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Shape Manipulation & Reshape

Understanding shape

shapeshape tells the size of each dimension.

shape
import numpy as np
 
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # (2, 3)
shape
import numpy as np
 
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # (2, 3)

Reshape with .reshape().reshape()

Reshape changes the dimension layout without changing the data.

reshape
import numpy as np
 
arr = np.arange(1, 13)  # 1..12
mat = arr.reshape(3, 4) # 3 rows, 4 cols
print(mat)
reshape
import numpy as np
 
arr = np.arange(1, 13)  # 1..12
mat = arr.reshape(3, 4) # 3 rows, 4 cols
print(mat)

Using -1-1 (auto infer)

reshape-auto
import numpy as np
 
arr = np.arange(12)
mat = arr.reshape(3, -1)
print(mat.shape)  # (3, 4)
reshape-auto
import numpy as np
 
arr = np.arange(12)
mat = arr.reshape(3, -1)
print(mat.shape)  # (3, 4)

Flattening arrays

.ravel().ravel() (often a view)

ravel
import numpy as np
 
mat = np.array([[1, 2], [3, 4]])
flat = mat.ravel()
print(flat)
ravel
import numpy as np
 
mat = np.array([[1, 2], [3, 4]])
flat = mat.ravel()
print(flat)

.flatten().flatten() (always copy)

flatten
import numpy as np
 
mat = np.array([[1, 2], [3, 4]])
flat = mat.flatten()
print(flat)
flatten
import numpy as np
 
mat = np.array([[1, 2], [3, 4]])
flat = mat.flatten()
print(flat)

Add or remove dimensions

np.newaxisnp.newaxis / NoneNone

Convert a 1D array into a column vector:

newaxis
import numpy as np
 
arr = np.array([1, 2, 3])
col = arr[:, None]
print(col)
print(col.shape)  # (3, 1)
newaxis
import numpy as np
 
arr = np.array([1, 2, 3])
col = arr[:, None]
print(col)
print(col.shape)  # (3, 1)

np.expand_dimsnp.expand_dims

expand
import numpy as np
 
arr = np.array([1, 2, 3])
arr2 = np.expand_dims(arr, axis=0)
print(arr2.shape)  # (1, 3)
expand
import numpy as np
 
arr = np.array([1, 2, 3])
arr2 = np.expand_dims(arr, axis=0)
print(arr2.shape)  # (1, 3)

np.squeezenp.squeeze (remove size-1 dims)

squeeze
import numpy as np
 
arr = np.array([[[1], [2], [3]]])
print(arr.shape)           # (1, 3, 1)
print(np.squeeze(arr).shape)  # (3,)
squeeze
import numpy as np
 
arr = np.array([[[1], [2], [3]]])
print(arr.shape)           # (1, 3, 1)
print(np.squeeze(arr).shape)  # (3,)

Transpose (swap axes)

For 2D:

transpose
import numpy as np
 
mat = np.array([[1, 2, 3], [4, 5, 6]])
print(mat.T)
transpose
import numpy as np
 
mat = np.array([[1, 2, 3], [4, 5, 6]])
print(mat.T)

Next

Continue to: Broadcasting in NumPy to learn how NumPy applies operations between different shapes.

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