Python For Data Science - Real Time Experience

In this course, you will embark on a journey into the world of the Python Programming Language through hands-on coding exercises in Jupyter Notebook, presented in an exceptionally accessible manner.

To begin, you will be guided through the installation and initial usage of the Jupyter Notebook environment, setting the stage for an immersive learning experience.

Subsequently, we will delve into the various essential topics of Python programming.

Lets have a look at some theoretical part (not covered in video lectures).

Introduction -

Python is a high-level programming language that uses instructions to teach the computer how to perform a task. Python is an easy to learn, powerful programming language.

A language which is closer to the human language (like English) is known as a high-level language.

Python provides an easy approach to object-oriented programming.

Object-oriented is approach used to write programs.

Python is a free and open source language i.e., we can read, modify and distribute the source code of Python scripts.

It was developed by Guido van Rossum and was released in 1991.

Python finds its application in various domains. Python is used to create web applications, used in game development, to create desktop applications, is used in Machine Learning and Data Science.

How Python Works ? -

We write instructions in Python language.

Python is an interpreted language, so there is no need to compiling it.

Python programs runs (executed) directly through source code. The source code is converted into Intermediate Bytecode and then Bytecode is converted into the native language of computer (i.e., machine language) internally by Python Interpreter. The code is executed and the output is presented.

Python Source Code > Intermediate Bytecode > Machine Language > Code Executed

What is a Program ? -

A Program is a set of instructions that tells the computer to perform a specific task. A programming language is the language used to create programs.

Eg. When we click on Play button on media player, then there is a program working behind the scene which tells the computer to turn on the music.

A built-in function is a function which is predefined and can be used directly. Eg. print()

Comments are the pieces of code which are ignored by the python interpreter. Comments are used to make source code easier to understand by other people. Python supports single line comments mean they can cover only one line.

The various topics explained in this course video lectures with examples are as follows -

1.2 , c = ‘Ram’, d = lambda (‘any function’)

# Variables are used to store values. The stored values in the variables can be used later in the programs. We can retrieve them by referring to the variable names.

2. String – String is a series of characters, surrounded by single or double quotes. Eg. “Hello”, ‘Hello999’, ‘999’.

4. LIST

[ int /float / str ] à A = [4 , 3.4, ‘a’ , ‘bcd’ ]

à Collection of data-types, Mutable : Values can be changed , Ordered : Values order will be as it is , Changeable , Allows duplicate values.

5. TUPLE

( int / float / str ) à B = (1 , 2 , 3.4 , 3.4 , ‘a’ , ‘bcd’ )

àImmutable : Values can’t be changed , Ordered : Values order will be as it is , Unchangeable, Heterogeneous Data, Allows duplicate values.

6. SET

{ int / float / str } à C = {4 , 5.6 , ‘a’ , ‘bcd’ }

àValues can’t be changed but new values can be added , Unordered : Values order may change , Arrange the items in ascending order, Doesn’t allow duplicate values, Un-indexed.

7.4 , K4 : 5.6 , K5 : ‘ab’ , K6 : ‘bcd’ }

à Mutable , Unordered , Doesn’t allows duplicate keys , Indexed, Keys must be unique & immutable.

8. “\n” – For next new line

print("My Name is", "\n" , "My city is ", "\n" ,"My country is")

print(‘Delhi’) , print(‘’) , print(‘Noida’) # To create a gap of one line between two strings.

10.append(55) - To add a new value at the end of the list.

A.clear( ) – To clear/delete/blank a list.

B = A.copy( ) – To create a copy of the list.

A.count(5) – To count how many times a value occurs.

A.extend(c) – To add a new list in the existing list.

A.index(7) – To show the index of a value. # A.index(value, start_index, stop_index)

A.insert(3,66) – To insert a new value at a given position.

A.pop(3) – To delete a value with the help of index. # A.pop( )

A.remove( 55) – To delete a value from the list.

A.reverse( ) – To reverse the list.

A.sort( ) – To sort the list. # A.sort(reverse=True)

del A[ 1 : 4 ] – To delete some items from the list.

type(A) – To see the type.

List Concatenation - A = [1,2,3,4] , B = [5,6,7,8] ; C = A+B = [count(5) – To count how many times a value occurs.

T.index(7) – To show the index of a value.

12.add(5) – To add a new value 5 in the set.

S.clear() – To clear all the elements of the set.

S.copy() – To copy a set.

S1.difference(S2) – S1-S2 - It shows the elements of set S1 only.

S1.difference_update(S2) – It removes all common elements from the set1.

S.discard(x) – It will remove an element(x) from the set. If x is not in set, it will not show error.

S.remove(x) – It will remove an element(x) from the set. If x is not in set, it will show an error.

S.pop() – It deletes the first/random element of the set.

S1.Union(S2) – Set1 | Set2 – It shows all elements of set1 and set 2.

S1.Intersection(S2) – Set1 & Set2 – It shows common elements of set1 and set2.

S1.Intersection_update(S2) – Now set S1 will contain only common elements.

S1.isdisjoint(S2) – It returns True, if S1 & S2 don’t have any common values, otherwise False.

S1.issubset(S2) – It returns True, if all elements of S1 are in set S2.

S2.issuperset(S1) – It returns True, if all elements of S1 are in set S2, otherwise False.

len(S) – It shows the no. of unique elements in the set.

S1.symmetric_difference(S2) – S1^S2 – To show the non-common elements from S1 and S2.

S1.symmetric_difference_update(S2) - Now set S1 will contain only non-common elements.

S1.update([4,5,6]) – To add multiple items, in list/tuple/set form.

13.clear( ) – To delete the dictionary.

E = D.copy( ) – To copy a dictionary.

D.get(‘K1’) – To get the value against a key in the dictionary. If the key is not in dictionary, it will show None, without showing any error.

D.items( ) – To show all the items of a dictionary.

D.keys( ) – To show all the keys of a dictionary.

D.values( ) – To show all the values of a dictionary.

D.pop(‘K1’) – To delete the key alongwith its index.

D.popitem( ) – To delete the last key with value.

D.setdefault(‘K3’) , D.setdefault(‘K4’, value), D[‘K4’] = value - To add a key at the end of the dictionary.

D.update(‘E’) – To add a new dictionary in the existing dictionary.

D.fromkeys(A) – To create a dictionary, using list items as keys. And adding a value to all keys is optional.

“Key” in D – To check the presence of any element(key) in the dictionary.

14.

int (1) =>1 - Converting int into int

int (3.2) => 3 – Converting float into int

int (‘5’) => 5 – Converting a numerical string into int

int (‘a’) => error – Can’t convert an alphabetical string into int

float (3.2) => 3.2 – Converting float into float

float (6) => 6.0 – Converting int into float

float (“10”) => 10.0 – Converting a numerical string into float

float (‘b’) => error – Can’t convert an alphabetical string into float

Str (‘a’) => ‘a’ – Converting a string into string

str (1) => ‘1’ – Converting an int into string

str (3.2) => ‘3.2’ – Converting a float into string

15. RANGE - It creates a sequential list of numbers.

range(start value, stop value, step value) , range(0,50,1) , range(1, 50) , range(50)

16. FUNCTION – A function is a block of code, which is defined to perform some task. We have call a function to run it whenever required.

Parameter : Given at the time of defining function . Ex : def func(a,b)

Arguments : Given at the time of calling the function . Ex : func(2,3)

def fun_name ( args / parameters ) : multiple line statement ,

def fun_name ( var1, var2 ) : multiple line statement

def new ( 2 , 3 ) : c = a + b , return c

If the number of arguments to be passed is not fixed…then we use the Arbitrary Arguments (with *args)

Ex : def func(*values) : for i in values print(i) # It can take any number of arguments.

Keyword Arguments : We can also send the args with key=value syntax.

Ex : def new(b,a,c): print("The winner is " , a)

new(a= ‘Ram’, b= ‘Sham’, c= ‘Shiva’) ….. O/p will be : The winner is Ram

17. LAMBDA FUNCTION à It is a single line function.

fun_name = lambda parameters : single line statement

Ex : sum = lambda a , b : a + b

18.

Ex 1 : a = input ( ‘Enter your name’ ) ,

Ex 2 : print ( ‘Enter your name’ )

x = input ( )

19. INDEXING – list.index( item ) , list [index value] , list [ start : stop : step ]

A.index(25) , A[1] , A [ 1 : 20 : 2 ] , A [ : 4 ] , A[ 2 : ] , A [ : ]

Negative Indexing – A[-1] , A [ 8 : 0 : -1 ] , A [ : : -1 ]

String Indexing – A.index( ‘r’ ) , A[ : 16 ]

Nested List - List in a list

Ex : A = [ [1,2,3] FOR LOOP – for val in sequence : body of for loop,

Ex 1 : for x in [1,2,3,4,5] : print (x) ,

Ex 2 : for i in ‘banana’ : print (i)

1) i = 0

while i < 6 :

print (i)

i = i +1

2) i = 0

while i < 6 :

i = i +1

print (i)

Syntax : string.split ( separator , maxsplit )

23.

Syntax : map( function, iterables ) or map( condition, values )

Ex : list ( map ( lambda x : x+1 , [1,2,3,4,5] ) )

24.

Syntax : filter( function, sequence )

Ex : list ( filter ( lambda x : x%2 . = 0 , [ We can enumerate as list, tuple, set, dictionary.

Syntax : enumerate( list )

Ex : list ( enumerate (‘apple’ , ‘mango’ , ‘orange’) )

26.

Syntax : z = zip(list1, list2, list3)

z = list(z) , print(z)

Example : A = [1,2,3] , B = [‘Ram’ , ‘Sham’ , ‘Shiva’] , C = [‘Delhi’, ‘Noida’, ‘Agra’]

z = zip(A, B, C) , z = list(z) , print(z)

27.