| 01-ReportLab-Groundwork.ipynb | 43.2 KB | ||
| 02-Updating-the-Report.ipynb | 38.7 KB | ||
| 03-Automation.ipynb | 15 KB | ||
| 1. Introduction.mp4 | 6.7 MB | ||
| 1. Why You Should Consider Automating the Process.mp4 | 12.6 MB | ||
| 1.1 01-ReportLab-Groundwork.ipynb | 43.2 KB | ||
| 1.1 02-Updating-the-Report.ipynb | 38.7 KB | ||
| 1.1 03-Automation.ipynb | 15 KB | ||
| 1.1 notes.pdf | 2.4 MB | ||
| 1.2 americanHFP.csv | 204.8 B | ||
| 1.3 generalMills.csv | 1.7 KB | ||
| 1.4 kelloggs.csv | 1.7 KB | ||
| 1.5 nabisco.csv | 512 B | ||
| 1.6 post.csv | 819.2 B | ||
| 1.7 quakerOats.csv | 716.8 B | ||
| 1.8 ralstonPurina.csv | 716.8 B | ||
| 10. Improving the Print in the PDF Document.mp4 | 27.8 MB | ||
| 11. Overview.mp4 | 20.3 MB | ||
| 2. Building the Automation.mp4 | 51.6 MB | ||
| 2. Information on Resources.html | 1.3 KB | ||
| 2. Introducing Additional Page Templates.mp4 | 18.6 MB | ||
| 2. The Canvas Object of PDFgen.mp4 | 34.9 MB | ||
| 2. The Cereal Dataset.mp4 | 15 MB | ||
| 2. The Matplotlib Figure Object.mp4 | 3.4 MB | ||
| 2.1 cereal.csv | 4.9 KB | ||
| 3. Dataset Import.mp4 | 19.2 MB | ||
| 3. Farewell.mp4 | 2.8 MB | ||
| 3. Regular Pyplot Charts and the Pandas Plotting System.mp4 | 27.9 MB | ||
| 3. ReportLab Anatomy - The Hierarchy of Document Building Blocks.mp4 | 20.5 MB | ||
| 3. Updating Flowable Style Objects.mp4 | 22.9 MB | ||
| 3. Virtual Environment Setup.mp4 | 25.1 MB | ||
| 4. Building Documents with PLATYPUS - Create Your Own Page and Document Templates.mp4 | 51.4 MB | ||
| 4. Data Type Management with NumPy and alternatives with PyArrow.mp4 | 25 MB | ||
| 4. Exporting Results of Custom Data Visualization Functions.mp4 | 32.4 MB | ||
| 4. Installing Python Libraries.mp4 | 21.9 MB | ||
| 4. Reorganizing Data Tables and Paragraphs.mp4 | 41 MB | ||
| 4.1 requirements.txt | 102.4 B | ||
| 5. Handling Multiple Data Visualizations in a Single Document.mp4 | 14.9 MB | ||
| 5. Identifying and Handling Invalid Observations.mp4 | 23 MB | ||
| 5. Include Data Visualizations in Your Reports with Figure to Image Conversion.mp4 | 29 MB | ||
| 5. Including a Subplot Grid in a Report.mp4 | 23.1 MB | ||
| 6. Add Tables to Your Report Easily with DataFrame to Table Conversion.mp4 | 16.8 MB | ||
| 6. Equalizing Nutritional Values on the Basis of Weight.mp4 | 30.9 MB | ||
| 6. Pre-Defined Layouts with the Help of Container Tables.mp4 | 32.4 MB | ||
| 6. Various Types of Plots Created with Seaborn - Plot Matrix and Facet Grid Plot.mp4 | 23.9 MB | ||
| 7. Building the PLATYPUS Story.mp4 | 47.4 MB | ||
| 7. Extending the Analysis with Nutritional Test.mp4 | 37.1 MB | ||
| 7. Overview.mp4 | 9.3 MB | ||
| 7. Styling Your Paragraphs and Tables with Dedicated Style Objects.mp4 | 32.2 MB | ||
| 8. Declaring Variable Units.mp4 | 18.4 MB | ||
| 8. Overview.mp4 | 12.2 MB | ||
| 9. Auxiliary Tables.mp4 | 13.1 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| READ_ME.txt | 1.3 KB | ||
| americanHFP.csv | 204.8 B | ||
| cereal.csv | 4.9 KB | ||
| generalMills.csv | 1.7 KB | ||
| kelloggs.csv | 1.7 KB | ||
| nabisco.csv | 512 B | ||
| notes.pdf | 2.4 MB | ||
| post.csv | 819.2 B | ||
| python_logo_icon.png | 9.2 KB | ||
| quakerOats.csv | 716.8 B | ||
| ralstonPurina.csv | 716.8 B | ||
| requirements.txt | 102.4 B | ||
| ▲ 70 total files | |||
Python and ReportLab for Efficient Reporting and Automation
https://DevCourseWeb.com
Published 4/2024
Created by R-Tutorials Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 40 Lectures ( 2h 15m ) | Size: 866 MB
Use Python, ReportLab and data visualization packages like Seaborn and Matplotlib to generate automated reports
What you'll learn:
Understand how Python can help you in data reporting
Learn how to speed up the data reporting process
Write Python code to automate PDF report generation
Understand the ReportLab (Python library) document building system
Document data analysis progress and data visualizations in a PDF report
Plan out document templates, layouts and styles
Requirements:
General understanding of data visualization
Basic Python skills (dictionaries. lists, loops)
Know how to install Python packages
Interest in analytics
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