Numpy Tutorial

Creating NumPy Array

NumPy Array Manipulation

Matrix in NumPy

Operations on NumPy Array

Reshaping NumPy Array

Indexing NumPy Array

Arithmetic operations on NumPy Array

Linear Algebra in NumPy Array

NumPy and Random Data

Sorting and Searching in NumPy Array

Universal Functions

Working With Images

Projects and Applications with NumPy

Numpy - Array Creation

NumPy is the foundational package for numerical computing in Python. One of its primary classes is the ndarray, or simply, array. This tutorial will guide you through various ways to create NumPy arrays.

Numpy - Array Creation

1. Setup:

First, ensure you have NumPy installed:

pip install numpy

Then, import the library:

import numpy as np

2. From a Python List:

You can create a NumPy array directly from a Python list or tuple.

arr = np.array([1, 2, 3, 4, 5])
print(arr)  # Output: [1 2 3 4 5]

For multi-dimensional arrays:

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(matrix)

3. Using zeros:

To create an array filled with zeros:

zeros_arr = np.zeros(5)
print(zeros_arr)  # Output: [0. 0. 0. 0. 0.]

4. Using ones:

For an array filled with ones:

ones_arr = np.ones(5)
print(ones_arr)  # Output: [1. 1. 1. 1. 1.]

5. Using arange:

To create an array with a sequence of numbers:

sequence_arr = np.arange(10)
print(sequence_arr)  # Output: [0 1 2 3 4 5 6 7 8 9]

6. Using linspace:

For evenly spaced numbers over a specified range:

linspace_arr = np.linspace(0, 1, 5)
print(linspace_arr)  # Output: [0.   0.25 0.5  0.75 1.  ]

7. Using empty:

To create an uninitialized array (its content is not predictable):

empty_arr = np.empty(5)
print(empty_arr)

8. Using eye:

For a 2-D array with ones on the diagonal and zeros elsewhere:

identity_matrix = np.eye(3)
print(identity_matrix)

9. Using full:

To create an array filled with any given value:

full_arr = np.full(5, 7)
print(full_arr)  # Output: [7 7 7 7 7]

10. From Random Values:

To create an array of random values:

random_arr = np.random.rand(5)
print(random_arr)

Conclusion:

NumPy offers a variety of methods to create arrays, from simple sequences to random initializations. Knowing which method to use can help in efficiently setting up your data structures for numerical and matrix operations.

1. Creating arrays in NumPy:

Description: NumPy provides various functions for creating arrays, allowing you to initialize arrays with specified values or shapes.

Code:

import numpy as np

# Creating a 1D array
array_1d = np.array([1, 2, 3])

# Creating a 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])

print("1D Array:")
print(array_1d)
print("2D Array:")
print(array_2d)

2. How to initialize arrays in NumPy:

Description: Initialization of arrays involves creating arrays with specified values or patterns.

Code:

import numpy as np

# Initializing a 1D array with zeros
zeros_array = np.zeros(5)

# Initializing a 2D array with ones
ones_array = np.ones((3, 4))

print("Zeros Array:")
print(zeros_array)
print("Ones Array:")
print(ones_array)

3. Python NumPy array creation functions:

Description: NumPy provides various array creation functions for different use cases, including np.zeros, np.ones, and np.empty.

Code:

import numpy as np

# Using array creation functions
zeros_array = np.zeros(3)
ones_array = np.ones((2, 4))
empty_array = np.empty(6)

print("Zeros Array:")
print(zeros_array)
print("Ones Array:")
print(ones_array)
print("Empty Array:")
print(empty_array)

4. NumPy array creation from a list:

Description: You can create a NumPy array by converting a Python list.

Code:

import numpy as np

# Creating an array from a Python list
python_list = [1, 2, 3, 4, 5]
numpy_array = np.array(python_list)

print("Python List:")
print(python_list)
print("NumPy Array:")
print(numpy_array)

5. Array initialization with NumPy in Python:

Description: Initialize arrays with specific values or patterns using NumPy's functions.

Code:

import numpy as np

# Initialize an array with a specific value
value_array = np.full((2, 3), 7)

# Initialize an array with a range of values
range_array = np.arange(1, 10, 2)

print("Array with Specific Value:")
print(value_array)
print("Array with Range of Values:")
print(range_array)

6. NumPy zeros and ones functions:

Description: NumPy provides np.zeros and np.ones functions to create arrays filled with zeros or ones.

Code:

import numpy as np

# Create arrays filled with zeros and ones
zeros_array = np.zeros((2, 3))
ones_array = np.ones(4)

print("Zeros Array:")
print(zeros_array)
print("Ones Array:")
print(ones_array)

7. Generating random arrays in NumPy:

Description: NumPy's np.random module allows the generation of arrays with random values.

Code:

import numpy as np

# Generate random arrays
random_array = np.random.random((2, 3))
randint_array = np.random.randint(1, 10, size=(3, 3))

print("Random Array:")
print(random_array)
print("Random Integer Array:")
print(randint_array)

8. Creating specific types of arrays with NumPy:

Description: NumPy enables the creation of specific types of arrays, such as identity matrices and diagonal matrices.

Code:

import numpy as np

# Create an identity matrix
identity_matrix = np.eye(3)

# Create a diagonal matrix
diagonal_matrix = np.diag([1, 2, 3])

print("Identity Matrix:")
print(identity_matrix)
print("Diagonal Matrix:")
print(diagonal_matrix)
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