NumPy: A Comprehensive Guide with Examples | Lecture 1

 

NumPy: A Comprehensive Guide with Examples

NumPy (Numerical Python) is a powerful library in Python for numerical computing. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

Key Features of NumPy

  1. Efficient multi-dimensional array support: ndarray is the core object.
  2. Broadcasting: Operate on arrays of different shapes.
  3. Vectorized operations: Faster computation compared to loops.
  4. Integration with other libraries: Pandas, SciPy, etc.
  5. Extensive math functions: Trigonometric, statistical, and linear algebra.

Getting Started with NumPy

Installation

pip install numpy

Importing NumPy

import numpy as np

Core Concepts

1. Creating Arrays

# 1D array

arr1 = np.array([1, 2, 3, 4])

print(arr1)

 

# 2D array

arr2 = np.array([[1, 2], [3, 4]])

print(arr2)

 

# Array with zeros

zeros = np.zeros((3, 3))  # 3x3 matrix

print(zeros)

 

# Array with ones

ones = np.ones((2, 4))  # 2x4 matrix

print(ones)

 

# Array with a range of numbers

range_arr = np.arange(1, 10, 2)  # Start at 1, end before 10, step 2

print(range_arr)

 

# Array with equally spaced numbers

linspace_arr = np.linspace(0, 1, 5)  # 5 numbers between 0 and 1

print(linspace_arr)

 

# Random array

rand_arr = np.random.rand(3, 2)  # 3x2 matrix with values between 0 and 1

print(rand_arr)

2. Array Attributes

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

 

print(arr.shape)  # Shape of the array (rows, columns)

print(arr.size)   # Total number of elements

print(arr.ndim)   # Number of dimensions

print(arr.dtype)  # Data type of elements

3. Array Indexing and Slicing

arr = np.array([10, 20, 30, 40, 50])

 

# Accessing elements

print(arr[0])  # First element

print(arr[-1])  # Last element

 

# Slicing

print(arr[1:4])  # Elements from index 1 to 3

print(arr[:3])   # First three elements

print(arr[::2])  # Every second element

 

# 2D slicing

arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print(arr2d[1, 2])  # Element at 2nd row, 3rd column

print(arr2d[:2, 1:])  # Subarray with first two rows and last two columns

4. Operations on Arrays

arr1 = np.array([1, 2, 3])

arr2 = np.array([4, 5, 6])

 

# Element-wise operations

print(arr1 + arr2)  # [5 7 9]

print(arr1 * arr2)  # [4 10 18]

print(arr1 ** 2)    # [1 4 9]

 

# Mathematical operations

print(np.sum(arr1))     # Sum of elements

print(np.mean(arr1))    # Mean

print(np.std(arr1))     # Standard deviation

print(np.max(arr1))     # Maximum value

print(np.min(arr1))     # Minimum value

5. Matrix Operations

# Dot product

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

B = np.array([[5, 6], [7, 8]])

print(np.dot(A, B))

 

# Transpose

print(A.T)

 

# Determinant

print(np.linalg.det(A))

 

# Inverse

print(np.linalg.inv(A))

6. Broadcasting

Broadcasting allows operations on arrays with different shapes.

arr = np.array([1, 2, 3])

print(arr + 10)  # Add 10 to each element

7. Reshaping and Manipulating Arrays

arr = np.arange(1, 10)

 

# Reshape

reshaped = arr.reshape((3, 3))

print(reshaped)

 

# Flatten

flattened = reshaped.flatten()

print(flattened)

 

# Stack arrays

arr1 = np.array([1, 2])

arr2 = np.array([3, 4])

print(np.vstack((arr1, arr2)))  # Vertical stack

print(np.hstack((arr1, arr2)))  # Horizontal stack

8. Boolean Indexing

arr = np.array([10, 20, 30, 40, 50])

 

# Boolean condition

print(arr[arr > 25])  # [30 40 50]

9. Handling Missing Data

arr = np.array([1, 2, np.nan, 4, 5])

 

# Check for NaN

print(np.isnan(arr))

 

# Replace NaN with a value

arr[np.isnan(arr)] = 0

print(arr)

10. Saving and Loading Data

arr = np.array([1, 2, 3, 4, 5])

 

# Save array

np.save('array_file.npy', arr)

 

# Load array

loaded_arr = np.load('array_file.npy')

print(loaded_arr)

 

# Save/load text files

np.savetxt('array_file.txt', arr)

loaded_txt = np.loadtxt('array_file.txt')

print(loaded_txt)

Advanced Topics

1. Random Sampling

# Random integers

rand_ints = np.random.randint(1, 100, size=(3, 3))

print(rand_ints)

 

# Random seed

np.random.seed(42)  # Reproducible random numbers

print(np.random.rand(3))

2. Sorting

arr = np.array([3, 1, 2])

print(np.sort(arr))  # Sorted array

3. Advanced Indexing

arr = np.array([10, 20, 30, 40, 50])

 

# Using a list of indices

indices = [0, 2, 4]

print(arr[indices])  # [10, 30, 50]

Conclusion

NumPy is a fundamental library for scientific computing in Python. Its array manipulation, mathematical operations, and efficient computations make it an essential tool for data scientists, engineers, and researchers.

 

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