Introduction-to-NumPy-in-Python (1).pptx

Introduction to NumPy in
Python
NumPy is a fundamental package for scientific computing in Python,
enabling efficient and powerful numerical operations for a wide range
of applications.
by Озод Юсупов

What is NumPy?
Core Scientific Computing
NumPy is the cornerstone of scientific
computing in Python, providing
essential tools for numerical analysis,
data manipulation, and more.
Numerical Python
The name "NumPy" is short for
"Numerical Python," reflecting its
focus on numerical computation and
efficient data handling.
Created in 2005
Developed by Travis Oliphant in 2005,
NumPy has become a widely adopted
and essential library within the
Python ecosystem.

Why Use NumPy?
1 Significantly faster than
Python lists, often achieving
speed increases of 50 times
or more for numerical
computations.
2 Highly efficient for handling
large-scale numerical
operations, crucial for
processing extensive
datasets in data science
and machine learning.
3 Essential for various applications in data science and machine
learning, forming the foundation for many popular libraries and
frameworks.

Key Features of NumPy
ndarray
NumPy's ndarray object provides a multi-dimensional array structure for
representing and manipulating data in a structured and efficient way.
Vectorized Operations
NumPy allows for vectorized operations, enabling efficient computations across
entire arrays without the need for explicit loops.
Broadcasting
Broadcasting capabilities automatically expand the dimensions of arrays during
operations, simplifying complex calculations.
C/C++ Integration
NumPy offers tools for integrating C/C++ code, enabling faster execution for
computationally intensive tasks.

Installing and Importing NumPy
To install NumPy, use the following command in your
terminal:
pip install numpy
Import NumPy using the standard convention:
import numpy as np

NumPy Arrays vs Python Lists
NumPy Arrays
• Homogeneous data types
• Fixed size at creation
• Contiguous memory allocation
Python Lists
• Heterogeneous data types
• Dynamic size
• Scattered memory allocation

Creating NumPy Arrays
From Python lists:
np.array([1, 2, 3])
Using NumPy functions:
• np.zeros()
• np.ones()
• np.arange()

Array Indexing and Slicing
Basic Indexing
Access individual elements using their index: arr, arr[-1]
Slicing
Extract a range of elements: arr[1:5], arr[::2]
Boolean Indexing
Select elements based on conditions: arr[arr > 5]

Array Reshaping and Manipulation
Reshaping
Change the array's shape using
np.reshape()
1
Flattening
Transform a multi-dimensional array
into a 1D array using np.flatten() or
np.ravel()
2
Stacking
Combine multiple arrays vertically or
horizontally using np.vstack() or
np.hstack()
3

Basic Array Operations
Element-wise Operations
Perform arithmetic operations on
corresponding elements: +, -, *, /
Matrix Multiplication
Multiply matrices using np.dot() or
the @ operator
Aggregation
Calculate sums, means, maximum
values, etc., using functions like
np.sum(), np.mean(), np.max()

Broadcasting in NumPy
1
Broadcasting Rules
Determine how arrays are expanded to match dimensions for compatible
operations.
2
Broadcasting Operations
Demonstrate examples of broadcasting operations, illustrating the
automatic expansion of arrays.
3
Memory Efficiency
Highlight the advantages of broadcasting in terms of
memory efficiency and reduced resource usage.

Universal Functions (ufuncs)
1
Element-wise Functions
Apply functions to each element individually: np.sqrt(), np.exp()
2
Trigonometric Functions
Calculate trigonometric values: np.sin(), np.cos()
3
Comparison Functions
Perform comparisons between elements: np.greater(),
np.less()

Linear Algebra with NumPy
Matrix Operations
Perform basic matrix operations like
addition, subtraction, multiplication,
and inversion.
Eigenvalues and Eigenvectors
Calculate the eigenvalues and
eigenvectors of a matrix, which are
fundamental concepts in linear
algebra.
Solving Linear Equations
Solve systems of linear equations using
NumPy's linear algebra functions,
enabling analysis and modeling.

Random Number Generation
1
np.random module
NumPy's np.random module provides
tools for generating random numbers
and arrays.
2
Random Number Arrays
Generate arrays filled with random
numbers following various
distributions.
3
Reproducibility
Set random seeds for reproducibility,
ensuring consistency in generated
random numbers.

Performance Optimization
1 Employ vectorization
techniques to minimize
loops and leverage
NumPy's efficient array
operations.
2 Optimize memory
management by choosing
appropriate data types and
considering memory usage
during computations.
3 Utilize np.einsum() for complex operations involving summation
and contraction across array indices, improving performance.

NumPy Best Practices
Avoid loops when possible, leveraging NumPy's
vectorized operations for speed.
Choose appropriate data types for your arrays,
considering memory usage and computational efficiency.
Handle large datasets efficiently by using memory-
efficient techniques and chunking data for processing.

Common Pitfalls and How to
Avoid Them
Mutable Default
Arguments
Be mindful of mutable default
arguments, as they can lead to
unexpected behavior.
Copy vs View
Operations
Understand the difference
between creating copies and
views of arrays to avoid
unintended modifications.
Broadcasting Errors
Address potential broadcasting errors by carefully considering array
shapes and ensuring compatibility.

Conclusion and Resources
1
NumPy is a powerful and essential tool for scientific
computing in Python, enabling efficient and effective
numerical operations for diverse applications.
2
Refer to the official NumPy documentation for
comprehensive information and detailed
explanations of its functionalities.
3
Explore online tutorials and interactive platforms to
enhance your understanding and practical skills with
NumPy.
4
Engage with the vibrant NumPy community for
support, discussions, and collaboration on projects.

Introduction-to-NumPy-in-Python (1).pptx

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