Reshaping Arrays

Reshaping changes an array's dimensions while preserving its data.

Method reshape

The reshape method returns a new view with the specified shape.

1. Basic Usage

import numpy as np

def main():
    x = np.arange(6)
    print(f"{x.shape = }")
    print(x, end="\n\n")

    y = x.reshape((3, 2))
    print(f"{y.shape = }")
    print(y)

if __name__ == "__main__":
    main()

Output:

x.shape = (6,)
[0 1 2 3 4 5]

y.shape = (3, 2)
[[0 1]
 [2 3]
 [4 5]]

2. Total Size Rule

The product of new dimensions must equal the original total size.

Function np.reshape

The np.reshape function provides the same functionality.

1. Function Syntax

import numpy as np

def main():
    x = np.arange(6)
    print(f"{x.shape = }")
    print(x, end="\n\n")

    y = np.reshape(x, (3, 2))
    print(f"{y.shape = }")
    print(y)

if __name__ == "__main__":
    main()

2. Method vs Function

Both are equivalent; the method syntax is more common.

The -1 Wildcard

Using -1 lets NumPy infer one dimension automatically.

1. Flatten to 1D

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    y = x.reshape((-1,))
    print(f"{x.shape = }")
    print(f"{y.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (2, 3, 4)
y.shape = (24,)

2. Keep First Axis

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    z = x.reshape((2, -1))
    print(f"{x.shape = }")
    print(f"{z.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (2, 3, 4)
z.shape = (2, 12)

3. Keep Last Axis

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    w = x.reshape((-1, 4))
    print(f"{x.shape = }")
    print(f"{w.shape = }")

if __name__ == "__main__":
    main()

Output:

x.shape = (2, 3, 4)
w.shape = (6, 4)

Multiple Examples

Combining all -1 examples in one block.

1. All Together

import numpy as np

def main():
    x = np.zeros((2, 3, 4))
    y = x.reshape((-1,))
    z = x.reshape((2, -1))
    w = x.reshape((-1, 4))
    print(f"{x.shape = }")
    print(f"{y.shape = }")
    print(f"{z.shape = }")
    print(f"{w.shape = }")

if __name__ == "__main__":
    main()

2. Only One -1

You can only use -1 for a single dimension per reshape call.

---

Runnable Example: shape_manipulation_tutorial.py

"""
02_shape_manipulation.py - Reshaping and Transforming

🔗 Topic #24: Most operations return VIEWS (no memory copy)!
"""

import numpy as np

if __name__ == "__main__":

    print("="*80)
    print("SHAPE MANIPULATION")
    print("="*80)
    print("\n🔗 Remember: Most operations return VIEWS (Topic #24)!")

    # ============================================================================
    # Reshape
    # ============================================================================

    print("\n" + "="*80)
    print("Reshape - Change Dimensions")
    print("="*80)

    arr = np.arange(12)
    print(f"Original: {arr}")
    print(f"Shape: {arr.shape}")

    matrix = arr.reshape(3, 4)
    print(f"\nReshaped to (3, 4):\n{matrix}")

    # CRITICAL: Reshape returns a VIEW!
    print(f"\nIs it a view? {matrix.base is arr}")
    matrix[0, 0] = 999
    print(f"After matrix[0,0]=999: original arr[0]={arr[0]}")
    print("They share memory! (Topic #24)")

    # ============================================================================
    # Transpose
    # ============================================================================

    print("\n" + "="*80)
    print("Transpose - Flip Dimensions")
    print("="*80)

    matrix = np.array([[1, 2, 3], [4, 5, 6]])
    print(f"Original (2,3):\n{matrix}")
    transposed = matrix.T
    print(f"\nTransposed (3,2):\n{transposed}")
    print(f"Is view? {transposed.base is matrix}")

    # ============================================================================
    # Ravel and Flatten
    # ============================================================================

    print("\n" + "="*80)
    print("Ravel vs Flatten")
    print("="*80)

    matrix = np.array([[1, 2, 3], [4, 5, 6]])
    print(f"Matrix:\n{matrix}")

    ravel = matrix.ravel()  # Returns view if possible
    flatten = matrix.flatten()  # Always returns copy

    print(f"\nravel(): {ravel}")
    print(f"Is view? {ravel.base is matrix}")

    print(f"\nflatten(): {flatten}")
    print(f"Is view? {flatten.base is matrix}")

    print("""
    \nravel(): Fast (view if possible)
    flatten(): Slower (always copies)
    """)

    print("""
    \n🎯 KEY TAKEAWAYS:
    1. reshape() returns views (fast!)
    2. .T (transpose) returns views
    3. ravel() tries to return view
    4. flatten() always copies
    5. Check .base to verify view vs copy

    🔜 NEXT: 03_mathematical_ops.py
    """)