PYTHON

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]])

python reshape array

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

Reshape (-1,1)

np_scaled = scaler.fit_transform(data.values.reshape(-1,1))

.reshape(-1,1)

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

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

# Python Program illustrating
# numpy.reshape() method
 
import numpy as geek
 
# array = geek.arrange(8)
# The 'numpy' module has no attribute 'arrange'
array1 = geek.arange(8)
print("Original array : 
", array1)
 
# shape array with 2 rows and 4 columns
array2 = geek.arange(8).reshape(-1, 1)
print("
array reshaped with 2 rows and 4 columns : 
",
      array2)
 
# shape array with 4 rows and 2 columns
array3 = geek.arange(8).reshape(4, 2)
print("
array reshaped with 2 rows and 4 columns : 
",
      array3)
 
# Constructs 3D array
array4 = geek.arange(8).reshape(2, 2, 2)
print("
Original array reshaped to 3D : 
",
      array4)