Showing posts with label Python. Show all posts
Showing posts with label Python. Show all posts
Thursday, 27 March 2025
Tuesday, 8 March 2022
Python#07
Class in Python
A Class in Python is a logical grouping of data and functions. It gives the freedom to create data structures that contains arbitrary content and hence easily accessible.
Class” is a logical grouping of functions and data. Python class provides all the standard features of Object Oriented Programming.
Class inheritance mechanism : A derived class that override any method of its base class. A method can call the method of a base class with the same name. Python Classes are defined by keyword “class” itself. Inside classes, you can define functions or methods that are part of the class
Everything in a class is indented, just like the code in the function, loop, if statement, etc.
The self argument in Python refers to the object itself. Self is the name preferred by convention by Pythons to indicate the first parameter of instance methods in Python
Python runtime will pass “self” value automatically when you call an instance method on in instance, whether you provide it deliberately or not
In Python, a class can inherit attributes and behavior methods from another class called subclass
class ametClass():
def aMethod(self):
print("Men Money Machine & Matters")
def bMethod(self, param):
print("Hey I am testing class method :" + param)
def main():
c = ametClass()
c.aMethod()
c.bMethod('I am param porul')
main()
Result:
Men Money Machine & Matters
Hey I am testing class method :I am param porul
####inheritance
class aClass():
def aMethod(self):
print('A ha : Base class method 1')
def bMethod(self, str):
print('oh oh Base class method 2 '+str)
class childClass(aClass):
def cMethod(self):
aClass.aMethod(self) # Note instance of base class method
print('A ha : Child class Method1.....')
def dMethod(self, str):
print('Oh Oh Child class method 2'+str)
def main():
c1 = aClass()
c1.aMethod() # Note child inherited from aClass
c1.bMethod('Base class ..nna') # Note child inherited from aClass
c2 = childClass()
c2.aMethod()
c2.bMethod('Besh besh Child Class Method1')
c2.cMethod()
c2.dMethod('Nanna Irukku Child Class Method 2')
main()
Result
A ha : Base class method 1
oh oh Base class method 2 Base class ..nna
A ha : Base class method 1
oh oh Base class method 2 Besh besh Child Class Method1
A ha : Base class method 1
A ha : Child class Method1.....
Oh Oh Child class method 2Nanna Irukku Child Class Method 2
Using Constructors
# Constructors : Defining and initalizing values
class Cadet:
name = " " # Class Variables
def __init__(self, name):
self.name = name
def printWelcome(self):
print('Welcome to Kalvi Koil, AMET '+ self.name)
Cadet1 = Cadet('Anbu')
Cadet1.printWelcome()
Result :
Welcome to Kalvi Koil, AMET Anbu
The above sample codes explain the how to define a class, define class method, inherit a class, overloading a method, initialize the class using constructor.
Happy Learning by simple super code!
Python#06
Python Yield
yield is not like return. When return is used it is given to caller. When yield is used you can iterate, pass or suspend. Le us see with example. Ready guys! Start.. Here you go..
Here is the situation when you should use Yield instead of Return
Use yield instead of return when the data size is large
Yield is the best choice when you need your execution to be faster on large data sets.
Use yield when you want to return a big set of values to the calling function.
Yield is an efficient way of producing data that is big or infinite.
yield wont take memory.
Execution is faster The yield keyword in python works like a return with the only difference is that instead of returning a value, it gives back a generator function to the caller.
A generator is a special type of iterator that, once used, will not be available again.
The values are not stored in memory and are only available when called.
The values from the generator can be read using for-in, list() and next() method.
The main difference between yield and return is that yield returns back a generator function to the caller and return gives a single value to the caller.
Yield does not store any of the values in memory, and the advantage is that it is helpful when the data size is big, as none of the values are stored in memory.
The performance is better if the yield keyword is used in comparison to return for large data size.
# Yield is just like return but with generator(?) objects instead of values
# Generators are special functions that have to be iterated to get the values
# To get the values the
# To get the values the objects has to be iterated
#Syntax yield expression
# #Sample yield function
# def demoyield():
# yield "Welcom to AMET - Pioneer in Maritime Education"
#
# out = demoyield()
# print(out)
# #<generator object demoyield at 0x000002802AC5DA50>
# # iterate to get the value in the above object
# #Generators are functions that return an iterable generator object. The values from the generator object are fetched one at a time instead of the full list together and hence to get the actual values you can use a for-loop, using next() or list() method
# for i in out:
#. print(i)
# Example 2
# def gen():
# yield 'G'
#. yield 'A'
# yield 'N'
# yield 'D'
# yield 'H'
# yield 'I'
# yield ' '
# yield 'M'
# yield 'A'
# yield 'H'
# yield 'A'
# yield 'N'
#
# demo = gen()
#
# for i in demo:
#. print(i)
#
#example 3 difference between normal function and generator function
#Normal
def normalfun():
return 'Hello Zoox Robo Taxi to Universe - normal function'
def genfun():
yield "'Hello Zoox Robo Taxi to Universe - generator function"
print(normalfun())
print(genfun())
# Hello Zoox Robo Taxi to Universe - normal function
#until it prints the whole string normalfun()wont stop
# <generator object genfun at 0x00000298E53AD900>
#but gen iterates so we can use the generator obhject to get the values and also , 'pause' and 'resume'
print(next(genfun()))
#Note next function
print(list(genfun()))
Python#05
File Operations in Python
How to open a file, read, print, write, append the contents.? All shown with the following example code and output
# Simple Excel file read
import pandas as pd
csv_file = pd.read_csv('csv.csv')
print(csv_file)
# # printed the following
# h1 h2 h3 h4
# 0 1 2 3 4
# 1 5 6 7 8
# 2 9 10 11 12
# 3 13 14 15 16
# ##################
#Excel file sheet1 reading specific
excel_file = pd.read_excel('sample.xlsx', sheet_name='Sheet1')
print(excel_file)
# Print out
# Unnamed: 0 slno name age sal
# 0 1 1 1 1 1
# 1 2 2 2 2 2
# 2 3 3 3 3 3
# 3 4 4 4 4 4
# read multiple sheets
df_sheet_multi = pd.read_excel('sample.xlsx', sheet_name=['Sheet1', 'Sheet2'])
print(df_sheet_multi)
#Printout
# {'Sheet1': Unnamed: 0 slno name age sal
# 0 1 1 1 1 1
# 1 2 2 2 2 2
# 2 3 3 3 3 3
# 3 4 4 4 4 4, 'Sheet2': Unnamed: 0 slno name age sal
# 0 1 11 11 11 11
# 1 2 22 33 44 55
# 2 3 66 66 66 66
# 3 4 77 77 77 77}
print(25*'-','Sheet1')
print(df_sheet_multi['Sheet1'])
# ------------------------- Sheet1
# Unnamed: 0 slno name age sal
# 0 1 1 1 1 1
# 1 2 2 2 2 2
# 2 3 3 3 3 3
# 3 4 4 4 4 4
print(25*'-','Sheet2')
print(df_sheet_multi['Sheet2'])
# ------------------------- Sheet2
# Unnamed: 0 slno name age sal
# 0 1 11 11 11 11
# 1 2 22 33 44 55
# 2 3 66 66 66 66
# 3 4 77 77 77 77
# To Create and write contents
f = open("demo.txt", "w")
f.write("I have written the contents!")
f.close()
#open and read the file after the appending:
f = open("demo.txt", "r")
print(f.read())
#prints => I have written the contents!
f.close()
f = open("demo.txt", "a")
f.write("Now I have written the contents more again !")
f.close()
#In demo.txt => I have written the contents!Now I have written the contents more again !
import os.path
file_exists = os.path.exists('readme.txt')
print(file_exists)
#False
import os.path
file_exists = os.path.exists('demo.txt')
print(file_exists)
#True
f = open("demo1.txt", "w")
f.write("I have written the contents!")
f.close()
f = open("demo2.txt", "w")
f.write(str(list(csv_file)))
f = open("demo2.txt","r")
print(f.read())
#f.close()
print(f.read())
#Print ['h1', 'h2', 'h3', 'h4']
Hope The above simple code snippets and printouts shown as comments is clear and understandable. Happy Open and Distance Learning!
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