NumPy Mastery: Empowering Data Analysis and Computation: NumPy Demystified: A Comprehensive Guide to Efficient Computation

Table of contents :
Introduction to NumPy
What is NumPy?
Getting Started with NumPy
Installing NumPy
Importing NumPy
NumPy Arrays: ndarray
Creating NumPy Arrays
Array Indexing and Slicing
Array Operations
Broadcasting in NumPy
Broadcasting Example
Advanced Functionalities
Universal Functions (ufuncs)
Array Manipulation
Getting Started with NumPy: An Introduction to Numerical Computing in Python
Installing NumPy
Importing NumPy
NumPy Arrays: The ndarray
Creating NumPy Arrays
Array Indexing and Slicing
Array Operations
Broadcasting in NumPy
Broadcasting Example
Advanced Functionalities
Universal Functions (ufuncs)
Array Manipulation
Conclusion
NumPy: Creating Arrays
Creating Arrays in NumPy
1. np.array()
2. np.zeros()
3. np.ones()
4. np.arange()
5. np.linspace()
6. np.eye()
7. np.random.rand() and np.random.randn()
8. Creating Arrays with Specific Data Types
Understanding Array Properties
Conclusion
NumPy Array Indexing: Explained with Examples
Basic Indexing in NumPy
Accessing Elements
Indexing Multi-Dimensional Arrays
Array Slicing in NumPy
Slicing 1D Arrays
Slicing Multi-Dimensional Arrays
Advanced Indexing Techniques
Integer Array Indexing
Boolean Indexing
Modifying Array Elements
Working with Views vs. Copies
Conclusion
NumPy Array Slicing Explained: A Comprehensive Guide with Examples
Basic Array Slicing in NumPy
Slicing 1D Arrays
Slicing Multi-Dimensional Arrays
Advanced Array Slicing Techniques
Using Ellipsis (...) in Slicing
Negative Slicing Indices
Modifying Arrays Using Slicing
Working with Step-wise Slicing
Slice Views vs. Copies
Conclusion
Understanding NumPy Data Types: Explained with Examples
Basics of NumPy Data Types
Data Types in NumPy
Specifying Data Types
Common Numeric Data Types in NumPy
Integers
Floating Point Numbers
Complex Numbers
Additional Data Types in NumPy
Boolean Data Type
Strings and Unicode
Type Conversion and Casting in NumPy
Casting Data Types
Type Inference
Data Type Attributes and Properties
Data Type Attributes
Conclusion
Understanding NumPy Copy vs. View: Explained with Examples
Copy vs. View in NumPy
Copy: Creating a New Array
View: Creating a View of the Same Data
Identifying Copy and View in NumPy
Checking if an Array is a View
Creating a Copy of an Array
Examples Demonstrating Copy and View
Example 1: Slicing Creates a View
Example 2: Reshaping Creates a View
Example 3: Copy Method Creates a Copy
Modifying Arrays: Impact on Copy and View
Modifying a View
Modifying a Copy
Performance Implications
Memory Efficiency
Speed and Efficiency
Conclusion
Understanding NumPy Array Shape: Explained with Examples
Basics of NumPy Array Shape
Shape of an Array
Identifying Array Dimensions
Accessing and Modifying Array Shapes
Accessing Array Shape
Modifying Array Shape
Understanding Array Reshaping
Reshaping to Higher Dimensions
Automatic Dimension Inference
Operations Based on Array Shape
Element-Wise Operations
Broadcasting
Conclusion
Understanding NumPy Array Reshape: Explained with Examples
Basics of NumPy Array Reshape
Understanding Reshaping
Using the reshape() Function
Reshaping to Higher Dimensions
Reshaping with Automatic Dimension Inference
Practical Examples Demonstrating Array Reshaping
Reshaping to Different Dimensions
Reshaping for Transposing Arrays
Practical Applications of Array Reshaping
Image Processing
Conclusion
Exploring NumPy Array Iterating: Examples and Explanation
Iterating through NumPy Arrays
Using nditer() Function
Iterating with Different Data Types
Multi-dimensional Array Iteration
Iterating 2D Arrays
Using ndenumerate()
Understanding Iteration Order
C-style and F-style Iteration
Optimizing Performance during Iteration
Contiguous Iteration
Using flags Argument
Conclusion
Understanding NumPy Array Join: Explained with Examples
Basics of NumPy Array Joining
Concatenating Arrays with np.concatenate()
Vertical and Horizontal Stacking
Understanding Axis in NumPy Arrays
Axis Concept in NumPy
Joining Arrays in Higher Dimensions
Concatenating along Higher Dimensions
Practical Examples of Array Joining
Combining Multiple Arrays
Joining along Different Axes
Conclusion
Understanding NumPy Array Split: Explained with Examples
Basics of NumPy Array Splitting
Splitting Arrays with np.split()
Vertical and Horizontal Splitting
Understanding Axis in NumPy Arrays
Axis Concept in NumPy
Splitting Arrays at Specified Positions
Splitting at Specified Indices
Practical Examples of Array Splitting
Splitting Arrays into Unequal Parts
Vertical and Horizontal Splitting for 2D Arrays
Conclusion
Understanding NumPy Array Search: Explained with Examples
Basics of NumPy Array Search
Finding Elements with np.where()
Using np.argwhere() for Index-Based Search
Element-Wise and Conditional Searches
Element-Wise Comparison with np.nonzero()
Conditional Search for Multiple Conditions
Practical Examples of Array Searching
Finding Specific Elements
Identifying Outliers in Data
Conclusion
Understanding NumPy Array Sort: Explained with Examples
Basics of NumPy Array Sorting
Sorting Elements with np.sort()
Sorting Indices with np.argsort()
Sorting along Specified Axes
Sorting along Different Axes
Sorting in Ascending and Descending Order
Sorting in Descending Order
Practical Examples of Array Sorting
Sorting Arrays with String Elements
Sorting Based on Multiple Columns
Conclusion
Understanding NumPy Array Filtering: Explained with Examples
Basics of NumPy Array Filtering
Filtering with Boolean Indexing
Using np.where() for Conditional Filtering
Element-Wise and Conditional Filtering
Element-Wise Comparison for Filtering
Conditional Filtering with Multiple Conditions
Handling Multi-Dimensional Arrays
Filtering for 2D Arrays
Conditional Filtering in Multi-Dimensional Arrays
Practical Examples of Array Filtering
Extracting Specific Elements
Filtering Outliers in Data
Conclusion
Understanding NumPy Random: Explained with Examples
Basics of NumPy Random Module
Generating Random Numbers
Generating Random Integers
Setting Seeds for Reproducibility
Setting a Seed for Reproducibility
Generating Random Distributions
Uniform Distribution
Normal Distribution
Random Sampling
Random Sampling from Arrays
Shuffling Arrays
Advanced Randomization Techniques
Permutation
Beta Distribution
Conclusion
Introduction to Randomness in Programming: Explained with Examples
Understanding Randomness
What is Randomness?
Importance of Randomness in Programming
Randomness in Programming Languages
Randomness in Python
Randomness in JavaScript
Randomness in Other Languages
Types of Randomness
Pseudo-Randomness
True Randomness
Generating Random Sequences
Random Seed
Applications of Randomness
Simulations and Modeling
Cryptography
Gaming and Entertainment
Statistical Analysis
Conclusion
Understanding Data Distribution: Explained with Examples
Basics of Data Distribution
What is Data Distribution?
Importance of Data Distribution
Types of Data Distributions
Normal Distribution
Uniform Distribution
Skewed Distribution
Exponential Distribution
Measures of Central Tendency
Mean, Median, and Mode
Exploring Data Distribution in Python
Generating Random Distributions
Real-World Applications
Finance and Economics
Healthcare and Biology
Social Sciences
Conclusion
Understanding Random Permutation: Explained with Examples
Basics of Random Permutation
What is a Permutation?
Importance of Random Permutation
Methods to Generate Random Permutations
Using Built-in Functions in Programming Languages
Using Permutation Algorithms
Applications of Random Permutation
Cryptography
Statistical Sampling
Game Development
Data Analysis
Properties of Random Permutations
Unpredictability
Uniformity
Repeatability
Conclusion
Understanding Seaborn Module in Python: Explained with Examples
Features of Seaborn
1. High-Level Interface
2. Built-in Themes and Styles
3. Statistical Estimation
4. Dataset-oriented API
Getting Started with Seaborn
Installation
Importing Seaborn
Common Types of Plots in Seaborn
1. Scatter Plot
2. Histogram
3. Bar Plot
4. Box Plot
Customization and Styling
Customizing Plot Aesthetics
Changing Plot Size and Aspect Ratio
Conclusion
Understanding Normal Distribution: Explained with Examples
Basics of Normal Distribution
What is Normal Distribution?
Properties of Normal Distribution
Standard Normal Distribution
Mathematical Representation
Characteristics of Normal Distribution
Skewness and Kurtosis
Z-Score
Examples of Normal Distribution
Generating Normal Distribution in Python
Real-World Applications
Finance and Economics
Natural Phenomena
Quality Control
Statistical Analysis
Importance of Normal Distribution
Central Limit Theorem
Statistical Modeling
Conclusion
Understanding Binomial Distribution: Explained with Examples
Basics of Binomial Distribution
What is Binomial Distribution?
Characteristics of Binomial Distribution
Mathematical Representation
Characteristics and Properties
Mean and Variance
Binomial Coefficient
Examples of Binomial Distribution
Flipping a Coin
Real-World Applications
Quality Control
Finance
Biology
Psychology and Social Sciences
Importance of Binomial Distribution
Statistical Analysis
Foundation for Other Distributions
Conclusion
Understanding Poisson Distribution: Explained with Examples
Basics of Poisson Distribution
What is Poisson Distribution?
Characteristics of Poisson Distribution
Mathematical Representation
Characteristics and Properties
Mean and Variance
Probability Mass Function
Examples of Poisson Distribution
Arrival of Customers at a Restaurant
Real-World Applications
Telecommunications
Biology
Finance
Quality Control
Importance of Poisson Distribution
Rare Event Modeling
Decision Making
Conclusion
Understanding Uniform Distribution: Explained with Examples
Basics of Uniform Distribution
What is Uniform Distribution?
Characteristics of Uniform Distribution
Mathematical Representation
Characteristics and Properties
Mean and Variance
Probability Density Function
Examples of Uniform Distribution
Generating Uniform Distribution in Python
Real-World Applications
Random Number Generation
Finance
Quality Control
Simulation and Modeling
Importance of Uniform Distribution
Equal Likelihood
Simplicity and Predictability
Conclusion
Understanding Logistic Distribution: Explained with Examples
Basics of Logistic Distribution
What is Logistic Distribution?
Characteristics of Logistic Distribution
Mathematical Representation
Characteristics and Properties
Mean and Variance
Probability Density Function
Examples of Logistic Distribution
Generating Logistic Distribution in Python
Real-World Applications
Population Dynamics
Extreme Value Analysis
Growth Processes
Forecasting and Predictions
Importance of Logistic Distribution
Versatility
Statistical Modeling
Conclusion
Understanding Multinomial Distribution: Explained with Examples
Basics of Multinomial Distribution
What is Multinomial Distribution?
Characteristics of Multinomial Distribution
Mathematical Representation
Characteristics and Properties
Conditions
Mean and Variance
Examples of Multinomial Distribution
Simulating Multinomial Distribution in Python
Real-World Applications
Genetics and Biology
Market Research
Sports Analytics
Social Sciences
Importance of Multinomial Distribution
Versatility
Data Analysis
Conclusion
Understanding Exponential Distribution: Explained with Examples
Basics of Exponential Distribution
What is Exponential Distribution?
Characteristics of Exponential Distribution
Mathematical Representation
Characteristics and Properties
Mean and Variance
Probability Density Function
Examples of Exponential Distribution
Generating Exponential Distribution in Python
Real-World Applications
Reliability Engineering
Queueing Theory
Survival Analysis
Finance
Importance of Exponential Distribution
Time-to-Event Modeling
Predictive Analysis
Conclusion
Understanding Chi-Square Distribution: Explained with Examples
Basics of Chi-Square Distribution
What is Chi-Square Distribution?
Characteristics of Chi-Square Distribution
Mathematical Representation
Characteristics and Properties
Degrees of Freedom
Mean and Variance
Probability Density Function
Examples of Chi-Square Distribution
Generating Chi-Square Distribution in Python
Real-World Applications
Statistical Testing
Biology and Health Sciences
Engineering
Finance
Importance of Chi-Square Distribution
Statistical Analysis
Versatility
Conclusion
Understanding Rayleigh Distribution: Explained with Examples
Basics of Rayleigh Distribution
What is Rayleigh Distribution?
Characteristics of Rayleigh Distribution
Mathematical Representation
Characteristics and Properties
Scale Parameter
Mean and Variance
Probability Density Function
Examples of Rayleigh Distribution
Generating Rayleigh Distribution in Python
Real-World Applications
Wireless Communication
Radar Systems
Reliability Engineering
Wind Speed Analysis
Importance of Rayleigh Distribution
Signal Strength Modeling
Reliability Assessment
Conclusion
Understanding Pareto Distribution: Explained with Examples
Basics of Pareto Distribution
What is Pareto Distribution?
Characteristics of Pareto Distribution
Mathematical Representation
Characteristics and Properties
Shape Parameter
Minimum Value
Mean and Variance
Probability Density Function
Examples of Pareto Distribution
Generating Pareto Distribution in Python
Real-World Applications
Wealth Distribution
City Sizes
Internet Traffic
Natural Disasters
Importance of Pareto Distribution
Economic and Social Analysis
Risk Assessment
Conclusion
Understanding Zipf Distribution: Explained with Examples
Basics of Zipf Distribution
What is Zipf Distribution?
Characteristics of Zipf Distribution
Mathematical Representation
Characteristics and Properties
Shape Parameter
Rank and Frequency Relationship
Examples of Zipf Distribution
Simulating Zipf Distribution in Python
Real-World Applications
Linguistics
Information Retrieval
Economics
Internet Traffic Analysis
Importance of Zipf Distribution
Language Modeling
Data Analysis
Conclusion
Understanding NumPy ufunc: Explained with Examples
Basics of NumPy ufunc
What are NumPy ufuncs?
Characteristics of NumPy ufuncs
Mathematical Operations with NumPy ufuncs
Creating NumPy Arrays
Basic Examples of NumPy ufunc
Addition with ufuncs
Trigonometric Functions with ufuncs
Comparison Operations with ufuncs
Advanced Features of NumPy ufunc
Broadcasting with ufuncs
Reduction Operations with ufuncs
Masked Operations with ufuncs
Real-World Applications
Data Analysis and Scientific Computing
Image Processing
Financial Modeling
Physical Simulations and Engineering
Importance of NumPy ufunc
Performance Optimization
Code Readability and Efficiency
Conclusion
Introduction to ufuncs in NumPy: Explained with Examples
Understanding ufuncs in NumPy
What are ufuncs?
Key Characteristics of ufuncs
Advantages of Using ufuncs
Basic Examples of ufuncs in NumPy
Mathematical Operations
Trigonometric Functions
Comparison Operations
Advanced Features of ufuncs in NumPy
Broadcasting
Reduction Operations
Masked Operations
Real-World Applications of ufuncs
Scientific Computing and Data Analysis
Image Processing and Computer Vision
Financial Modeling and Analytics
Physical Simulations and Engineering
Importance of ufuncs in NumPy
Computational Efficiency
Code Readability and Maintainability
Conclusion
Basics of Creating ufuncs in NumPy
What are User-Defined ufuncs?
Creating ufuncs using numpy.frompyfunc()
Creating ufuncs using numpy.vectorize()
Examples of Creating ufuncs in NumPy
Example 1: Simple Function
Example 2: Complex Function
Advanced Features and Use Cases
Broadcasting Support
Handling Multiple Input Arrays
Use Cases
Real-World Applications of User-Defined ufuncs
Scientific Computing
Engineering and Physics
Image Processing and Computer Vision
Financial Modeling and Analytics
Importance and Benefits of User-Defined ufuncs
Customized Operations
Computational Efficiency
Conclusion
Understanding NumPy's ufunc for Simple Arithmetic Operations: Explained with Examples
Basics of ufunc for Simple Arithmetic Operations
What are ufuncs in NumPy?
Characteristics of ufuncs for Simple Arithmetic
Mathematical Operations Supported by ufuncs
Examples of Simple Arithmetic Operations using ufuncs
Addition
Subtraction
Multiplication
Division
Exponentiation
Real-World Applications of Simple Arithmetic ufuncs
Scientific Computing
Data Analysis and Processing
Financial Modeling
Signal Processing and Engineering
Importance and Benefits of ufuncs for Simple Arithmetic Operations
Computational Efficiency
Code Readability and Maintainability
Conclusion
Understanding Rounding Decimals with NumPy's ufunc: Explained with Examples
Basics of Rounding Decimals with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Rounding Decimals ufuncs
Rounding Methods Supported by ufuncs
Examples of Rounding Decimals using ufuncs in NumPy
Round to the Nearest Integer
Round Down
Round Up
Round to a Specified Number of Decimals
Real-World Applications of Rounding Decimals ufuncs
Financial Calculations
Data Analysis and Reporting
Scientific Computations
Machine Learning and Data Preprocessing
Importance and Benefits of Rounding Decimals ufuncs
Precision and Accuracy
Data Presentation
Conclusion
Understanding Logarithmic Operations with NumPy's ufunc: Explained with Examples
Basics of Logarithmic Operations with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Logarithmic ufuncs
Logarithmic Functions Supported by ufuncs
Examples of Logarithmic Operations using ufuncs in NumPy
Natural Logarithm (Base e)
Base-10 Logarithm
Base-2 Logarithm
Real-World Applications of Logarithmic ufuncs
Scientific Computations
Engineering and Physics
Financial Modeling and Analytics
Machine Learning and Data Processing
Importance and Benefits of Logarithmic ufuncs
Scaling and Data Transformation
Computational Efficiency
Conclusion
Understanding Summation Operations with NumPy's ufunc: Explained with Examples
Basics of Summation Operations with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Summation ufuncs
Summation Methods Supported by ufuncs
Examples of Summation Operations using ufuncs in NumPy
Summation of All Elements
Column-Wise Summation
Row-Wise Summation
Real-World Applications of Summation ufuncs
Statistical Analysis
Data Processing and Aggregation
Scientific Computations
Machine Learning and Feature Engineering
Importance and Benefits of Summation ufuncs
Aggregation and Analysis
Computational Efficiency
Conclusion
Understanding Product Operations with NumPy's ufunc: Explained with Examples
Basics of Product Operations with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Product ufuncs
Product Methods Supported by ufuncs
Examples of Product Operations using ufuncs in NumPy
Product of All Elements
Column-Wise Product
Row-Wise Product
Real-World Applications of Product ufuncs
Statistical Analysis
Finance and Economics
Scientific Computations
Machine Learning and Data Processing
Importance and Benefits of Product ufuncs
Computational Efficiency
Analytical Capabilities
Conclusion
Understanding Differences Operations with NumPy's ufunc: Explained with Examples
Basics of Differences Operations with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Differences ufuncs
Differences Methods Supported by ufuncs
Examples of Differences Operations using ufuncs in NumPy
Differences between Adjacent Elements
Differences along Specified Axis
Real-World Applications of Differences ufuncs
Signal Processing and Time Series Analysis
Financial Analysis
Scientific Computations
Machine Learning and Data Preprocessing
Importance and Benefits of Differences ufuncs
Trend Detection and Analysis
Computational Efficiency
Conclusion
Understanding Least Common Multiple (LCM) with NumPy's ufunc: Explained with Examples
Basics of Finding LCM with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of LCM ufuncs
LCM Methods Supported by ufuncs
Examples of Finding LCM using ufuncs in NumPy
Pairwise LCM Calculation
LCM along Specified Axis
Real-World Applications of LCM ufuncs
Number Theory and Mathematics
Algorithm Design and Problem-Solving
Data Processing and Analysis
Engineering and Optimization
Importance and Benefits of LCM ufuncs
Mathematical Computations
Computational Efficiency
Conclusion
Understanding Greatest Common Divisor (GCD) with NumPy's ufunc: Explained with Examples
Basics of Finding GCD with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of GCD ufuncs
GCD Methods Supported by ufuncs
Examples of Finding GCD using ufuncs in NumPy
Pairwise GCD Calculation
GCD along Specified Axis
Real-World Applications of GCD ufuncs
Number Theory and Mathematics
Algorithm Design and Optimization
Engineering and Cryptography
Data Analysis and Processing
Importance and Benefits of GCD ufuncs
Mathematical Computations
Computational Efficiency
Conclusion
Understanding Trigonometric Functions with NumPy's ufunc: Explained with Examples
Basics of Trigonometric Functions with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Trigonometric ufuncs
Trigonometric Functions Supported by ufuncs
Examples of Trigonometric Functions using ufuncs in NumPy
Sine Function
Cosine Function
Tangent Function
Real-World Applications of Trigonometric ufuncs
Scientific Computations
Engineering and Physics
Data Visualization
Geometry and Navigation
Importance and Benefits of Trigonometric ufuncs
Mathematical Computations
Versatility and Flexibility
Conclusion
Understanding Hyperbolic Functions with NumPy's ufunc: Explained with Examples
Basics of Hyperbolic Functions with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Hyperbolic ufuncs
Hyperbolic Functions Supported by ufuncs
Examples of Hyperbolic Functions using ufuncs in NumPy
Hyperbolic Sine Function
Hyperbolic Cosine Function
Hyperbolic Tangent Function
Real-World Applications of Hyperbolic ufuncs
Scientific Computations
Engineering and Signal Processing
Data Transformation and Machine Learning
Finance and Economics
Importance and Benefits of Hyperbolic ufuncs
Computational Efficiency
Versatility in Mathematical Operations
Conclusion
Understanding Set Operations with NumPy's ufunc: Explained with Examples
Basics of Set Operations with ufuncs in NumPy
What are ufuncs in NumPy?
Characteristics of Set Operation ufuncs
Set Operations Supported by ufuncs
Examples of Set Operations using ufuncs in NumPy
Union of Sets
Intersection of Sets
Difference of Sets
Symmetric Difference of Sets
Real-World Applications of Set Operation ufuncs
Data Manipulation and Processing
Database Operations
Algorithm Design and Optimization
Statistical Analysis
Importance and Benefits of Set Operation ufuncs
Data Manipulation
Computational Efficiency
Conclusion
NumPy Example 1: Importing NumPy
NumPy Example 2: Creating an Array
NumPy Example 3: Array Indexing and Slicing
NumPy Example 4: Array Reshaping
NumPy Example 5: Generating Random Numbers
NumPy Example 6: Matrix Multiplication
NumPy Example 7: Element-Wise Multiplication
NumPy Example 8: Statistical Operations
NumPy Example 9: Broadcasting
NumPy Example 10: Masking and Filtering
NumPy Example 11: Array Concatenation
NumPy Example 12: Array Transposition
NumPy Example 13: Finding Unique Elements
NumPy Example 14: Element-wise Comparisons
NumPy Example 15: Linear Algebra - Eigenvalues and Eigenvectors
NumPy Example 16: Polynomial Operations
NumPy Example 17: Loading Data from a File
NumPy Example 18: Calculating Pearson Correlation Coefficient
NumPy Example 19: Reshaping and Broadcasting
NumPy Example 20: Polynomial Fit
NumPy Example 21: Stacking Arrays Vertically and Horizontally
NumPy Example 22: Finding Non-zero Elements
NumPy Example 23: Computing Matrix Determinant
NumPy Example 24: Finding the Cumulative Sum
NumPy Example 25: Reversing an Array
NumPy Example 26: Loading Data from a Text File
NumPy Example 27: Solving Linear Equations
NumPy Example 28: Finding Mean Along Axis
NumPy Example 29: Performing Matrix Inversion
NumPy Example 30: Creating a 3D Array
NumPy Example 31: Element-wise Absolute Values
NumPy Example 32: Creating a Diagonal Matrix
NumPy Example 33: Reshaping Arrays using Ravel
NumPy Example 34: Element-wise Exponential Function
NumPy Example 35: Calculating Mean Squared Error
NumPy Example 36: Checking for NaN Values
NumPy Example 37: Sorting Array Elements
NumPy Example 38: Finding Common Elements
NumPy Example 39: Array Broadcasting with Arithmetic Operations
NumPy Example 40: Extracting Diagonal Elements
NumPy Example 41: Element-wise Logarithm
NumPy Example 42: Repeating Array Elements
NumPy Example 43: Checking for Finite Values
NumPy Example 44: Element-wise Square Root
NumPy Example 45: Array Masking
NumPy Example 46: Creating a Meshgrid
NumPy Example 47: Computing Element-wise Power
NumPy Example 48: Finding Unique Counts
NumPy Example 49: Changing Data Type of an Array
NumPy Example 50: Finding Median of an Array
NumPy Example 51: Array Reshaping with newaxis
NumPy Example 52: Element-wise Trigonometric Functions
NumPy Example 53: Finding Unique Indices
NumPy Example 54: Array Rotation
NumPy Example 55: Sorting with Order Argument
NumPy Example 56: Reshaping with Resize Method
NumPy Example 57: Finding Common Indices
NumPy Example 58: Flattening a Multidimensional Array
NumPy Example 59: Finding Logarithmic Sum
NumPy Example 60: Rotating Array Elements
NumPy Example 61: Extracting Unique Rows
NumPy Example 62: Array Broadcasting with Universal Functions
NumPy Example 63: Computing Log Sum Exponential
NumPy Example 64: Finding the Mode
NumPy Example 65: Inverting a Boolean Array
NumPy Example 66: Cumulative Product
NumPy Example 67: Sparse Matrix Representation
NumPy Example 68: Padding Array with Zeros
NumPy Example 69: Creating a Custom Data Type
NumPy Example 70: Check If All Elements are True
NumPy Example 71: Creating an Identity Matrix
NumPy Example 72: Linearly Spaced Values
NumPy Example 73: Finding Element-wise Modulus
NumPy Example 74: Finding Determinant of a Matrix
NumPy Example 75: Reshaping Arrays using Ravel vs. Flatten
NumPy Example 76: Finding Cross Product
NumPy Example 77: Computing Element-wise Hyperbolic Functions
NumPy Example 78: Finding Rank of a Matrix
NumPy Example 79: Array Splitting by Indices
NumPy Example 80: Computing Median Along Axis
NumPy Example 81: Adding a Constant to an Array
NumPy Example 82: Creating a Zero Array with Same Shape
NumPy Example 83: Calculating Exponential Moving Average
NumPy Example 84: Generating Random Integers
NumPy Example 85: Element-wise Ceil and Floor Functions
NumPy Example 86: Creating a Meshgrid with Numpy Arrays
NumPy Example 87: Finding Unique Elements and Counts
NumPy Example 88: Computing Element-wise Square
NumPy Example 89: Finding Eigenvalues and Eigenvectors
NumPy Example 90: Generating Poisson Distributed Random Numbers
NumPy Example 91: Finding Exponents of an Array
NumPy Example 92: Creating an Array from a Function
NumPy Example 93: Finding Determinant of a Higher Dimension Matrix
NumPy Example 94: Sorting with Custom Comparator Function
NumPy Example 95: Creating a Sparse Identity Matrix
NumPy Example 96: Counting Non-zero Elements Along Axis
NumPy Example 97: Computing Element-wise Logarithm Base 10
NumPy Example 98: Finding LCM (Least Common Multiple)
NumPy Example 99: Extracting Unique Rows with Count
NumPy Example 100: Creating a Random Float Array