Thursday, 9 December 2021

Write a programs to demonstrate working with Data Reshaping in PYTHON

 The numpy.reshape() function shapes an array without changing the data of the array.

Syntax: numpy.reshape(array, shape, order = 'C')

In the above syntax parameters are

  1. array : [array_like]Input array
  2. shape : [int or tuples of int] e.g. if we are aranging an array with 10 elements then shaping
    it like numpy.reshape(4, 8) is wrong; we can do numpy.reshape(2, 5) or (5, 2)
  3. order : [C-contiguous, F-contiguous, A-contiguous; optional]
    C-contiguous order in memory(last index varies the fastest)
    C order means that operating row-rise on the array will be slightly quicker
    FORTRAN-contiguous order in memory (first index varies the fastest).
    F order means that column-wise operations will be faster.
    ‘A’ means to read / write the elements in Fortran-like index order if,
    array is Fortran contiguous in memory, C-like order otherwise

The function will return array which is reshaped without changing the data.

The  arange([start,] stop[, step,][, dtype]) : 

Returns an array with evenly spaced elements as per the interval. The interval mentioned is half-opened i.e. [Start, Stop) 

Parameters :

  1. start : [optional] start of interval range. By default start = 0
  2. stop  : end of interval range
  3. step  : [optional] step size of interval. By default step size = 1,  
  4. For any output out, this is the distance between two adjacent values, out[i+1] - out[i].
  5. dtype : type of output array

Return:
Array of evenly spaced values.
Length of array being generated  = Ceil((Stop - Start) / Step)

Example

import numpy as np

#array = geek.arrange(8) # The 'numpy' module has no attribute 'arange'
array1 = np.arange(8)
print("Original array : \n", array1)

# shape array with 2 rows and 4 columns
array2 = np.arange(8).reshape(2, 4)
print("\narray reshaped with 2 rows and 4 columns : \n", array2)

# shape array with 4 rows and 2 columns
array3 = np.arange(8).reshape(4 ,2)
print("\narray reshaped with 4 rows and 2 columns : \n", array3)

# Constructs 3D array
array4 = np.arange(8).reshape(2, 2, 2)
print("\nOriginal array reshaped to 3D : \n", array4)

Output

Original array : 
 [0 1 2 3 4 5 6 7]

array reshaped with 2 rows and 4 columns : 
 [[0 1 2 3]
 [4 5 6 7]]

array reshaped with 4 rows and 2 columns : 
 [[0 1]
 [2 3]
 [4 5]
 [6 7]]

Original array reshaped to 3D : 
 [[[0 1]
  [2 3]]

 [[4 5]
  [6 7]]]

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