Data Visualization with Python

2.

Setting Up the Python Environment

2.1.

Installing Python and Package Managers

2.1.1.

Python Installation Options

2.1.1.1.

Official Python Distribution

2.1.1.2.

Anaconda Distribution

2.1.1.3.

2.1.2.

Package Management

2.1.2.1.

Conda Package Manager

2.1.2.2.

Pip Package Manager

2.1.2.3.

Virtual Environment Management

2.1.3.

Environment Configuration

2.1.3.1.

Creating Project Environments

2.1.3.2.

Managing Dependencies

2.1.3.3.

Environment Activation and Deactivation

2.2.

Development Environments

2.2.1.

Jupyter Ecosystem

2.2.1.1.

Jupyter Notebook

2.2.1.2.

2.2.1.3.

Jupyter Extensions

2.2.1.4.

Magic Commands for Visualization

2.2.2.

Integrated Development Environments

2.2.2.1.

VS Code with Python Extensions

2.2.2.2.

2.2.2.3.

2.2.3.

Cloud-Based Platforms

2.2.3.1.

2.2.3.2.

Kaggle Notebooks

2.2.3.3.

Azure Notebooks

2.3.

Essential Libraries for Data Handling

2.3.1.

NumPy for Numerical Operations

2.3.1.1.

Array Creation and Manipulation

2.3.1.2.

Mathematical Operations

2.3.1.3.

2.3.1.4.

Random Number Generation

2.3.2.

Pandas for Data Manipulation

2.3.2.1.

DataFrame and Series Objects

2.3.2.1.1.

Creating DataFrames and Series

2.3.2.1.2.

Indexing and Selection

2.3.2.1.3.

Data Types and Conversion

2.3.2.2.

Data Import and Export

2.3.2.2.1.

Reading CSV Files

2.3.2.2.2.

Reading Excel Files

2.3.2.2.3.

Reading JSON Files

2.3.2.2.4.

Database Connections

2.3.2.2.5.

Web Scraping Integration

2.3.2.3.

Data Cleaning and Transformation

2.3.2.3.1.

Handling Missing Data

2.3.2.3.2.

Data Type Conversion

2.3.2.3.3.

String Operations

2.3.2.3.4.

Date and Time Handling

2.3.2.3.5.

Filtering and Sorting

2.3.2.3.6.

Grouping and Aggregation

2.3.2.3.7.

Merging and Joining

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3. Foundational Plotting with Matplotlib