a list or string).
The function repeat repeats an object, either infinitely or a specific number of times.
Example:
from itertools import count

for i in count(3):
print(i)
if i >=11:
break

Result:
>>>
3
4
5
6
7
8
9
10
11
>>>
-----------------------------------------------------------------------------------------
itertools

There are many functions in itertools that operate on iterables, in a similar way to
map and filter.
Some examples:
takewhile - takes items from an iterable while a predicate function remains true;
chain - combines several iterables into one long one;
accumulate - returns a running total of values in an iterable.
from itertools import accumulate, takewhile

nums = list(accumulate(range(8)))
print(nums)
print(list(takewhile(lambda x: x<= 6, nums)))

Result:
>>>
[0, 1, 3, 6, 10, 15, 21, 28]
[0, 1, 3, 6]
>>>
-------------------------------------------------------------------------------------------
itertools

There are also several combinatoric functions in itertool, such as product and permutation.
These are used when you want to accomplish a task with all possible combinations of some items.
Example:
from itertools import product, permutations

letters = ("A", "B")
print(list(product(letters, range(2))))
print(list(permutations(letters)))

Result:
>>>
[('A', 0), ('A', 1), ('B', 0), ('B', 1)]
[('A', 'B'), ('B', 'A')]
>>>
-------------------------------------------------------------------------------------------

Classes

We have previously looked at two paradigms of programming - imperative
(using statements, loops, and functions as subroutines), and functional
(using pure functions, higher-order functions, and recursion).

Another very popular paradigm is object-oriented programming (OOP).
Objects are created using classes, which are actually the focal point of OOP.
The class describes what the object will be, but is separate from the object itself.
In other words, a class can be described as an object's blueprint, description, or
definition.
You can use the same class as a blueprint for creating multiple different objects.

Classes are created using the keyword class and an indented block, which contains
class methods (which are functions).
Below is an example of a simple class and its objects.

class Cat:
def __init__(self, color, legs):
self.color = color
self.legs = legs

felix = Cat("ginger", 4)
rover = Cat("dog-colored", 4)
stumpy = Cat("brown", 3)

This code defines a class named Cat, which has two attributes: color and legs.
Then the class is used to create 3 separate objects of that class.
Tap Continue to learn more!
-------------------------------------------------------------------------------------------
__init__

The __init__ method is the most important method in a class.
This is called when an instance (object) of the class is created, using the class
name as a function.

All methods must have self as their first parameter, although it isn't explicitly
passed, Python adds the self argument to the list for you; you do not need to include
it when you call the methods. Within a method definition, self refers to the instance
calling the method.

Instances of a class have attributes, which are pieces of data associated with them.
In this example, Cat instances have attributes color and legs. These can be accessed
by putting a dot, and the attribute name after an instance.
In an __init__ method, self.attribute can therefore be used to set the initial
value of an instance's attributes.

Example:
class Cat:
def __init__(self, color, legs):
self.color = color
self.legs = legs

felix = Cat("ginger", 4)
print(felix.color)


Result:
>>>
ginger
>>>

In the example above, the __init__ method takes two arguments and assigns them to the
object's attributes. The __init__ method is called the class constructor.
------------------------------------------------------------------------------------------
Methods

Classes can have other methods defined to add functionality to them.
Remember, that all methods must have self as their first parameter.
These methods are accessed using the same dot syntax as attributes.
Example:
class Dog:
def __init__(self, name, color):
self.name = name
self.color = color

def bark(self):
print("Woof!")

fido = Dog("Fido", "brown")
print(fido.name)
fido.bark()
Try It Yourself

Result:
>>>
Fido
Woof!
>>>

Classes can also have class attributes, created by assigning variables
within the body of the class. These can be accessed either from instances
of the class, or the class itself.
Example:
class Dog:
legs = 4
def __init__(self, name, color):
self.name = name
self.color = color

fido = Dog("Fido", "brown")
print(fido.legs)
print(Dog.legs)
Try It Yourself

Result:
>>>
4
4
>>>

Class attributes are shared by all instances of the class.
-----------------------------------------------------------------------------
Classes

Trying to access an attribute of an instance that isn't defined causes an
AttributeError. This also applies when you call an undefined method.

Example:
class Rectangle:
def __init__(self, width, height):
self.width = width
self.height = height

rect = Rectangle(7, 8)
print(rect.color)
Try It Yourself

Result:
>>>
AttributeError: 'Rectangle' object has no attribute 'color'
>>>
-------------------------------------------------------------------------------
Inheritance

Inheritance provides a way to share functionality between classes.
Imagine several classes, Cat, Dog, Rabbit and so on. Although they may differ
in some ways (only Dog might have the method bark), they are likely to be similar
in others (all having the attributes color and name).
This similarity can be expressed by making them all inherit from a superclass
Animal, which contains the shared functionality.
To inherit a class from another class, put the superclass name in parentheses
after the class name.

Example:
class Animal:
def __init__(self, name, color):
self.name = name
self.color = color

class Cat(Animal):
def purr(self):
print("Purr...")

class Dog(Animal):
def bark(self):
print("Woof!")

fido = Dog("Fido", "brown")
print(fido.color)
fido.bark()

Result:
>>>
brown
Woof!
>>>
-------------------------------------------------------------------------------