This course teaches learners how to use the pandas-profiling library in Python to generate detailed reports about datasets. By the end of the course, students will be able to create visually appealing and interactive reports that include information on interactions, correlations, histograms, missing values, and duplicate values in their datasets. The course covers installation, usage with a sample dataset, customization options, and exporting reports. The intended audience for this course is Python pandas library users with experience in processing datasets who want to streamline the process of collecting and analyzing dataset-specific details.
Python Pandas Profiling - Why Data Profiling Is Important for You
Overview
Syllabus
- Video Start
- Video Content Intro
- Code & Jupyter Notebook Introduction
- Library Installation
- Pandas-profiling Library Introduction
- Demo with Titanic Dataset
- Demo with Titanic Dataset - Interactions
- Demo with Titanic Dataset - Correlations
- Demo with Titanic Dataset - Missing Values
- Saving HTML Report
- Profiling large dataset
- Minimal Profiling Reporting Setting
- Demo with Titanic Dataset - Config Metadata
- Demo with Titanic Dataset - Config Details
- Demo with Titanic Dataset - Config Param
- Demo with Titanic Dataset - View Widgets
- Demo with Titanic Dataset - Histogram Config
- Streamlit Application with Profiling Report
- Recap
- Credits
Taught by
Prodramp
