Python - NumPy Part 4: Reshaping and Manipulating Arrays

Reshaping allows flexibility in structuring data, while manipulation enables dynamic modifications.

Examples and Explanation

Reshaping Arrays

array = np.arange(1, 10)

reshaped = array.reshape(3, 3)

print(reshaped)

# Output:

# [[1 2 3]

#  [4 5 6]

#  [7 8 9]]

Explanation: Reshape the array to any compatible dimensions. This is crucial for handling multidimensional data.

Flattening Arrays

flat = reshaped.flatten()

print(flat)  # Output: [1 2 3 4 5 6 7 8 9]

Explanation: Converts multi-dimensional arrays back to 1D.

Concatenating Arrays

array1 = np.array([1, 2, 3])

array2 = np.array([4, 5, 6])

print(np.concatenate((array1, array2)))  # Output: [1 2 3 4 5 6]

Explanation: Joins two or more arrays along an existing axis.

Splitting Arrays

split = np.split(array, 3)

print(split)  # Output: [array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])]

Explanation: Divides arrays into smaller sub-arrays.

Transposing Arrays

print(reshaped.T)

# Output:

# [[1 4 7]

#  [2 5 8]

#  [3 6 9]]

Explanation: Switches rows and columns, commonly used in matrix computations.