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Numpy

Introduction Reading Time: 12 min

Table of Contents

Description

NumPy (Numerical Python) is a foundational Python library for numerical computing. It provides support for multi-dimensional arrays, a collection of high-performance mathematical functions, and tools for array manipulation, broadcasting, and indexing. It's widely used in Data Science, Machine Learning, AI, and scientific computing.

Prerequisites

  • Basic Python syntax
  • Understanding of lists and loops
  • Familiarity with functions and basic math operations

Examples

Here's a simple example of a data science task using Python:


import numpy as np

# Creating a NumPy array
arr = np.array([1, 2, 3])
print("Array:", arr)

# Array operations
print("Added 5:", arr + 5)       # Broadcasting: adds 5 to every element
print("Squared:", arr ** 2)      # Element-wise square

# 2D Array
matrix = np.array([[1, 2], [3, 4]])
print("Matrix:\n", matrix)

# Indexing
print("First row:", matrix[0])
print("Element at (1,1):", matrix[1][1])

# Broadcasting in 2D
print("Add 1 to all:", matrix + 1)

Real-World Applications

Data Science & Machine Learning

Handling datasets efficiently (fast numerical computation)
Feature scaling and transformation
Building mathematical models with large arrays

Image & Signal Processing

Representing pixel data as arrays
Applying filters via convolution

Finance

Large-scale numerical simulations
Vectorized calculations for pricing models.

Where Data Science Is Applied

Finance

  • Risk and return modeling using matrix operations
  • Portfolio simulations

Healthcare

  • Image-based analysis (e.g., CT, MRI using arrays)
  • Patient data modeling and transformation

E-commerce

  • Efficient recommendation algorithms using matrix multiplication
  • Data normalization and batch processing

Machine Learning

  • Underlying numerical operations in models like linear regression, PCA
  • Data preprocessing and augmentation

Robotics

  • Coordinate transformations and movement control using arrays
  • Sensor data processing with broadcasting

Resources

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Interview Questions

➤ NumPy is a Python library used for numerical operations with support for arrays, broadcasting, and mathematical functions. It offers high speed and memory efficiency, making it ideal for scientific computing.

➤ A Python list is generic and supports heterogeneous data types. A NumPy array is homogeneous and allows element-wise operations and faster computation due to optimized C-based backend.

➤ Broadcasting is a technique that allows NumPy to perform operations between arrays of different shapes by automatically expanding dimensions so the shapes are compatible.

➤ np.array(), np.zeros(), np.ones(), np.arange(), np.linspace(), np.reshape(), np.mean(), np.dot(), etc.

➤ NumPy supports multi-dimensional indexing, slicing, and even boolean indexing, making it much more powerful than standard list indexing.