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Introduction to NumPy

NumPy is a powerful library in Python for numerical computing. It provides support for arrays, matrices, and many mathematical functions that operate on these data structures. NumPy is widely used in data science, machine learning, scientific computing, and many other fields due to its efficiency and ease of use.

 

Key Features of NumPy

  1. N-Dimensional Arrays (ndarray): The core of NumPy is the ndarray object, which is a multi-dimensional array of elements, typically of a numerical type.
  2. Broadcasting: Allows arithmetic operations on arrays of different shapes.
  3. Vectorization: Eliminates the need for explicit loops by performing operations on entire arrays.
  4. Linear Algebra Functions: Provides numerous functions for linear algebra, including matrix multiplication, eigenvalues, and more.
  5. Random Number Generation: Includes functions for generating random numbers and performing random sampling.
  6. Integration with Other Libraries: NumPy arrays are the foundation for many other libraries such as pandas, SciPy, and scikit-learn.

 

 

 

Installing NumPy

You can install NumPy using pip:

pip install numpy
 

 

Importing NumPy

To use NumPy, you first need to import it:

import numpy as np

 

 

Creating Arrays

  1. From Lists:
arr = np.array([1, 2, 3, 4, 5])
print(arr)  # Output: [1 2 3 4 5]
 
  1. From Scratch:
arr = np.zeros((2, 3))
print(arr)  # Output: [[0. 0. 0.]
           #          [0. 0. 0.]]
arr = np.ones((2, 3))
print(arr)  # Output: [[1. 1. 1.]
           #          [1. 1. 1.]]
arr = np.eye(3)
print(arr)  # Output: [[1. 0. 0.]
           #          [0. 1. 0.]
           #          [0. 0. 1.]]
 

 

  1. Using arange and linspace:
arr = np.arange(0, 10, 2)
print(arr)  # Output: [0 2 4 6 8]
arr = np.linspace(0, 1, 5)
print(arr)  # Output: [0.   0.25 0.5  0.75 1. ]
 

 

Array Attributes

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # Output: (2, 3)
print(arr.ndim)   # Output: 2
print(arr.size)   # Output: 6
print(arr.dtype)  # Output: dtype('int64')
 

 

Array Operations

Arithmetic Operations

NumPy allows element-wise operations and broadcasting:

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1 + arr2)  # Output: [5 7 9]
print(arr1 * arr2)  # Output: [ 4 10 18]
print(arr1 + 5)     # Output: [6 7 8]

 

 

Indexing and Slicing

NumPy arrays can be indexed and sliced similarly to Python lists:

arr = np.array([1, 2, 3, 4, 5])
print(arr[0])      # Output: 1
print(arr[1:4])    # Output: [2 3 4]
print(arr[:3])     # Output: [1 2 3]
print(arr[::2])    # Output: [1 3 5]
 

 

Aggregation Functions

NumPy provides functions for aggregating data:

arr = np.array([1, 2, 3, 4, 5])
print(np.sum(arr))     # Output: 15
print(np.mean(arr))    # Output: 3.0
print(np.max(arr))     # Output: 5
print(np.min(arr))     # Output: 1
print(np.std(arr))     # Output: 1.4142135623730951

 

 

Broadcasting

Broadcasting allows NumPy to perform operations on arrays of different shapes:

arr = np.array([1, 2, 3])
print(arr + 1)      # Output: [2 3 4]
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix + arr) # Output: [[2 4 6]
                   #          [5 7 9]]
 

 

Random Number Generation

NumPy includes a submodule for random number generation:

rand_array = np.random.rand(3, 3)      # Uniform distribution
rand_ints = np.random.randint(0, 10, size=(3, 3))  # Random integers
rand_norm = np.random.randn(3, 3)      # Standard normal distribution
 

 

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