Data Cleaning Using Pandas And Pyspan
Published 10/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Master Data Cleaning with pandas and pyspan: Essential Techniques for Clean, Accurate, and Ready-to-Use Datasets
What you’ll learn
Recognize opportunities for data cleaning and prepare your dataset for the cleaning process.
Implement common data cleaning steps such as handle missing values and formatting date/time columns.
Understand and implement complex data cleaning tasks such as outlier removal and splitting/creating new columns.
Develop and apply custom data transformation techniques to standardize and enhance dataset quality.
Requirements
Basic understanding of Python programming.
Understanding of using basic libraries like Pandas.
A computer with internet access.
Description
Overview
Section 1: Introduction
Lecture 1 Introduction to Data Cleaning and Course Overview
Section 2: Applied Data Cleaning
Lecture 2 Exploring the Dataset and Writing Pseudocode
Lecture 4 Outlier Detection and Handling with pyspan
Lecture 5 Advanced pyspan Functions: format_dt and split_column
Lecture 6 Evaluating Data Cleaning: Before vs. After
Section 3: Conclusion
Lecture 7 Conclusion of the course
