143. Multi-dimensional Arrays with numpy

Numpy is a powerful library in Python used for handling multi-dimensional arrays and performing mathematical operations on them. It provides efficient ways to work with large datasets and enables scientific computing tasks, such as matrix manipulation, linear algebra, statistics, and more.

Here are 10 Python code snippets demonstrating how to work with multi-dimensional arrays using numpy.

1. Creating a 2D Array

Creating a basic 2D (matrix-like) array using numpy.array().

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import numpy as np

# Creating a 2x3 matrix (2D array)
arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr)

Output:

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[[1 2 3]
 [4 5 6]]

Explanation:

• np.array() creates a 2D array with the provided nested list.

2. Creating an Array of Zeros

Creating a 2D array filled with zeros.

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import numpy as np

# Creating a 3x3 array filled with zeros
arr = np.zeros((3, 3))

print(arr)

Output:

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[[0. 0. 0.]
 [0. 0. 0.]
 [0. 0. 0.]]

Explanation:

• np.zeros() creates a multi-dimensional array of the given shape filled with zeros.

3. Creating an Array of Ones

Creating a 3x3 array filled with ones.

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import numpy as np

# Creating a 3x3 array filled with ones
arr = np.ones((3, 3))

print(arr)

Output:

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[[1. 1. 1.]
 [1. 1. 1.]
 [1. 1. 1.]]

Explanation:

• np.ones() creates a multi-dimensional array of the given shape filled with ones.

4. Reshaping Arrays

Changing the shape of an existing array.

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import numpy as np

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

# Reshaping it into a 2x3 array
reshaped_arr = arr.reshape(2, 3)

print(reshaped_arr)

Output:

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[[1 2 3]
 [4 5 6]]

Explanation:

• reshape() is used to change the shape of the array without changing its data.

5. Indexing and Slicing in 2D Arrays

Accessing elements and slicing rows and columns.

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import numpy as np

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

# Accessing element at row 1, column 2
element = arr[1, 2]

# Slicing the second row
row = arr[1]

# Slicing the second column
col = arr[:, 1]

print("Element:", element)
print("Row:", row)
print("Column:", col)

Output:

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Element: 6
Row: [4 5 6]
Column: [2 5 8]

Explanation:

• Use array indexing (arr[row, col]) to access specific elements.

• Use slicing to access rows (arr[row]) and columns (arr[:, col]).

6. Array Transposition

Transposing a 2D array to flip rows and columns.

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import numpy as np

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

# Transposing the array
transposed_arr = arr.T

print(transposed_arr)

Output:

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[[1 4]
 [2 5]
 [3 6]]

Explanation:

• .T is a shorthand to transpose the matrix, swapping rows and columns.

7. Element-wise Operations

Performing element-wise addition and multiplication on two arrays.

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import numpy as np

# Creating two 2D arrays
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

# Element-wise addition
sum_arr = arr1 + arr2

# Element-wise multiplication
prod_arr = arr1 * arr2

print("Sum Array:\n", sum_arr)
print("Product Array:\n", prod_arr)

Output:

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Sum Array:
 [[ 6  8]
 [10 12]]
Product Array:
 [[ 5 12]
 [21 32]]

Explanation:

• Numpy automatically performs element-wise addition and multiplication for arrays of the same shape.

8. Matrix Multiplication

Performing matrix multiplication using np.dot() or @.

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import numpy as np

# Creating two 2D arrays (matrices)
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

# Matrix multiplication
result = np.dot(arr1, arr2)

# Alternatively, you can use @ for matrix multiplication
result_alt = arr1 @ arr2

print("Matrix Product:\n", result)
print("Alternative Matrix Product:\n", result_alt)

Output:

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Matrix Product:
 [[19 22]
 [43 50]]
Alternative Matrix Product:
 [[19 22]
 [43 50]]

Explanation:

• np.dot() or @ performs matrix multiplication on two arrays (if they satisfy matrix multiplication conditions).

9. Broadcasting

Using broadcasting to perform element-wise operations on arrays of different shapes.

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import numpy as np

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

# Creating a 1D array
arr2 = np.array([10, 20, 30])

# Broadcasting the 1D array to the shape of the 2D array and adding them
result = arr + arr2

print(result)

Output:

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[[11 22 33]
 [14 25 36]]

Explanation:

• Broadcasting allows operations between arrays of different shapes, where smaller arrays are automatically expanded to match the larger array's shape.

10. Calculating the Mean, Sum, and Standard Deviation

Performing statistical operations on multi-dimensional arrays.

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import numpy as np

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

# Calculating the mean, sum, and standard deviation
mean = np.mean(arr)
sum_arr = np.sum(arr)
std_dev = np.std(arr)

print(f"Mean: {mean}")
print(f"Sum: {sum_arr}")
print(f"Standard Deviation: {std_dev}")

Output:

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Mean: 3.5
Sum: 21
Standard Deviation: 1.707825127659933

Explanation:

• Numpy provides easy-to-use functions like np.mean(), np.sum(), and np.std() to compute statistical metrics on arrays.

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Conclusion:

Using numpy, you can efficiently create and manipulate multi-dimensional arrays, perform mathematical operations, and work with large datasets. These functionalities are highly optimized for performance, making numpy an essential library for scientific computing and data analysis in Python.