Python 和 ReportLab 实现高效报告和自动化
Python and ReportLab for Efficient Reporting and Automation
- 1. Workspace Setup
- 1. Introduction
- 3. Virtual Environment Setup
- 4. Installing Python Libraries
- 2. ReportLab Groundwork
- 1. Introduction
- 2. The Canvas Object of PDFgen
- 3. ReportLab Anatomy - The Hierarchy of Document Building Blocks
- 4. Building Documents with PLATYPUS - Create Your Own Page and Document Templates
- 5. Include Data Visualizations in Your Reports with Figure to Image Conversion
- 6. Add Tables to Your Report Easily with DataFrame to Table Conversion
- 7. Styling Your Paragraphs and Tables with Dedicated Style Objects
- 8. Overview
- 3. Document Data Analysis Progress with ReportLab
- 1. Introduction
- 2. The Cereal Dataset
- 3. Dataset Import
- 4. Data Type Management with NumPy and alternatives with PyArrow
- 5. Identifying and Handling Invalid Observations
- 6. Equalizing Nutritional Values on the Basis of Weight
- 7. Extending the Analysis with Nutritional Test
- 8. Declaring Variable Units
- 9. Auxiliary Tables
- 10. Improving the Print in the PDF Document
- 11. Overview
- 4. Including Data Visualizations in Your Reports
- 1. Introduction
- 2. The Matplotlib Figure Object
- 3. Regular Pyplot Charts and the Pandas Plotting System
- 4. Exporting Results of Custom Data Visualization Functions
- 5. Including a Subplot Grid in a Report
- 6. Various Types of Plots Created with Seaborn - Plot Matrix and Facet Grid Plot
- 7. Overview
- 5. Updating and Extending the Report
- 1. Introduction
- 2. Introducing Additional Page Templates
- 3. Updating Flowable Style Objects
- 4. Reorganizing Data Tables and Paragraphs
- 5. Handling Multiple Data Visualizations in a Single Document
- 6. Pre-Defined Layouts with the Help of Container Tables
- 7. Building the PLATYPUS Story
- 6. Automating the Report Building Process
- 1. Why You Should Consider Automating the Process
- 2. Building the Automation
- 3. Farewell