Essential Tools for Array Manipulation: Examples using numpy.shape()

1. Import NumPy
   You'll typically begin by importing the NumPy library using import numpy as np. This establishes the np alias to access NumPy functions and attributes conveniently.

2. Create a NumPy Array
   Arrays are the fundamental data structure in NumPy. You can create arrays using the np.array() function. For instance, to create a 2D array:

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

3. Get the Array Shape
   Once you have your NumPy array, use the .shape attribute to retrieve its shape. This attribute returns a tuple representing the number of elements along each dimension of the array.

   shape = arr.shape
   

4. Interpreting the Shape
   The returned tuple provides insights into the array's dimensionality. In the case of a 2D array, the shape might be (2, 3), indicating there are 2 rows (first element) and 3 columns (second element) in the array.

import numpy as np

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

# Get the shape of the array using numpy.shape()
shape = arr.shape

# Print the shape of the array
print(shape)

This code will output:

(2, 2, 3)

The output(2, 2, 3) signifies that the array has a shape of 2 (number of rows in the 3D array), 2 (number of columns in each sub-array within the 3D array), and 3 (number of elements in each row/column).

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Reshaping arrays

import numpy as np

# Create a 1D array
arr = np.array([1, 2, 3, 4, 5, 6])

# Print the original shape
print("Original shape:", arr.shape)

# Reshape the array into a 2x3 matrix (requires compatible total number of elements)
reshaped_arr = arr.reshape(2, 3)

# Print the reshaped array and its shape
print("Reshaped array:\n", reshaped_  arr)
print("Shape after reshape:", reshaped_arr.shape)

This code showcases how numpy.shape() helps verify if a reshape operation is possible (compatible total elements) and displays the resulting shape after the reshape.

Element-wise operations

import numpy as np

# Create two arrays with compatible shapes
arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([[7, 8, 9], [10, 11, 12]])

# Print the shapes of both arrays
print("Shape of arr1:", arr1.shape)
print("Shape of arr2:", arr2.shape)

# Perform element-wise addition (requires matching shapes)
sum_arr = arr1 + arr2

# Print the resulting array
print("Sum of arrays:\n", sum_arr)

Here, numpy.shape() ensures the arrays have compatible shapes before performing element-wise addition. If the shapes don't match, NumPy will raise an error.

Broadcasting

import numpy as np

# Create a 1D array and a 2D array
arr1 = np.array([1, 2, 3])
arr2 = np.array([[10, 20, 30], [40, 50, 60]])

# Print the shapes of both arrays
print("Shape of arr1:", arr1.shape)
print("Shape of arr2:", arr2.shape)

# Perform addition using broadcasting (arr1 is expanded to match arr2)
sum_arr = arr1 + arr2

# Print the resulting array
print("Sum using broadcasting:\n", sum_arr)

In this example, numpy.shape() helps understand how broadcasting works. Even though arr1 has a different shape, NumPy expands it to match arr2 element-wise for the addition operation.

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1. Using len() for 1D arrays
   

   • If you're dealing with a guaranteed 1D array and only need the total number of elements, you can use the built-in len() function. However, this won't provide information about the shape along different dimensions like numpy.shape() does.

   import numpy as np
   
   # Create a 1D array
   arr = np.array([1, 2, 3, 4])
   
   # Get the total number of elements using len()
   total_elements = len(arr)
   
   # Get the shape using numpy.shape()
   shape = arr.shape
   
   print("Total elements using len():", total_elements)
   print("Shape using numpy.shape():", shape)
   

   This code demonstrates that len() only returns the single value (4 in this case), whereas numpy.shape() returns the complete shape tuple (4,).

2. Attribute Access for Number of Dimensions
   

   • To retrieve the total number of dimensions (ranks) in an array, you can use the ndim attribute of the array.

   import numpy as np
   
   # Create a 3D array
   arr = np.array([[[1, 2, 3]]])
   
   # Get the number of dimensions using ndim
   num_dimensions = arr.ndim
   
   # Get the shape using numpy.shape()
   shape = arr.shape
   
   print("Number of dimensions:", num_dimensions)
   print("Shape using numpy.shape():", shape)
   

   Here, arr.ndim returns 3, indicating a 3D array, while arr.shape provides the complete shape information (1, 1, 3).