ls scripts/

cat scripts/run_hello_world.py

!python scripts/run_hello_world.py

"""
Explanation: Introduction to Python programming
The following notebook serves as an introduction to Python for the course "Data Mining".
For any comments or suggestions you can contact me at 
charlotte (dot) laclau (at) univ-grenoble-alpes (dot) fr
This notebook was created by : 
georgios (dot) balikas (at) imag (dot) fr
Pseudocode
Pseudocode is a high level description of the operating principles of a computed program. It is intended for human understanding rather than machine understanding. The purpose of using pseudocode is that it is easier for people to understand it than conventional programming language code, and that it is an efficient and environment-independent description of the key principles of an algorithm. Here is the pseudocode example for summing the elements of a list:
cnt &lt;- 0
for element in list:
    cnt &lt;- cnt + element
Writing pseudocode for complex programs, before starting with the machine code, may help to avoid logical errors and can save time while debbuging. 
Python is an interpreted language
That is the instructions are first read and executed by another program one the fly. Therefore, there is no need to run a compilation stage. Interpreted languages can be more convenient
for dynamic languages
Python program files


Python code is usually stored in text files with the file ending ".py":
myprogram.py



Every line in a Python program file is assumed to be a Python statement, or part thereof. 

The only exception is comment lines, which start with the character # (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter.



To run our Python program from the command line we use:
$ python myprogram.py



Example:
End of explanation
"""


import math

"""
Explanation: Note from the above that we have used the Ipython notebook (or a Jupyter Notebook running a python kernel) as a terminal where we executed terminal commands without arguments. In the case of a command with an argument, one needs to add '!' in the beginning of the line e.g., "!python scripts/run_hello_world.py"
TBD: Count the number of lines in 'lines.txt', which is under the directory 'scripts'.
IPython notebooks
This file - an IPython notebook -  does not follow the standard pattern with Python code in a text file. Instead, an IPython notebook is stored as a file in the JSON format. The advantage is that we can mix formatted text, Python code and code output. It requires the IPython notebook server to run it though, and therefore isn't a stand-alone Python program as described above. Other than that, there is no difference between the Python code that goes into a program file or an IPython notebook.
Modules
Most of the functionality in Python is provided by modules. The Python Standard Library is a large collection of modules that provides cross-platform implementations of common facilities such as access to the operating system, file I/O, string management, network communication, and much more.
References

The Python Language Reference: http://docs.python.org/2/reference/index.html
The Python Standard Library: http://docs.python.org/2/library/

To use a module in a Python program it first has to be imported. A module can be imported using the import statement. For example, to import the module math, which contains many standard mathematical functions, we can do:
End of explanation
"""


import math

x = math.cos(2 * math.pi)

print(x)

"""
Explanation: This includes the whole module and makes it available for use later in the program. For example, we can do:
End of explanation
"""


from math import * #This is a bad practise 

x = cos(2 * pi)
print(x)

"""
Explanation: Alternatively, we can chose to import all symbols (functions and variables) in a module to the current namespace (so that we don't need to use the prefix "math." every time we use something from the math module:
End of explanation
"""


from math import cos, pi # Importing only the functions you need from a particular package is a much better practise. 

x = cos(2 * pi)

print(x)

"""
Explanation: This pattern can be very convenient, but in large programs that include many modules it is often a good idea to keep the symbols from each module in their own namespaces, by using the import math pattern. This would elminate potentially confusing problems with name space collisions.
As a third alternative, we can chose to import only a few selected symbols from a module by explicitly listing which ones we want to import instead of using the wildcard character *:
End of explanation
"""


import math

print(dir(math))

"""
Explanation: Looking at what a module contains, and its documentation
Once a module is imported, we can list the symbols it provides using the dir function:
End of explanation
"""


help(math.log)

"""
Explanation: And using the function help we can get a description of each function (almost .. not all functions have docstrings, as they are technically called, but the vast majority of functions are documented this way).
End of explanation
"""


?math.log

log(10)

from math import log
log(10, 2)

from numpy import log
log(10, 2) # This will create an error. This is why importing function from several packages is not a good practise.


?log

"""
Explanation: Another way to accomplish that is by using the "?" in front of the name of the function.
End of explanation
"""


# variable assignments
x = 1.0
my_variable = 12.2

"""
Explanation: We can also use the help function directly on modules: Try
help(math)

Some very useful modules form the Python standard library are os, sys, math, shutil, re, subprocess, multiprocessing, threading. 
Other useful modules more relevant to Data Science and Scientific Python in general are numpy, scipy, scikit-learn and pandas.
A complete lists of standard modules for Python 2 and Python 3 are available at http://docs.python.org/2/library/ and http://docs.python.org/3/library/, respectively.
Variables and types
Symbol names
Variable names in Python can contain alphanumerical characters a-z, A-Z, 0-9 and some special characters such as _. Normal variable names must start with a letter. 
By convention, variable names start with a lower-case letter, and Class names start with a capital letter. 
In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:
and, as, assert, break, class, continue, def, del, elif, else, except, 
exec, finally, for, from, global, if, import, in, is, lambda, not, or,
pass, print, raise, return, try, while, with, yield

Note: Be aware of the keyword lambda, which could easily be a natural variable name in a scientific program. But being a keyword, it cannot be used as a variable name.
Assignment
The assignment operator in Python is =. Python is a dynamically typed language, so we do not need to specify the type of a variable when we create one.
Assigning a value to a new variable creates the variable:
End of explanation
"""


type(x)

"""
Explanation: Although not explicitly specified, a variable does have a type associated with it. The type is derived from the value that was assigned to it.
End of explanation
"""


x = 1

type(x)

"""
Explanation: If we assign a new value to a variable, its type can change.
End of explanation
"""


print(y)

"""
Explanation: If we try to use a variable that has not yet been defined we get an NameError:
End of explanation
"""


# integers
x = 1
type(x)

# float
x = 1.0
type(x)

# boolean
b1 = True
b2 = False

type(b1)

# complex numbers: note the use of `j` to specify the imaginary part
x = 1.0 - 1.0j
type(x)

print(x)

print(x.real, x.imag)

"""
Explanation: Fundamental types
End of explanation
"""


import types

# print all types defined in the `types` module
print(dir(types))

x = 1.0

# check if the variable x is a float
type(x) is float

# check if the variable x is an int
type(x) is int

"""
Explanation: Type utility functions
The module types contains a number of type name definitions that can be used to test if variables are of certain types:
End of explanation
"""


isinstance(x, float)

"""
Explanation: We can also use the isinstance method for testing types of variables:
End of explanation
"""


x = 1.5

print(x, type(x))

x = int(x)

print(x, type(x))

z = complex(x)

print(z, type(z))

x = float(z)

"""
Explanation: Type casting
End of explanation
"""


y = bool(z.real)

print(z.real, " -> ", y, type(y))

y = bool(z.imag)

print(z.imag, " -> ", y, type(y))

"""
Explanation: Complex variables cannot be cast to floats or integers. We need to use z.real or z.imag to extract the part of the complex number we want:
End of explanation
"""


1 + 2, 1 - 2, 1 * 2, 1 / 2

1.0 + 2.0, 1.0 - 2.0, 1.0 * 2.0, 1.0 / 2.0

# Integer division of float numbers
3.0 // 2.0

# Note! The power operators in python isn't ^, but **
2 ** 2

"""
Explanation: Althouth, we are not going to use complex numbers in this course, it is good to know the possibilities. 
Operators and comparisons
Most operators and comparisons in Python work as one would expect:

Arithmetic operators +, -, *, /, // (integer division), '**' power
End of explanation
"""


True and False

not False

True or False

"""
Explanation: Note: The / operator always performs a floating point division in Python 3.x.
This is not true in Python 2.x, where the result of / is always an integer if the operands are integers.
to be more specific, 1/2 = 0.5 (float) in Python 3.x, and 1/2 = 0 (int) in Python 2.x (but 1.0/2 = 0.5 in Python 2.x).

The boolean operators are spelled out as the words and, not, or.
End of explanation
"""


2 > 1, 2 < 1

2 > 2, 2 < 2

2 >= 2, 2 <= 2

# equality
[1,2] == [1,2]

# objects identical?
l1 = l2 = [1,2]

l1 is l2

"""
Explanation: Comparison operators &gt;, &lt;, &gt;= (greater or equal), &lt;= (less or equal), == equality, is identical.
End of explanation
"""


s = "Hello world"
type(s)

# length of the string: the number of characters
len(s)

# replace a substring in a string with something else
s2 = s.replace("world", "test")
print(s2)

"""
Explanation: Compound types: Strings, List and dictionaries
Strings
Strings are the variable type that is used for storing text messages.
End of explanation
"""


s[0]

"""
Explanation: We can index a character in a string using []:
End of explanation
"""


s[0:5]

s[4:5]

"""
Explanation: Heads up MATLAB users: Indexing start at 0!
We can extract a part of a string using the syntax [start:stop], which extracts characters between index start and stop -1 (the character at index stop is not included):
End of explanation
"""


s[:5]

s[6:]

s[:]

"""
Explanation: If we omit either (or both) of start or stop from [start:stop], the default is the beginning and the end of the string, respectively:
End of explanation
"""


s[::1]

s[::2]

"""
Explanation: We can also define the step size using the syntax [start:end:step] (the default value for step is 1, as we saw above):
End of explanation
"""


print("str1", "str2", "str3")  # The print statement concatenates strings with a space

print("str1", 1.0, False, -1j)  # The print statements converts all arguments to strings

print("str1" + "str2" + "str3") # strings added with + are concatenated without space

print("value = %f" % 1.0)       # we can use C-style string formatting


# this formatting creates a string
s2 = "value1 = %.2f. value2 = %d" % (3.1415, 1.5)

print(s2) #1.5 -> 1 (integer) is a type casting operation that takes place in the string formatting process

# alternative, more intuitive way of formatting a string 
s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)

print(s3)

"""
Explanation: This technique is called slicing. Read more about the syntax here: http://docs.python.org/release/2.7.3/library/functions.html?highlight=slice#slice
Python has a very rich set of functions for text processing. See for example http://docs.python.org/2/library/string.html for more information.
String formatting examples
End of explanation
"""


l = [1,2,3,4]

print(type(l))
print(l)
print(len(l))

"""
Explanation: List
Lists are very similar to strings, except that each element can be of any type.
The syntax for creating lists in Python is [...]:
End of explanation
"""


print(l)

print (l[0]) # Indexing starts at 0

print (l[-1]) # Inverse indexing, makes code more difficult to read but it can be useful

print(l[1:3])

print(l[::2])

print (l[-2:-1])

"""
Explanation: We can use the same slicing techniques to manipulate lists as we could use on strings:
End of explanation
"""


l = [1, 'a', 1.0, 1-1j]

print(l)

"""
Explanation: Elements in a list do not all have to be of the same type:
End of explanation
"""


nested_list = [1, [2, [3, [4, [5]]]]]

nested_list

"""
Explanation: Python lists can be inhomogeneous and arbitrarily nested:
End of explanation
"""


start = 10
stop = 30
step = 2

range(start, stop, step)

# in python 3 range generates an interator, which can be converted to a list using 'list(...)'.
# It has no effect in python 2
list(range(start, stop, step))

list(range(-10, 10))

s = "Hello World!"

# convert a string to a list by type casting:
s2 = list(s)

s2

help(list.sort)

# sorting lists
s3 = [3, 2, 4, 1, 6]
s3.sort()
print(s3)
s3.sort(reverse=True)
print(s3)

"""
Explanation: Lists play a very important role in Python. For example they are used in loops and other flow control structures (discussed below). There are a number of convenient functions for generating lists of various types, for example the range function:
End of explanation
"""


# create a new empty list
l = []

# add an elements using `append`
l.append("A")
l.append("d")
l.append("d")

print(l)

"""
Explanation: Adding, inserting, modifying, and removing elements from lists
End of explanation
"""


l[1] = "p"
l[2] = "p"

print(l)

l[1:3] = ["d", "d"]

print(l)

"""
Explanation: We can modify lists by assigning new values to elements in the list. In technical jargon, lists are mutable.
End of explanation
"""


l.insert(0, "i")
l.insert(1, "n")
l.insert(2, "s")
l.insert(3, "e")
l.insert(4, "r")
l.insert(5, "t")

print(l)

"""
Explanation: Insert an element at an specific index using insert
End of explanation
"""


l.remove("A")

print(l)

"""
Explanation: Remove first element with specific value using 'remove'
End of explanation
"""


del l[7]
del l[6]

print(l)

"""
Explanation: Remove an element at a specific location using del:
End of explanation
"""


point = (10, 20)

print(point, type(point))

point = 10, 20

print(point, type(point))

"""
Explanation: See help(list) for more details, or read the online documentation 
Tuples
Tuples are like lists, except that they cannot be modified once created, that is they are immutable. 
In Python, tuples are created using the syntax (..., ..., ...), or even ..., ...:
End of explanation
"""


x, y = point

print("x =", x)
print("y =", y)

"""
Explanation: We can unpack a tuple by assigning it to a comma-separated list of variables:
End of explanation
"""


point[0] = 20

"""
Explanation: If we try to assign a new value to an element in a tuple we get an error:
End of explanation
"""


params = {"parameter1" : 1.0,
          "parameter2" : 2.0,
          "parameter3" : 3.0,}

print(type(params))
print(params)

print("parameter1 = " + str(params["parameter1"]))
print("parameter2 = " + str(params["parameter2"]))
print("parameter3 = " + str(params["parameter3"]))

params["parameter1"] = "A"
params["parameter2"] = "B"

# add a new entry
params["parameter4"] = "D"

print("parameter1 = " + str(params["parameter1"]))
print("parameter2 = " + str(params["parameter2"]))
print("parameter3 = " + str(params["parameter3"]))
print("parameter4 = " + str(params["parameter4"]))

"""
Explanation: Dictionaries
Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is {key1 : value1, ...}:
End of explanation
"""


statement1 = False
statement2 = False

if statement1:
    print("statement1 is True")
    
elif statement2:
    print("statement2 is True")
    
else:
    print("statement1 and statement2 are False")

"""
Explanation: Control Flow
Conditional statements: if, elif, else
The Python syntax for conditional execution of code uses the keywords if, elif (else if), else:
End of explanation
"""


statement1 = statement2 = True

if statement1:
    if statement2:
        print("both statement1 and statement2 are True")

# Bad indentation!
if statement1:
    if statement2:
        print("both statement1 and statement2 are True")  # this line is not properly indented

statement1 = False 

if statement1:
    print("printed if statement1 is True")
    
    print("still inside the if block")

if statement1:
    print("printed if statement1 is True")
    
print("now outside the if block")

"""
Explanation: For the first time, here we encounted a peculiar and unusual aspect of the Python programming language: Program blocks are defined by their indentation level. 
Compare to the equivalent C code:
if (statement1)
{
    printf("statement1 is True\n");
}
else if (statement2)
{
    printf("statement2 is True\n");
}
else
{
    printf("statement1 and statement2 are False\n");
}

In C blocks are defined by the enclosing curly brakets { and }. And the level of indentation (white space before the code statements) does not matter (completely optional). 
But in Python, the extent of a code block is defined by the indentation level (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors. 
Examples:
End of explanation
"""


for x in [1,2,3]:
    print(x)

"""
Explanation: Loops
In Python, loops can be programmed in a number of different ways. The most common is the for loop, which is used together with iterable objects, such as lists. The basic syntax is:
for loops:
End of explanation
"""


for x in range(4): # by default range start at 0
    print(x)

"""
Explanation: The for loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of list can be used in the for loop. For example:
End of explanation
"""


for x in range(-3,3):
    print(x)

for word in ["scientific", "computing", "with", "python"]:
    print(word)

"""
Explanation: Note: range(4) does not include 4 !
End of explanation
"""


print params
for key, value in params.items():
    print(key + " = " + str(value))

"""
Explanation: To iterate over key-value pairs of a dictionary:
End of explanation
"""


for idx, x in enumerate(range(-3,3)):
    print(idx, x)

"""
Explanation: Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the enumerate function for this:
End of explanation
"""


l1 = [x**2 for x in range(0,5)]

print(l1)

"""
Explanation: List comprehensions: Creating lists using for loops:
A convenient and compact way to initialize lists:
End of explanation
"""


i = 0

while i < 5:
    print(i)
    
    i = i + 1
    
print("done")

"""
Explanation: while loops:
End of explanation
"""


def func0():   
    print("test")

func0()

"""
Explanation: Note that the print("done") statement is not part of the while loop body because of the difference in indentation.
Functions
A function in Python is defined using the keyword def, followed by a function name, a signature within parentheses (), and a colon :. The following code, with one additional level of indentation, is the function body.
End of explanation
"""


def func1(s):
    """
    Print a string 's' and tell how many characters it has    
    """
    
    print(s + " has " + str(len(s)) + " characters")

help(func1)

func1("test")

"""
Explanation: Optionally, but highly recommended, we can define a so called "docstring", which is a description of the functions purpose and behaivor. The docstring should follow directly after the function definition, before the code in the function body.
End of explanation
"""


def square(x):
    """
    Return the square of x.
    """
    return x ** 2

square(4)

"""
Explanation: Functions that returns a value use the return keyword:
End of explanation
"""


def powers(x):
    """
    Return a few powers of x.
    """
    return x ** 2, x ** 3, x ** 4

powers(3)

x2, x3, x4 = powers(3)

print(x3)

"""
Explanation: We can return multiple values from a function using tuples (see above):
End of explanation
"""


def myfunc(x, p=2, debug=False):
    if debug:
        print("evaluating myfunc for x = " + str(x) + " using exponent p = " + str(p))
    return x**p

"""
Explanation: Default argument and keyword arguments
In a definition of a function, we can give default values to the arguments the function takes:
End of explanation
"""


myfunc(5)

myfunc(5, debug=True)

"""
Explanation: If we don't provide a value of the debug argument when calling the the function myfunc it defaults to the value provided in the function definition:
End of explanation
"""


myfunc(p=3, debug=True, x=7)

"""
Explanation: If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition. This is called keyword arguments, and is often very useful in functions that takes a lot of optional arguments.
End of explanation
"""


f1 = lambda x: x**2
    
# is equivalent to 

def f2(x):
    return x**2

f1(2), f2(2)

"""
Explanation: Unnamed functions (lambda function)
In Python we can also create unnamed functions, using the lambda keyword:
End of explanation
"""


# map is a built-in python function
map(lambda x: x**2, range(-3,4))

# in python 3 we can use `list(...)` to convert the iterator to an explicit list
list(map(lambda x: x**2, range(-3,4)))

"""
Explanation: This technique is useful for example when we want to pass a simple function as an argument to another function, like this:
End of explanation
"""


class Point:
    """
    Simple class for representing a point in a Cartesian coordinate system.
    """
    
    def __init__(self, x, y):
        """
        Create a new Point at x, y.
        """
        self.x = x
        self.y = y
        
    def translate(self, dx, dy):
        """
        Translate the point by dx and dy in the x and y direction.
        """
        self.x += dx
        self.y += dy
        
    def __str__(self):
        return("Point at [%f, %f]" % (self.x, self.y))

"""
Explanation: Classes
Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object. 
In Python a class can contain attributes (variables) and methods (functions).
A class is defined almost like a function, but using the class keyword, and the class definition usually contains a number of class method definitions (a function in a class).


Each class method should have an argument self as its first argument. This object is a self-reference.


Some class method names have special meaning, for example:

__init__: The name of the method that is invoked when the object is first created.
__str__ : A method that is invoked when a simple string representation of the class is needed, as for example when printed.
There are many more, see http://docs.python.org/2/reference/datamodel.html#special-method-names
End of explanation
"""


p1 = Point(0, 0) # this will invoke the __init__ method in the Point class

print(p1)         # this will invoke the __str__ method

"""
Explanation: To create a new instance of a class:
End of explanation
"""


p2 = Point(1, 1)

p1.translate(0.25, 1.5)

print(p1)
print(p2)

"""
Explanation: To invoke a class method in the class instance p:
End of explanation
"""


%%file mymodule.py
"""
Example of a python module. Contains a variable called my_variable,
a function called my_function, and a class called MyClass.
"""

my_variable = 0

def my_function():
    """
    Example function
    """
    return my_variable
    
class MyClass:
    """
    Example class.
    """

    def __init__(self):
        self.variable = my_variable
        
    def set_variable(self, new_value):
        """
        Set self.variable to a new value
        """
        self.variable = new_value
        
    def get_variable(self):
        return self.variable

"""
Explanation: Note that calling class methods can modifiy the state of that particular class instance, but does not effect other class instances or any global variables.
That is one of the nice things about object-oriented design: code such as functions and related variables are grouped in separate and independent entities. 
Modules
One of the most important concepts in good programming is to reuse code and avoid repetitions.
The idea is to write functions and classes with a well-defined purpose and scope, and reuse these instead of repeating similar code in different part of a program (modular programming). The result is usually that readability and maintainability of a program is greatly improved. What this means in practice is that our programs have fewer bugs, are easier to extend and debug/troubleshoot. 
Python supports modular programming at different levels. Functions and classes are examples of tools for low-level modular programming. Python modules are a higher-level modular programming construct, where we can collect related variables, functions and classes in a module. A python module is defined in a python file (with file-ending .py), and it can be made accessible to other Python modules and programs using the import statement. 
Consider the following example: the file mymodule.py contains simple example implementations of a variable, function and a class:
End of explanation
"""


import mymodule

"""
Explanation: We can import the module mymodule into our Python program using import:
End of explanation
"""


help(mymodule)

mymodule.my_variable

mymodule.my_function() 

my_class = mymodule.MyClass() 
my_class.set_variable(10)
my_class.get_variable()

"""
Explanation: Use help(module) to get a summary of what the module provides:
End of explanation
"""


reload(mymodule)  # works only in python 2

"""
Explanation: If we make changes to the code in mymodule.py, we need to reload it using reload:
End of explanation
"""


raise Exception("description of the error")

"""
Explanation: Exceptions
In Python errors are managed with a special language construct called "Exceptions". When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statement is defined.
To generate an exception we can use the raise statement, which takes an argument that must be an instance of the class BaseException or a class derived from it.
End of explanation
"""


try:
    print("test")
    # generate an error: the variable test is not defined
    print(test)
except:
    print("Caught an exception")

"""
Explanation: A typical use of exceptions is to abort functions when some error condition occurs, for example:
def my_function(arguments):

    if not verify(arguments):
        raise Exception("Invalid arguments")

    # rest of the code goes here

To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the try and  except statements:
try:
    # normal code goes here
except:
    # code for error handling goes here
    # this code is not executed unless the code
    # above generated an error

For example:
End of explanation
"""


try:
    print("test")
    # generate an error: the variable test is not defined
    print(test)
except Exception as e:
    print("Caught an exception:" + str(e))

"""
Explanation: To get information about the error, we can access the Exception class instance that describes the exception by using for example:
except Exception as e:
End of explanation
"""


