Python for AI

2.

Essential Libraries for Data Science and AI

2.1.

NumPy for Numerical Computing

2.1.1.

2.1.1.1.

Understanding ndarray

2.1.1.2.

Array Properties

2.1.1.2.1.

shape Attribute

2.1.1.2.2.

dtype Attribute

2.1.1.2.3.

2.1.1.2.4.

2.1.1.3.

Array Memory Layout

2.1.2.

2.1.2.1.

Creating Arrays from Lists

2.1.2.2.

Creating Arrays from Tuples

2.1.2.3.

Array Creation Functions

2.1.2.3.1.

2.1.2.3.2.

2.1.2.3.3.

2.1.2.3.4.

2.1.2.3.5.

2.1.2.3.6.

2.1.2.3.7.

2.1.2.4.

Random Array Generation

2.1.2.5.

Array Initialization Patterns

2.1.3.

Array Indexing and Slicing

2.1.3.1.

2.1.3.2.

Negative Indexing

2.1.3.3.

2.1.3.4.

Multi-dimensional Indexing

2.1.3.5.

Boolean Indexing

2.1.3.6.

2.1.3.7.

2.1.4.

Array Operations

2.1.4.1.

Element-wise Operations

2.1.4.2.

Arithmetic Operations

2.1.4.3.

Comparison Operations

2.1.4.4.

Logical Operations

2.1.4.5.

Broadcasting Rules

2.1.4.6.

Broadcasting Examples

2.1.5.

Array Manipulation

2.1.5.1.

Reshaping Arrays

2.1.5.2.

Flattening Arrays

2.1.5.3.

Transposing Arrays

2.1.5.4.

Concatenating Arrays

2.1.5.5.

Splitting Arrays

2.1.5.6.

Stacking Arrays

2.1.6.

Mathematical Functions

2.1.6.1.

Universal Functions (ufuncs)

2.1.6.2.

Trigonometric Functions

2.1.6.3.

Exponential and Logarithmic Functions

2.1.6.4.

Statistical Functions

2.1.6.4.1.

2.1.6.4.2.

2.1.6.4.3.

2.1.6.4.4.

2.1.6.4.5.

min() and max()

2.1.6.4.6.

2.1.6.5.

Aggregation Functions

2.1.6.6.

Reduction Operations

2.1.7.

2.1.7.1.

Matrix Operations

2.1.7.2.

Matrix Multiplication

2.1.7.3.

2.1.7.4.

2.1.7.5.

Matrix Decomposition

2.1.7.6.

Eigenvalues and Eigenvectors

2.1.7.7.

Solving Linear Systems

2.1.8.

Advanced NumPy Features

2.1.8.1.

Structured Arrays

2.1.8.2.

Memory-mapped Files

2.1.8.3.

Performance Optimization

2.1.8.4.

Vectorization Techniques

2.2.

Pandas for Data Manipulation

2.2.1.

Core Data Structures

2.2.1.1.

2.2.1.1.1.

Creating Series

2.2.1.1.2.

Series Indexing

2.2.1.1.3.

Series Operations

2.2.1.1.4.

2.2.1.1.5.

Series Attributes

2.2.1.2.

2.2.1.2.1.

Creating DataFrames

2.2.1.2.2.

DataFrame Structure

2.2.1.2.3.

Column Operations

2.2.1.2.4.

2.2.1.2.5.

DataFrame Attributes

2.2.1.3.

2.2.1.3.1.

2.2.1.3.2.

Index Operations

2.2.1.3.3.

2.2.2.

Data Input and Output

2.2.2.1.

2.2.2.1.1.

2.2.2.1.2.

2.2.2.1.3.

2.2.2.1.4.

2.2.2.1.5.

2.2.2.1.6.

2.2.2.2.

2.2.2.2.1.

2.2.2.2.2.

2.2.2.2.3.

2.2.2.2.4.

SQL Database Output

2.2.2.3.

Data Format Conversion

2.2.3.

Data Selection and Filtering

2.2.3.1.

Column Selection

2.2.3.2.

2.2.3.3.

Label-based Selection (loc)

2.2.3.4.

Position-based Selection (iloc)

2.2.3.5.

Boolean Indexing

2.2.3.6.

2.2.3.7.

Conditional Selection

2.2.3.8.

Multi-level Selection

2.2.4.

Data Cleaning and Preparation

2.2.4.1.

Missing Data Handling

2.2.4.1.1.

Detecting Missing Values

2.2.4.1.2.

Filling Missing Values

2.2.4.1.3.

Dropping Missing Values

2.2.4.1.4.

Interpolation Methods

2.2.4.2.

2.2.4.2.1.

Identifying Duplicates

2.2.4.2.2.

Removing Duplicates

2.2.4.2.3.

Duplicate Handling Strategies

2.2.4.3.

Data Type Conversion

2.2.4.4.

String Data Cleaning

2.2.4.5.

Outlier Detection and Treatment

2.2.5.

Data Transformation

2.2.5.1.

Applying Functions

2.2.5.1.1.

2.2.5.1.2.

2.2.5.1.3.

applymap() Method

2.2.5.2.

Data Aggregation

2.2.5.2.1.

groupby() Operations

2.2.5.2.2.

Aggregation Functions

2.2.5.2.3.

Custom Aggregations

2.2.5.2.4.

Multiple Aggregations

2.2.5.3.

2.2.5.3.1.

2.2.5.3.2.

2.2.5.3.3.

Stacking and Unstacking

2.2.5.4.

Data Merging and Joining

2.2.5.4.1.

merge() Function

2.2.5.4.2.

2.2.5.4.3.

concat() Function

2.2.5.4.4.

2.2.6.

Time Series Analysis

2.2.6.1.

DateTime Data Types

2.2.6.2.

DateTime Indexing

2.2.6.3.

Time Series Operations

2.2.6.4.

2.2.6.5.

Frequency Conversion

2.2.6.6.

Rolling Windows

2.2.6.7.

Expanding Windows

2.2.6.8.

Time Zone Handling

2.2.7.

Advanced Pandas Features

2.2.7.1.

Categorical Data

2.2.7.2.

2.2.7.3.

Performance Optimization

2.2.7.4.

Memory Usage Optimization

2.2.7.5.

Parallel Processing

2.3.

Matplotlib for Data Visualization

2.3.1.

Matplotlib Basics

2.3.1.1.

Figure and Axes Objects

2.3.1.2.

Plotting Interface

2.3.1.3.

Object-oriented Interface

2.3.1.4.

Pyplot Interface

2.3.2.

2.3.2.1.

2.3.2.2.

2.3.2.3.

2.3.2.4.

2.3.2.5.

2.3.2.6.

2.3.3.

Plot Customization

2.3.3.1.

Colors and Styles

2.3.3.2.

Markers and Line Styles

2.3.3.3.

Labels and Titles

2.3.3.4.

2.3.3.5.

2.3.3.6.

2.3.4.

Axes and Layout

2.3.4.1.

Multiple Subplots

2.3.4.2.

Subplot Arrangements

2.3.4.3.

2.3.4.4.

Axis Limits and Scaling

2.3.4.5.

Ticks and Tick Labels

2.3.4.6.

2.3.5.

Advanced Plotting Features

2.3.5.1.

2.3.5.2.

2.3.5.3.

2.3.5.4.

2.3.5.5.

2.3.5.6.

2.3.6.

Saving and Exporting

2.3.6.1.

2.3.6.2.

Resolution Settings

2.3.6.3.

Figure Size Control

2.4.

Seaborn for Statistical Visualization

2.4.1.

2.4.1.1.

Seaborn vs Matplotlib

2.4.1.2.

Setting Styles and Themes

2.4.1.3.

2.4.2.

Statistical Plots

2.4.2.1.

Distribution Plots

2.4.2.2.

Regression Plots

2.4.2.3.

Categorical Plots

2.4.2.4.

2.4.3.

Multi-plot Grids

2.4.3.1.

2.4.3.2.

2.4.3.3.

2.4.4.

Advanced Seaborn Features

2.4.4.1.

Custom Color Maps

2.4.4.2.

Statistical Annotations

2.4.4.3.

Plot Aesthetics

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