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Basic NumPy for beginners:

Creating a NumPy array: To create a NumPy array from a list or tuple, you can use the np.array() function. For example, the following code creates a NumPy array from a list of numbers:

import numpy as np

# Create a NumPy array from a list of numbers
numbers = [1, 2, 3, 4, 5]
numbers_array = np.array(numbers)

# Print the array
print(numbers_array)

output:
[1 2 3 4 5]

Basic mathematical operations: NumPy provides functions for performing mathematical operations on arrays, such as addition, subtraction, multiplication, and division. These operations can be performed element-wise, allowing for efficient computation on large datasets. For example, the following code adds two NumPy arrays element-wise:

import numpy as np

# Create two NumPy arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([6, 7, 8, 9, 10])

# Add the arrays element-wise
z = x + y

# Print the result
print(z)

output:
[ 7 9 11 13 15]

Indexing and slicing: NumPy arrays can be indexed and sliced just like lists. This allows you to access and manipulate specific elements or subarrays within an array. For example, the following code slices a NumPy array to extract the second and third elements:

import numpy as np

# Create a NumPy array
numbers = np.array([1, 2, 3, 4, 5])

# Slice the array to extract the second and third elements
subarray = numbers[1:3]

# Print the result
print(subarray)

output:
[2 3]

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Basic NumPy for beginners:

Creating a NumPy array: To create a NumPy array from a list or tuple, you can use the np.array() function. For example, the following code creates a NumPy array from a list of numbers:

import numpy as np

# Create a NumPy array from a list of numbers
numbers = [1, 2, 3, 4, 5]
numbers_array = np.array(numbers)

# Print the array
print(numbers_array)

output:
[1 2 3 4 5]

Basic mathematical operations: NumPy provides functions for performing mathematical operations on arrays, such as addition, subtraction, multiplication, and division. These operations can be performed element-wise, allowing for efficient computation on large datasets. For example, the following code adds two NumPy arrays element-wise:

import numpy as np

# Create two NumPy arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([6, 7, 8, 9, 10])

# Add the arrays element-wise
z = x + y

# Print the result
print(z)

output:
[ 7 9 11 13 15]

Indexing and slicing: NumPy arrays can be indexed and sliced just like lists. This allows you to access and manipulate specific elements or subarrays within an array. For example, the following code slices a NumPy array to extract the second and third elements:

import numpy as np

# Create a NumPy array
numbers = np.array([1, 2, 3, 4, 5])

# Slice the array to extract the second and third elements
subarray = numbers[1:3]

# Print the result
print(subarray)

output:
[2 3]

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