| 1 -Conclusion of the course.mp4 | 3.2 MB | ||
| 1 -Exploring the Dataset and Writing Pseudocode.mp4 | 86.6 MB | ||
| 1 -FIFA 21 Messy Raw Dataset.url | 102.4 B | ||
| 1 -Google Colab Notebook.url | 102.4 B | ||
| 1 -Introduction to Data Cleaning and Course Overview.mp4 | 13.2 MB | ||
| 2 -Getting Started with pyspan remove and handle_nulls.mp4 | 67.1 MB | ||
| 3 -Outlier Detection and Handling with pyspan.mp4 | 36.7 MB | ||
| 4 -Advanced pyspan Functions format_dt and split_column.mp4 | 108.9 MB | ||
| 5 -Evaluating Data Cleaning Before vs. After.mp4 | 41.6 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B |
Data Cleaning using pandas and pyspan
https://DevCourseWeb.com
Published 10/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 57m | Size: 357 MB
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.
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