Clean your data with these 8 python libraries

In this video, we dive deep into the world of data cleaning with Python libraries. Clean data is crucial for accurate data analysis and powerful machine-learning models.

We'll explore 8 essential libraries that'll help you to clean up your data:

◉ Pandas: The data manipulation powerhouse for wrangling tabular data.
◉ NumPy: The foundation for numerical computations and array operations.
◉ SciPy: Extends NumPy's functionalities for advanced data cleaning tasks. 🪄
◉ PyJanitor: Streamlines data cleaning workflows built on top of Pandas. ⚡
◉ Dataprep: Accelerates data preparation with cleaning, wrangling, and exploration tools. ️
◉ Great Expectations: Ensures data quality through validation, testing, and documentation. ️
◉ Pandera: Maintains data integrity and consistency with data schemas and validation.
◉ Dask: Scales data cleaning tasks for massive datasets using parallel computing.

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Timeline:

Intro: (0:00​​​)
Why data cleaning is so important: (0:22)
Pandas: (0:44)
NumPy: (1:56)
SciPy: (3:07)
PyJanitor: (4:29)
Dataprep: (5:30)
Great Expectations: (6:12)
Pandera: (6:49)
Dask: (7:27)
Conclusion: (8:20​​)

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#DataCleanUp #datacleaning #datascience #python #dataanalysis #machinelearning #pythonlibraries #dataanalytics

[Maksilver]

Great video mate !! I found education, resources and entertainment in one go

[ambaigbaig]

As a Python developer this video and them libraries are fire, Thanks for sharing this info🤗🤗🤗 much appreciated