Fundas2

Some Basic Functions

# for <loop_variable> in <iterable>:
#     <code>
for i in range(10):
    print(i)
print(25*'-')
# for <loop_variable> in range(<start>, <stop>, <step>):
#     <code>
for i in range(1,10,2):
    print(i)
for i in range(1,30,2):
    print('fun'*i)
for i in range(30,1,-2):
    print('fun'*i)
l = ['apple','boy','cat','dog']
# for loop with iterable list
for i in range(len(l)):
   print(l[i])
str = ' I am iterable by seperation?'
for i in str:
    print(i)


# Break
lis = [1, 2, 3, 4, 5]


for elem in lis:
   if elem % 2 == 0:
      print("Even:", elem)
      print("break")
      break
   else:
      print("Odd:", elem)
"""
Odd: 1
Even: 2
break
"""
# Continue

for elem in lis:
   if elem % 2 == 0:
      print("continue")
      continue
   print("Odd:", elem)
"""
Odd: 1
continue
Odd: 3
continue
Odd: 5
"""

# zip() is an amazing built-in function that we can use in Python
# to iterate over multiple sequences at once,
# getting their corresponding elements in each iteration

list1 = [10, 20, 30, 40]
list2 = [50, 60, 70, 80]
list3 = ['a','b','c','d']

for elem1, elem2,elem3 in zip(list1, list2, list3):
    print(elem1, elem2, elem3)
"""
10 50 a
20 60 b
30 70 c
40 80 d
"""


# while <condition>:
#     <code>

x=5
while x >= 0:
   print("Fun " * x)
   x -= 1

#nested for loop
dim =3
for i in range(dim):
    for j in range(dim):
        for k in range(dim):
            print(i,j,k)
        print('\n')
    print('\n') # 3 x 3 3 = 9 elements x 9

num_cols=5
num_rows=5
for i in range(5):
   for num_cols in range(num_rows-i):
      print("*", end="")
   print()


# Function in pytho

def fun1():
    print('def()')

fun1()

def add(x,y):
    print(x+y)
add(5,5)


def mulp(a, b=5):  # default value for b
    print(a * b)

mulp(10) #50


# Recursibve Factorial function

def fact(n):
    if n == 0 or n == 1:
        return 1
    else:
        return n * fact(n-1)

f = fact(5)
print(f)#120

def fib(n):
    if n == 0 or n == 1:
        return n
    else:
        return fib(n-1) + fib(n-2)
print(5*'-')
f = fib(4)
print(f) #3

Class : Object oriented concepts in python using class

class student:
    name =  'Steve Jobs',
    co = 'Apple',
    country = 'USA',
    sal = 1000000

    def __init__(self,param1, param2, param3, param4):
        self.name = param1
        self.co = param2
        self.country = param3
        self.sal = param4

    def display(self, param1,param2, param3, param4):
        print(param1,param2,param3, param4)


stu = student('Bill Gates','Micro Soft','USA',100000)
print(stu.name, stu.sal) # Bill Gates 100000

print(type(stu)) #<class '__main__.student'>
print(stu) # <__main__.student object at 0x000001ACB0C19FD0>
print(stu.co,stu.country) #Micro Soft USA

# del stu

#---------------------------------------------
class Student:

    def __init__(self, name):
        self._name = name

    @property
    def name(self):
        print("Calling getter")
        return self._name

    @name.setter
    def name(self, new_name):
        print("Calling setter")
        self._name = new_name

    @name.deleter
    def name(self):
        print("Calling deleter")
        del self._name

stu = Student("Sandilya")
print(stu.name)
stu.name = "Chandra"
print(stu.name)

del stu
"""
Calling getter
Sandilya
Calling setter
Calling getter
Chandra Gupta
"""

All about import statement

Many ways we can import a python Libraries we will see the various ways

import pandas
print(pandas.read_csv('bio.csv'))

import pandas as pd  # renamed as pd
df = pd.read_csv('bio.csv')
print(df)

from pandas import read_csv  # import only read_csv
print(read_csv('bio.csv'))

"""
   Unnamed: 0  Name  Age
0           0   Raj   23
1           1   Ram   23
2           2  Sita   24
3           3  Laks   21
"""

from pandas import *  # import all 
print('df:',read_csv('bio.csv'))
"""
df:    Unnamed: 0  Name  Age
0           0   Raj   23
1           1   Ram   23
2           2  Sita   24
3           3  Laks   21
"""

List Comprehension in Python

The syntax used to define list comprehensions usually follows one of these four patterns:

• [<value_to_include> for <var> in <sequence>]
• [<value_to_include> for <var1> in <sequence1> for <var2> in <sequence2>][<value_to_include> for <var> in <sequence> if <condition>]
• [<value> for <var1> in <sequence1> for <var2> in <sequence2> if <condition>]

# [<value_to_include> for <var> in <sequence>]
#print all alphabets
print([chr(i) for i in range(65, 91)])
#['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']

# [<value_to_include> for <var1> in <sequence1> for <var2> in <sequence2>][<value_to_include> for <var> in <sequence> if <condition>]
print([k for k in range(1, 25) if k % 2 == 0])
#[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]
# [<value> for <var1> in <sequence1> for <var2> in <sequence2> if <condition>]
print([i * j for i in range(1, 5) for j in range(1, 5) if i % j == 0])
# [1, 2, 4, 3, 9, 4, 8, 16]

Above sample code snippets will explain how to use list comprehension with for, nested for, and if condition.

# Brain Teaser See the differenc  between the below statements

import sys
print(sys.getsizeof([i for i in range(500)])) # 4216

print(sys.getsizeof((i for i in range(500))))   #112

Here all elements in a list and versus one element in a list at a time. That's why, there is difference in memory allocation.