sort method over arrays in python

 sort method over arrays in python

count method upon an array in python

 count method upon an array in python

python program to copy an array as another array using copy method

  python program to copy an array as another array using copy method

 

 

python program to copy an array as

   another array using copy method

=============================

 

Code:

from numpy import *

a = arrange(1,6)

# this method creates a copy of array

b = a.copy()

print('Original Array',a)

print('New Array',b)

b[0]=20

print('After Modification')

print('Original Array:',a)

print('New Array:',b)

 

Output :

Original Array:[1,2,3,4,5]

New Array:[1,2,3,4,5]

After Modification

Original Array:[1,2,3,4,5]

New Array:[20,2,3,4,5]

 

 

 

What are the distinguishing property of arrays

 What are the distinguishing property of arrays

 

 

What is the distinguishing property of arrays

====================================

Arrays are a data structure which can increase or

decrease their size in memory at runtime. So , one need

not specify the size of the array at the time of creation

of the array.

 

 

What are the types of arrays in data-structure

  What are the types of arrays in data-structure

 

 

What are the types of arrays in data-structure

======================================

There are two types of arrays :

1) single dimensional arrays or 1D arrays

2) Multi-Dimensional Arrays or 2D arrays

 

 

What are the various functions and operators available for comparison operation over arrays in python

 What are the various functions and operators available for comparison operation over arrays in python

 

Question : What are the various functions and operators available for comparison operation over arrays in python ?

 

Logical Functions available in python for comparison

operation over arrays in python :

logical_and()

logical_or()

logical_not()

 

Operators available in python for comparison

operations over arrays are :

> -    greater than operator

>= -  greater than equal to operator

< -    less than operator

<= -  less than equal to operator

== -  equals comparision operator

!= -  not equal to operator

 

 

384 - pop method upon an array in python

 384 - pop method upon an array in python

 

 

pop method upon an array in python

===============================

 

'pop' method removes the specified item from the array

and returns the array. And if no element is specified

then the last index element in the array is removed and

the array is returned .

 

example:

import array as ar

a = ar.array('i',[10,20,30,40,50])

a.pop(10)

a.pop()

a.pop()

 

output:

array('i',[20,30,40,50])

array('i',[20,30,40])

array('i',[20,30])

 

 

 

sum method over arrays in python

 sum method over arrays in python

 

     

      sum method over arrays in python

============================

 

sum method is used over arrays in python to return the

sun of all elements in an array

 

example:

from numpy import *

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

print("Original Array:", arr)

print("Sum value:", sum(arr))

 

output:

10

 

 

 

 

python program to accept 4 integers in the same line and display their sum

 python program to accept 4 integers in the same line and display their sum

 

python program to accept 4 integers in the same line and display their sum

==============================================================

Code:

var1 ,var2,var3,var4 = [int(x) for x in input("Enter four numbers:").split()]

print('Sum =',var1+var2+var3+var4)

 

Output:

Enter four numbers: 11 12 13 14

Sum = 50

 

 

python program to know the effects of any and all functions

 python program to know the effects of any and all functions

 

 

               python program to know the effects of

     'any' and 'all' conditional functions

==================================================

 

code

from numpy import *

arr1 = array([1,2,3,4])

arr2 = array([3,2,1,4])

condition = arr1 > arr2

print('Result of arr1 > arr2',condition)

print('Check if any one element is true:', any(condition))

print("Check if all elements are true", all(condition))

if(any(arr1>arr2)):

    print('a contains at-least one element greater than those of b')

 

output

Result of arr1 > arr2 : [True False False False]

Check if any one element is true : True

Check if all elements are true : False

a contains at-least one element greater than those of b

 

 

 

python program to alias an array and understand how aliasing concept in arrays work

 python program to alias an array and understand how aliasing concept in arrays work

 

 

python program to alias an array

    and understand how aliasing

        concept in arrays work

=================================================

 

code

from numpy import *

a = arange(1,11)

b = a     #giving another name b to a

print('Before Modification:')

print('Original Array:',a)

print('Alias Array:',b)

print('#####################')

b[0]=20

print('After Modification:')

print('Original Array:',a)

print('Alias Array:',b)

 

output

Before Modification:
Original Array: [ 1  2  3  4  5  6  7  8  9 10]
Alias Array: [ 1  2  3  4  5  6  7  8  9 10]
#####################
After Modification:
Original Array: [20  2  3  4  5  6  7  8  9 10]
Alias Array: [20  2  3  4  5  6  7  8  9 10]

 

 

 

 

 

python program to create an array with 5 equal points using linspace() function

 python program to create an array with 5 equal points using linspace() function

 

            

              python program to create an array

     with 5 equal points using linspace() function

==========================================================================

import numpy as np

a = np.linspace(0,10,5)

print('a=',a)

 

output

arr.py

a = [0  2.5  5.  7.5  10. ]

 

 

 

 

python program to create an array from another array

 python program to create an array from another array

Procedure to perform various mathematical Operations on elements of arrays like addition , subtraction , multiplication , division

 Procedure to perform various mathematical Operations on elements of arrays like addition , subtraction , multiplication , division

 

   Procedure to perform various mathematical

Operations on elements of arrays like addition ,

         subtraction , multiplication , division

    ==================================

 

1) addition

import numpy as np

arr = np.array([10,20,30,40,50])

arr1 = arr + 5

print(arr1)

 

output

[15,25,35,45,55]

 

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

 

2) subtraction

import numpy as np

arr = np.array([10,20,30,40,50])

arr2 = arr - 5

print(arr2)

 

output

[5,15,25,35,45]

 

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

 

3) multiplication

import numpy as np

arr = np.array([10,20,30,40,50])

arr3 = arr - 5

print(arr3)

 

output

[50,100,150,200,250]

 

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

 

4) division

import numpy as np

arr = np.array([10,20,30,40,50])

arr4 = arr / 5

print(arr4)

 

output

[2,4,6,8,10]

 

 

procedure to create an array using linspace function

 procedure to create an array using linspace function

 

 

procedure to create an array using linspace function

=======================================================================

 

The linspace() function is used to create an array with

evenly spaced points between a starting point and

ending point .The expression of linspace() function is as

given below :

 

linspace(start,stop,n)

 

Here , 'start' represents the starting point .

'stop' represents the ending point.

'n' is an integer that represents the number of parts the

element should be divided . If 'n' is omitted , then n is

taken as 50.

 

Example

a = linspace(0,10,5)

 

In the above statement , an array is being created with

starting point element 0 and ending point element as

10 . This range is divided into 5 equal parts and hence

the points are 0 , 2, 5, 7.5 and 10 , and all these subdivided

elements within the range are stored into 'a' array .

Here , the starting and the ending point elements

are part of the array and not omitted from the array .

 

 

 

 

creating arrays in python using zeros() and ones() function

 creating arrays in python using zeros() and ones() function

 

                          creating arrays in python using

                     zeros() and ones() function

           ===============================

 

We can use the zeros() function to create an array with

zeros . The ones() function is useful to create an array

with all 1's.

zeros(n , datatype)

ones(n , datatype)

where , 'n' represents the number of elements .

 

If we do not specify the 'datatype' , then the default

datatype used by numpy is 'float'.

 

Examples

• >>> zeros(5)

This will create an array with 5 elements and all are

zeros and in floating datatype. This is represented as :

[ 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ].

 

 

• If we want this array in integer format , we can use 'int'

as datatype .

>>> zeros(10,int)

This will create an array as :

[ 0 0 0 0 0 0 0 0 0 0]

 

• If we want to use the ones() function , it will create an

array with all elements 1

>>> ones(5,float)

This will create an array with 5 float elements and all

are 1's :

[ 1.0 1.0 1.0 1.0 1.0]

 

 

 

create arrays using arange() function in python

 create arrays using arange() function in python

 

 

create arrays using arange() function in python

========================================

 

The arrange() function in numpy is same as range()

function in python . The arrange function is used in the

following format :

arrange(start,stop,stepsize)

This creates an array with a group of elements from 'start' to one element prior to 'stop' in steps of 'stepsize'.If the

'Stepsize' parameter is omitted , then it is taken as 1 .If the

'start' parameter is omitted ,then start is taken as 0.

 

 

import numpy as np

np.arange(10)

output

[1,2,3,4,5,6,7,8,9]

 

import numpy as np

np.arange(5,10)

output

[5,6,7,8,9]

 

import numpy as np

np.arange(1,10,3)

output

[1,4,7]

 

import numpy as np

np.arange(10,1,-1)

output

[10,9,8,7,6,5,4,3,2]

 

import numpy as np

np.arange(0,10,1.5)

output

[0 , 1.5 , 3 , 4.5 , 6 , 7.5 , 9]

 

import numpy as np

np.arange(2,21,2)

output

[2,4,6,8,10,12,14,16,18,20]

 

 

 

 

Arrays in Python

 Arrays in Python

Advantages of Arrays in Python

 Advantages of Arrays in Python

concept of pruning in decision tress in data science - a summary

 concept of pruning in decision tress in data science  - a summary