• A function is a block of code which is executed only when it is called.
• To define a Python function, the def keyword is used.

• The Python’s constructor _init_() is the first method of a class.
• We can try to instantiate an object _init_() is automatically invoked by python to initialize members of an object.

• The Python supports 7 sequences, types. They are mentioned as follows;
  • str,
  • list,
  • tuple,
  • unicode,
  • byte array,
  • xrange, and
  • buffer.

• DBC stands for Open Database Connectivity.
• It is the API standard that allows the connections with the database supporting the interface as a PostreSQL database or Microsoft Access.
• There are 3 ODBC modules for Python 1) PythonWin ODBC module 2) MxODBC 3) pyodbc

• The command used for existing the help window is the quit command.
• The python shell prompt will appear by closing the help window automatically.

• Memory management in Python involves a private heap containing all Python objects and data structures. Interpreter takes care of Python heap and that the programmer has no access to it.
• The allocation of heap space for Python objects is done by Python memory manager. The core API of Python provides some tools for the programmer to code reliable and more robust program.
• Python also has a build-in garbage collector which recycles all the unused memory. When an object is no longer referenced by the program, the heap space it occupies can be freed. The garbage collector determines objects which are no longer referenced by the program frees the occupied memory and make it available to the heap space.

• Python program runs directly from the source code.
• Each type Python programs are executed code is required.
• Python converts source code written by the programmer into intermediate language which is again translated it into the native language / machine language that is executed.
• So Python is an Interpreted language.

• Guido Van Rossum has founded the Python and
• It was released in December 1989

15 ) What is generator in Python ?

• In Python, generator is a way that specifies how to implement iterators.
• It is a normal function except that it yields expression in the function.

• Slicing is a mechanism used to select a range of items from sequence type like list, tuple, string etc

• Free and open source
• Portable
• Extensible
• Object oriented
• Built-in data structure

• In Python, iterators are used to iterate a group of elements, containers like list.

• Python is object-oriented. It is object- oriented programming language.
• The OOP is a programming paradigm based on classes and instances of classes.
• The features of OOP are given below Encapsulation Data Abstraction Inheritance Polymorphism.

• Pass means, no-operation Python statement, or in other words it is a place holder in compound statement, where there should be a blank left and nothing has to be written there.

• Python is an interpreted language. That means that, unlike languages like C and its variants, Python does not need to be compiled before it is run. Other interpreted languages include PHP and Ruby.
• Python is dynamically typed, this means that you don’t need to state the types of variables when you declare them or anything like that. You can do things like x=111 and then x=”I’m a string” without error
• Python is well suited to object orientated programming in that it allows the definition of classes along with composition and inheritance. Python does not have access specifiers (like C++’s public, private).
• In Python, functions are first-class objects. This means that they can be assigned to variables, returned from other functions and passed into functions. Classes are also first class objects
• Writing Python code is quick but running it is often slower than compiled languages. Fortunately，Python allows the inclusion of C-based extensions so bottlenecks can be optimized away and often are. The numpy package is a good example of this, it’s really quite quick because a lot of the number-crunching it does isn’t actually done by Python
• Python finds use in many spheres – web applications, automation, scientific modeling, big data applications and many more. It’s also often used as “glue” code to get other languages and components to play nice. Learn more about Big Data and its applications from the Azure Data Engineering Training Course.

• Python is capable of scripting, but in general sense, it is considered as a general-purpose programming language. To know more about Scripting, you can refer to the Python Scripting Tutorial.

• An interpreted language is any programming language which is not in machine-level code before runtime. Therefore, Python is an interpreted language.

• A namespace in python refers to the name which is assigned to each object in python. The objects are variables and functions. As each object is created, its name along with space(the address of the outer function in which the object is), gets created. The namespaces are maintained in python like a dictionary where the key is th What is Pythone namespace and value is the address of the object. There 4 types of namespace in python-

1. Built-in namespace– These namespaces contain all the built-in objects in python and are available whenever python is running.
2. Global namespace– These are namespaces for all the objects created at the level of the main program.
3. Enclosing namespaces– These namespaces are at the higher level or outer function.
4. Local namespaces– These namespaces are at the local or inner function.

• Decorators are used to add some design patterns to a function without changing its structure. Decorators generally are defined before the function they are enhancing. To apply a decorator we first define the decorator function. Then we write the function it is applied to and simply add the decorator function above the function it has to be applied to. For this, we use the @ symbol before the decorator.

The common built-in data types in python are-

• Numbers– They include integers, floating-point numbers, and complex numbers. eg. 1, 7.9,3+4i
• List– An ordered sequence of items is called a list. The elements of a list may belong to different data types. Eg. [5,’market’,2.4]
• Tuple– It is also an ordered sequence of elements. Unlike lists , tuples are immutable, which means they can’t be changed. Eg. (3,’tool’,1)
• String– A sequence of characters is called a string. They are declared within single or double-quotes. Eg. “Sana”, ‘She is going to the market’, etc.
• Set– Sets are a collection of unique items that are not in order. Eg. {7,6,8}
• Dictionary– A dictionary stores values in key and value pairs where each value can be accessed through its key. The order of items is not important. Eg. {1:’apple’,2:’mango}
• Boolean– There are 2 boolean values- True and False.

• The .py files are the python source code files. While the .pyc files contain the bytecode of the python files. .pyc files are created when the code is imported from some other source. The interpreter converts the source .py files to .pyc files which helps by saving time. You can get a better understanding with the Data Engineering Course in Washington.

Keywords in python are reserved words that have special meaning.They are generally used to define type of variables. Keywords cannot be used for variable or function names. There are following 33 keywords in python-

• And
• Or
• Not
• If
• Elif
• Else
• For
• While
• Break
•  As
• Def
• Lambda
• Pass
• Return
• True
• False
• Try
• With
• Assert
• Class
• Continue
• Del
• Except
• Finally
• From
• Global
• Import
• In
• Is
• None
• Nonlocal
• Raise
• Yield

A literal in python source code represents a fixed value for primitive data types. There are 5 types of literals in python-

1. String literals– A string literal is created by assigning some text enclosed in single or double quotes to a variable. To create multiline literals, assign the multiline text enclosed in triple quotes. Eg.name=”Tanya”
2. A character literal– It is created by assigning a single character enclosed in double quotes. Eg. a=’t’
3. Numeric literals include numeric values that can be either integer, floating point value, or a complex number. Eg. a=50
4. Boolean literals– These can be 2 values- either True or False.
5. Special literal– Python has 1 special literal None which is used to return a null variable.
6. Literal Collections– These are of 4 types-

• a) List collections-Eg. a=[1,2,3,’Amit’]
• b) Tuple literals- Eg. a=(5,6,7,8)
• c) Dictionary literals- Eg. dict={1: ’apple’, 2: ’mango, 3: ’banana`’}
• d) Set literals- Eg. {“Tanya”, “Rohit”, “Mohan”}

The dataframes in python can be combined in the following ways-

1. Concatenating them by stacking the 2 dataframes vertically.
2. Concatenating them by stacking the 2 dataframes horizontally.
3. Combining them on a common column. This is referred to as joining.

• The concat() function is used to concatenate two dataframes. Its syntax is- pd.concat([dataframe1, dataframe2]).
• Dataframes are joined together on a common column called a key. When we combine all the rows in dataframe it is union and the join used is outer join. While, when we combine the common rows or intersection, the join used is the inner join. Its syntax is- pd.concat([dataframe1, dataframe2], axis=’axis’, join=’type_of_join)

The new features in Python 3.9.0.0 version are-

•  New Dictionary functions Merge(|) and Update(|=)
• New String Methods to Remove Prefixes and Suffixes
• Type Hinting Generics in Standard Collections
• New Parser based on PEG rather than LL1
• New modules like zoneinfo and graphlib
• Improved Modules like ast, asyncio, etc.
• Optimizations such as optimized idiom for assignment, signal handling, optimized python built ins, etc.
• Deprecated functions and commands such as deprecated parser and symbol modules, deprecated functions, etc.
• Removal of erroneous methods, functions, etc.

• A namespace is a naming system used to make sure that names are unique to avoid naming conflicts.

33 ) What is PYTHONPATH ?

def add():

b=3

c=a+b

print(c)

add()

5

import array as arr

My_Array=arr.array('i',[1,2,3,4])

My_list=[1,'abc',1.20]

print(My_Array)

print(My_list)

array(‘i’, [1, 2, 3, 4]) [1, ‘abc’, 1.2]

class Employee:

def __init__(self, name, age,salary):

self.name = name

self.age = age

self.salary = 20000

E1 = Employee("XYZ", 23, 20000)

# E1 is the instance of class Employee.

#__init__ allocates memory for E1.

print(E1.name)

print(E1.age)

print(E1.salary)

XYZ

23

20000

a = lambda x,y : x+y

print(a(5, 6))

11

import array as arr

My_Array=arr.array('i',[1,2,3,4,5])

My_Array[::-1]

array(‘i’, [5, 4, 3, 2, 1])

from random import shuffle

x = ['Keep', 'The', 'Blue', 'Flag', 'Flying', 'High']

shuffle(x)

print(x)

['Flying', 'Keep', 'Blue', 'High', 'The', 'Flag']

import random

random.random

<span data-mce-type="bookmark" style="display: inline-block; width: 0px; overflow: hidden; line-height: 0;" class="mce_SELRES_end"></span>

<pre><span>#Comments in Python start like this

print("Comments in Python start with a #")

Comments in Python start with a #

stg='ABCD'

print(stg.lower())

abcd

"""

Using docstring as a comment.

This code divides 2 numbers

"""

x=8

y=4

z=x/y

print(z)

2.0

dict={'Country':'India','Capital':'Delhi','PM':'Modi'}

print dict[Country]

India

print dict[Capital]

Delhi

print dict[PM]

Modi

stg='ABCD'

len(stg)

4

import os

os.remove("xyz.txt")

a=arr.array('d', [1.1 , 2.1 ,3.1] )

a.append(3.4)

print(a)

a.extend([4.5,6.3,6.8])

print(a)

a.insert(2,3.8)

print(a)

array(‘d’, [1.1, 2.1, 3.1, 3.4])

array(‘d’, [1.1, 2.1, 3.1, 3.4, 4.5, 6.3, 6.8])

array(‘d’, [1.1, 2.1, 3.8, 3.1, 3.4, 4.5, 6.3, 6.8])

a=arr.array('d', [1.1, 2.2, 3.8, 3.1, 3.7, 1.2, 4.6])

print(a.pop())

print(a.pop(3))

a.remove(1.1)

print(a)

3.1

array(‘d’, [2.2, 3.8, 3.7, 1.2])

a="Kaashiv python"

print(a.split())

[‘Kaashiv’, ‘python’]

class Employee:

def __init__(self, name):

self.name = name

E1=Employee("abc")

print(E1.name)

abc

# m.py

class MyClass:

def f(self):

print "f()"

import m

def monkey_f(self):

print "monkey_f()"

 

m.MyClass.f = monkey_f

obj = m.MyClass()

obj.f()

monkey_f()

class a:

pass

obj=a()

obj.name="xyz"

print("Name = ",obj.name)

Name =  xyz

def bs(a):

# a = name of list

b=len(a)-1nbsp;

# minus 1 because we always compare 2 adjacent values

for x in range(b):

for y in range(b-x):

a[y]=a[y+1]
 
a=[32,5,3,6,7,54,87]

bs(a)

[3, 5, 6, 7, 32, 54, 87]

def pyfunc(r):

for x in range(r):

print(' '*(r-x-1)+'*'*(2*x+1))

pyfunc(9)

*

***

*****

*******

*********

***********

*************

***************

*****************

# Enter number of terms needednbsp;#0,1,1,2,3,5....

a=int(input("Enter the terms"))

f=0;#first element of series

s=1#second element of series

if a=0:

print("The requested series is",f)

else:

print(f,s,end=" ")

for x in range(2,a):

print(next,end=" ")

f=s

s=next

Enter the terms 5 0 1 1 2 3

a=int(input("enter number"))

if a=1:

for x in range(2,a):

if(a%x)==0:

print("not prime")

break

else:

print("Prime")

else:

print("not prime")

enter number 3
Prime

a=input("enter sequence")

b=a[::-1]

if a==b:

print("palindrome")

else:

print("Not a Palindrome")

enter sequence 323 palindrome

with open(SOME_LARGE_FILE) as fh:

count = 0

text = fh.read()

for character in text:

if character.isupper():

count += 1

count sum(1 for line in fh for character in line if character.isupper())

list = ["1", "4", "0", "6", "9"]

list = [int(i) for i in list]

list.sort()

print (list)

A0 = dict(zip(('a','b','c','d','e'),(1,2,3,4,5)))

A1 = range(10)A2 = sorted([i for i in A1 if i in A0])

A3 = sorted([A0[s] for s in A0])

A4 = [i for i in A1 if i in A3]

A5 = {i:i*i for i in A1}

A6 = [[i,i*i] for i in A1]

print(A0,A1,A2,A3,A4,A5,A6)

A0 = {'a': 1, 'c': 3, 'b': 2, 'e': 5, 'd': 4} # the order may vary

A1 = range(0, 10)

A2 = []

A3 = [1, 2, 3, 4, 5]

A4 = [1, 2, 3, 4, 5]

A5 = {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81}

A6 = [[0, 0], [1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36], [7, 49], [8, 64], [9, 81]]

DATABASES = {

'default': {

'ENGINE' : 'django.db.backends.sqlite3',

'NAME' : os.path.join(BASE_DIR, 'db.sqlite3'),

}

}

from django.http import HttpResponse

import datetime
def Current_datetime(request):

now = datetime.datetime.now()

html = "It is now %s/body/html % now

return HttpResponse(html)

import urllib.request

urllib.request.urlretrieve("URL", "local-filename.jpg")

from bs4 import BeautifulSoup

import requests

import sys

url = '<a href="http://www.imdb.com/chart/top">http://www.imdb.com/chart/top</a>'

response = requests.get(url)

soup = BeautifulSoup(response.text)

tr = soup.findChildren("tr")

tr = iter(tr)

next(tr)

for movie in tr:

title = movie.find('td', {'class': 'titleColumn'} ).find('a').contents[0]

year = movie.find('td', {'class': 'titleColumn'} ).find('span', {'class': 'secondaryInfo'}).contents[0]

rating = movie.find('td', {'class': 'ratingColumn imdbRating'} ).find('strong').contents[0]

row = title + ' - ' + year + ' ' + ' ' + rating

print(row)

import numpy as np

arr = np.array([1, 3, 2, 4, 5])

print(arr.argsort()[-3:][::-1])

[ 4 3 1 ]

import numpy as np

a = np.array([1,2,3,4,5])

p = np.percentile(a, 50) #Returns 50th percentile, e.g. median

print(p)

3