Data Visualization with Python

Data Visualization with Python is the practice of creating graphical representations of information and data using the Python programming language. It leverages a rich ecosystem of powerful libraries—such as Matplotlib for foundational plotting, Seaborn for attractive statistical graphics, and Plotly for interactive, web-ready charts—to transform complex datasets into intuitive and insightful visuals like bar charts, line graphs, scatter plots, and heatmaps. This process is crucial for data scientists and analysts to explore data, identify trends, uncover patterns, and effectively communicate their findings to both technical and non-technical audiences.

  1. Introduction to Data Visualization
    1. The Role of Visualization in Data Analysis
      1. Communicating Insights
        1. Supporting Decision Making
          1. Exploratory Data Analysis
          2. Core Principles of Effective Visualization
            1. Clarity and Simplicity
              1. Reducing Visual Clutter
                1. Focusing on Key Messages
                  1. Information Hierarchy
                  2. Accuracy and Integrity
                    1. Avoiding Distortion of Data
                      1. Representing Data Proportionally
                        1. Maintaining Context
                        2. Choosing the Right Chart Type
                          1. Matching Chart Types to Data Types
                            1. Understanding Data Relationships
                              1. Common Chart Types and Their Uses
                                1. Avoiding Chart Misuse
                              2. The Python Visualization Ecosystem
                                1. Overview of Key Libraries
                                  1. Matplotlib
                                    1. Seaborn
                                      1. Plotly
                                        1. Bokeh
                                          1. Altair
                                            1. GeoPandas
                                              1. Folium
                                                1. NetworkX
                                                  1. Datashader
                                                    1. HoloViews
                                                    2. Declarative vs Imperative Plotting
                                                      1. Characteristics of Declarative Plotting
                                                        1. Characteristics of Imperative Plotting
                                                          1. Use Cases and Examples
                                                          2. Library Selection Criteria
                                                            1. Static vs Interactive Requirements
                                                              1. Performance Considerations
                                                                1. Learning Curve Assessment

                                                            Go to top

                                                            Next

                                                            2. Setting Up the Python Environment