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

Python: Operations on Numpy Arrays

NumPy is an essential library in Python for numerical computing. Its main object is the homogeneous multidimensional array, which is useful for performing vectorized operations without the need for explicit loops.

Here's a tutorial on various operations you can perform on NumPy arrays:

Initialization
Before you can work with NumPy, you need to install and import it:
```
import numpy as np
```

Basic Operations

Arithmetic Operations: These are element-wise.

a = np.array([10, 20, 30, 40])
b = np.array([1, 2, 3, 4])

print(a + b)  # [11 22 33 44]
print(a - b)  # [9 18 27 36]
print(a * b)  # [10 40 90 160]
print(a / b)  # [10. 10. 10. 10.]

Universal Functions (ufunc): NumPy provides familiar mathematical functions such as sin, cos, exp, etc. These operate element-wise.
```
c = np.array([0, np.pi/2, np.pi])
print(np.sin(c))  # [0. 1. 0.]
```

Matrix Operations

Element-wise Multiplication:

A = np.array([[1, 2],
              [3, 4]])
B = np.array([[2, 0],
              [0, 2]])

print(A * B)  # Element-wise product

Matrix Product:
```
print(A @ B)  # or use np.dot(A, B)
```
Matrix Transposition:
```
print(A.T)
```

Aggregate Functions: Operations for computing aggregates.

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

print(d.sum())        # 45
print(d.min())        # 1
print(d.max())        # 9
print(d.sum(axis=0))  # sum of each column: [12 15 18]
print(d.cumsum(axis=1))  # cumulative sum along each row: [[ 1  3  6]
                         #                                  [ 4  9 15]
                         #                                  [ 7 15 24]]

Reshaping, Splitting, and Joining

Reshape:

e = np.arange(12).reshape(3, 4)
print(e)

Split:

f1, f2, f3 = np.split(e, 3)  # Split the array into 3 equal-sized subarrays
print(f1)

Concatenate/Join:

g = np.concatenate((f1, f2, f3))
print(g)

Slicing and Indexing

h = np.array([[1, 2, 3, 4],
              [5, 6, 7, 8],
              [9, 10, 11, 12]])

print(h[0, 1])      # 2
print(h[:, 1])      # [ 2  6 10]
print(h[1:3, :])    # [[ 5  6  7  8]
                    #  [ 9 10 11 12]]

Conditional Operations

i = np.array([[1, 2], [3, 4], [5, 6]])
bool_idx = (i > 2)
print(bool_idx)  # Returns an array of the same shape with True/False values
print(i[bool_idx])  # Returns a 1D array with the True values: [3 4 5 6]

Broadcasting: Broadcasting allows NumPy to work with arrays of different shapes.

j = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
k = np.array([1, 0, 1])
print(j + k)  # Adds k to each row of j

This is just a basic overview of the capabilities of NumPy. The library has a rich set of features and functions which you can explore further in the official documentation or various online resources.

1. Performing operations on arrays in Python with Numpy:

Performing operations on arrays involves applying various mathematical and statistical operations using NumPy.

import numpy as np

# Create two arrays for demonstration
array1 = np.array([1, 2, 3, 4])
array2 = np.array([5, 6, 7, 8])

# Perform addition of arrays
result_addition = array1 + array2

# Perform multiplication of arrays
result_multiplication = array1 * array2

print("Array 1:", array1)
print("Array 2:", array2)
print("Addition Result:", result_addition)
print("Multiplication Result:", result_multiplication)

2. Common array operations using Numpy in Python:

Common array operations include addition, subtraction, multiplication, and division using NumPy in Python.

# Assuming 'array1' and 'array2' are already defined

# Perform common array operations
result_addition = array1 + array2
result_subtraction = array1 - array2
result_multiplication = array1 * array2
result_division = array1 / array2

print("Array 1:", array1)
print("Array 2:", array2)
print("Addition Result:", result_addition)
print("Subtraction Result:", result_subtraction)
print("Multiplication Result:", result_multiplication)
print("Division Result:", result_division)

3. Introduction to array operations in Python's Numpy:

An introduction to array operations using NumPy, showcasing basic mathematical operations.

# Assuming 'array1' and 'array2' are already defined

# Perform basic array operations
result_addition = array1 + array2
result_multiplication = array1 * array2

print("Array 1:", array1)
print("Array 2:", array2)
print("Addition Result:", result_addition)
print("Multiplication Result:", result_multiplication)

4. Numpy array mathematical operations examples:

Explore mathematical operations on NumPy arrays, such as addition and multiplication.

# Assuming 'array1' and 'array2' are already defined

# Perform mathematical operations on arrays
result_addition = np.add(array1, array2)
result_multiplication = np.multiply(array1, array2)

print("Array 1:", array1)
print("Array 2:", array2)
print("Addition Result:", result_addition)
print("Multiplication Result:", result_multiplication)

5. Element-wise operations on Numpy arrays in Python:

Understand and perform element-wise operations on NumPy arrays, where each element is individually processed.

# Assuming 'array1' and 'array2' are already defined

# Perform element-wise operations
result_square = np.square(array1)
result_sqrt = np.sqrt(array2)

print("Array 1:", array1)
print("Array 2:", array2)
print("Square of Array 1:", result_square)
print("Square Root of Array 2:", result_sqrt)

6. Sample code for array operations with Numpy:

Sample code demonstrating various array operations using NumPy in Python.

# Assuming 'array1' and 'array2' are already defined

# Perform sample array operations
result_sum = np.sum(array1)
result_mean = np.mean(array2)

print("Array 1:", array1)
print("Array 2:", array2)
print("Sum of Array 1:", result_sum)
print("Mean of Array 2:", result_mean)

7. Aggregate and statistical operations on Numpy arrays:

Explore aggregate and statistical operations on NumPy arrays, such as sum and mean.

# Assuming 'array1' and 'array2' are already defined

# Perform aggregate and statistical operations
result_sum = np.sum(array1)
result_mean = np.mean(array2)

print("Array 1:", array1)
print("Array 2:", array2)
print("Sum of Array 1:", result_sum)
print("Mean of Array 2:", result_mean)

8. Efficient ways to perform operations on large Numpy arrays in Python:

Efficiently perform operations on large NumPy arrays by utilizing the vectorized nature of NumPy.

import numpy as np

# Create large arrays for demonstration
large_array1 = np.random.rand(10**6)
large_array2 = np.random.rand(10**6)

# Perform efficient array operations
result_addition = large_array1 + large_array2
result_mean = np.mean(large_array1)

print("Large Array 1 (Partial):", large_array1[:5])
print("Large Array 2 (Partial):", large_array2[:5])
print("Efficient Addition Result (Partial):", result_addition[:5])
print("Efficient Mean of Large Array 1:", result_mean)