Python and ReportLab for Efficient Reporting and Automation
Python and ReportLab for Efficient Reporting and Automation, available at $54.99, has an average rating of 4.88, with 41 lectures, based on 4 reviews, and has 62 subscribers.
You will learn about 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 This course is ideal for individuals who are Data Analysts or Everybody looking to use Python to automate the reporting process or Analysts interested in new ways to improve and speed up their reporting skills or Students and graduates with a data focused background It is particularly useful for Data Analysts or Everybody looking to use Python to automate the reporting process or Analysts interested in new ways to improve and speed up their reporting skills or Students and graduates with a data focused background.
Enroll now: Python and ReportLab for Efficient Reporting and Automation
Summary
Title: Python and ReportLab for Efficient Reporting and Automation
Price: $54.99
Average Rating: 4.88
Number of Lectures: 41
Number of Published Lectures: 41
Number of Curriculum Items: 41
Number of Published Curriculum Objects: 41
Original Price: $69.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Data Analysts
- Everybody looking to use Python to automate the reporting process
- Analysts interested in new ways to improve and speed up their reporting skills
- Students and graduates with a data focused background
Target Audiences
- Data Analysts
- Everybody looking to use Python to automate the reporting process
- Analysts interested in new ways to improve and speed up their reporting skills
- Students and graduates with a data focused background
Creating reports is a standard task in the modern working environment. Pretty much every office worker has to do it from time to time, some of us even daily.
Therefore it makes perfect sense to be an expert at it. This can save you a lot of time, make your manager happy and you can be of great help to your colleagues.
Therefore I will demonstrate in this course how you can use Python and the main package ReportLab to easily create reports and to even automate the process for fast reporting of multiple similar data files.
Our target output file type will be the pdf which anybody of us knows and uses regularly.
The portable document format, or PDF, is the standard for document sharing since decades. Its cross platform compatibility, the ease of printing and the variety of written and visual content it can handle, make the pdf one of the most important document formats.
Reading a PDF is easy as it gets, however, generating a PDF document can get complicated. Text editing software and many interactive apps are able to generate PDFs – data analysis software usually makes great use of this feature too. On the user’s side, PDF generation is just a matter of some mouse clicks. However, in the background the PDF document is written in the PostScript language.
If you are working on your own applications, or if you create a data analysis with a programming language such as Python, then figuring out PDF generation is not as straight forward. You would need an interpreter which translates your code to PostScript and then a PDF document is generated. Sometimes, this process relies on additional software.
If you use python the ReportLab package could be the right tool for you. ReportLab lets you directly create documents in PDF format without any intervening steps. This means that your applications can generate reports very fast, sometimes much faster than stand alone report writing software. A great advantage, especially when you want to automate the process. Besides text, ReportLab also handles charts, graphs, data tables, model outputs – basically anything you can produce with python.
In order to follow along with this course the only skill you need is some beginner level python. So if you know how to install and import packages, handle lists, and how to write simple loops and functions, then you will have no problem keeping up with the course.
Do not worry if your understanding of python is still not at its fullest – I will make an effort in guiding you through the lectures step by step from setting up your working environment, performing a simple data analysis and writing the code for the actual PDF report generation and automation.
Alright I hope you will take this chance to bring your reporting skills to the next level!
Course Curriculum
Chapter 1: Workspace Setup
Lecture 1: Introduction
Lecture 2: Information on Resources
Lecture 3: Virtual Environment Setup
Lecture 4: Installing Python Libraries
Chapter 2: ReportLab Groundwork
Lecture 1: Introduction
Lecture 2: The Canvas Object of PDFgen
Lecture 3: ReportLab Anatomy – The Hierarchy of Document Building Blocks
Lecture 4: Building Documents with PLATYPUS – Create Your Own Page and Document Templates
Lecture 5: Include Data Visualizations in Your Reports with Figure to Image Conversion
Lecture 6: Add Tables to Your Report Easily with DataFrame to Table Conversion
Lecture 7: Styling Your Paragraphs and Tables with Dedicated Style Objects
Lecture 8: Overview
Chapter 3: Document Data Analysis Progress with ReportLab
Lecture 1: Introduction
Lecture 2: The Cereal Dataset
Lecture 3: Dataset Import
Lecture 4: Data Type Management with NumPy and alternatives with PyArrow
Lecture 5: Identifying and Handling Invalid Observations
Lecture 6: Equalizing Nutritional Values on the Basis of Weight
Lecture 7: Extending the Analysis with Nutritional Test
Lecture 8: Declaring Variable Units
Lecture 9: Auxiliary Tables
Lecture 10: Improving the Print in the PDF Document
Lecture 11: Overview
Chapter 4: Including Data Visualizations in Your Reports
Lecture 1: Introduction
Lecture 2: The Matplotlib Figure Object
Lecture 3: Regular Pyplot Charts and the Pandas Plotting System
Lecture 4: Exporting Results of Custom Data Visualization Functions
Lecture 5: Including a Subplot Grid in a Report
Lecture 6: Various Types of Plots Created with Seaborn – Plot Matrix and Facet Grid Plot
Lecture 7: Overview
Chapter 5: Updating and Extending the Report
Lecture 1: Introduction
Lecture 2: Introducing Additional Page Templates
Lecture 3: Updating Flowable Style Objects
Lecture 4: Reorganizing Data Tables and Paragraphs
Lecture 5: Handling Multiple Data Visualizations in a Single Document
Lecture 6: Pre-Defined Layouts with the Help of Container Tables
Lecture 7: Building the PLATYPUS Story
Chapter 6: Automating the Report Building Process
Lecture 1: Why You Should Consider Automating the Process
Lecture 2: Building the Automation
Lecture 3: Assignment: Build Your Own PDF Automation from JSON
Lecture 4: Farewell
Instructors
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R-Tutorials Training
Data Science Education
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- 4 stars: 1 votes
- 5 stars: 3 votes
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