PYTHON

-1 in numpy reshape

import numpy as np

# 2 X 3 matrix = 6 items
arr = np.array([[1, 2, 3], [4, 5, 6]])

# reshaping a matrix with -1 will let the function know that this dimension is not currently know
# x X 1 = 6 => x = 6
# therefore, by the end of this function, the new output will be 6 X 1 matrix
arr.reshape(-1, 1)

numpy reshape

import numpy as np

# 2d array
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

# print 2d array shape
print(arr.shape)
# output (2, 4)


# 4 dimension array
arr = np.array([1, 2, 3, 4], ndmin=4)

print(arr)
print('shape of array :', arr.shape)


# reshape array
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
arr = arr.reshape(4, 3)
print(arr)

# output
# [[ 1  2  3]
#  [ 4  5  6]
#  [ 7  8  9]
#  [10 11 12]]

# reshape uneven array
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
arr = arr.reshape(4, 3)
print(arr)

# output ValueError: cannot reshape array of size 11 into shape (4,3)

reshape python

>>> np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
       [2, 1, 5]])
>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
       [2, 1, 5]])

np reshape

np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
array([[1, 2],
       [3, 4],
       [5, 6]])

reshape array numpy

a = np.arange(6)
np.reshape(a, newshape=(1, 6))

numpy reshape

np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
       [3, 4, 5]])
np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0, 1, 2],
       [3, 4, 5]])
np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
       [2, 1, 5]])
np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
       [2, 1, 5]])

python reshape array

>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
       [2, 3],
       [4, 5]])

np.reshape()

a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])

.reshape(-1,1)

z.reshape(-1,1)
array([[ 1],
   [ 2],
   [ 3],
   [ 4],
   [ 5],
   [ 6],
   [ 7],
   [ 8],
   [ 9],
   [10],
   [11],
   [12]])

numpy reshape

>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])
>>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
array([[1, 2],
       [3, 4],
       [5, 6]])

Python NumPy Reshape function example

# Welcome to softhunt.net
# Python Program illustrating
# numpy.reshape() method
 
import numpy as np
 
# array = np.arrange(8)
# The 'numpy' module has no attribute 'arrange'
array1 = np.arange(8)
print("Original array : 
", array1)
 
# shape array with 3 rows and 3 columns
array2 = np.arange(8).reshape(2, 4)
print("
array reshaped with 2 rows and 4 columns : 
",
      array2)
 
# shape array with 4 rows and 2 columns
array3 = np.arange(8).reshape(4, 2)
print("
array reshaped with 4 rows and 2 columns : 
",
      array3)
 
# Constructs 3D array
array4 = np.arange(8).reshape(2, 2, 2)
print("
Original array reshaped to 3D : 
",
      array4)

Python NumPy Reshape function example

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