Uploaded July 2024 | Updated September 2026, 1 day ago
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.
Want more data science resources? Visit stratascratch.com
_____________________________________________________________________
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+python+libraries+to+clean+data
______________________________________________________________________
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)
______________________________________________________________________
About The StrataScratch Platform:
StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+python+libraries+to+clean+data) is a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+python+libraries+to+clean+data. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from the StrataScratch team, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email us at team@stratascratch.com
______________________________________________________________________
#DataCleanUp #datacleaning #datascience #python #dataanalysis #machinelearning #pythonlibraries #dataanalytics
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.
Want more data science resources? Visit stratascratch.com
_____________________________________________________________________
👉 Subscribe to my channel: bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: bit.ly/3jifw81
👉 Playlist for data science interview tips: bit.ly/2G5hNoJ
👉 Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
👉 Practice more real data science interview questions: platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+python+libraries+to+clean+data
______________________________________________________________________
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)
______________________________________________________________________
About The StrataScratch Platform:
StrataScratch (platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+python+libraries+to+clean+data) is a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+python+libraries+to+clean+data. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from the StrataScratch team, you can use ss15 for a 15% discount on the premium plans.
______________________________________________________________________
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email us at team@stratascratch.com
______________________________________________________________________
#DataCleanUp #datacleaning #datascience #python #dataanalysis #machinelearning #pythonlibraries #dataanalytics
![Facebooks Most Common Data Science SQL Interview Question [2021 Interview Question and Answer]](https://i.ytimg.com/vi/XRwxYOhHdE8/mqdefault.jpg "Facebooks Most Common Data Science SQL Interview Question [2021 Interview Question and Answer]
This is the most common data science interview question from Facebook. It’s the most commonly tested concept on Facebooks coding interviews, especially in the beginning rounds.
In the previous video, we talked about 5 coding concepts that companies test for in 2021 so now well actually solve the 5 questions that test your understanding of these concepts. If you havent watched that video, heres the link to the video - https://bit.ly/3ek04by
Link to the question: https://platform.stratascratch.com/coding/2005-share-of-active-users?python=&utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link
Timeline:
Intro: (0:00)
Interview Question: (0:50)
Solution Approach: (1:50)
Explore data: (2:28)
Identify the required columns: (3:40)
Visualize output: (4:14)
Code in increments: (5:14)
Conclusion: (10:16)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+description+link. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#SQL #DataScienceInterview")
![Exploratory Data Analysis For An Uber Python Data Science Project [Part 1]](https://i.ytimg.com/vi/ZOthTms86Bw/mqdefault.jpg "Exploratory Data Analysis For An Uber Python Data Science Project [Part 1]
In this video, well take you through a data project from Uber called Partner Business Modeling. Well delve into the details of exploring and cleaning up the dataset. Well use popular Python libraries such as Pandas, NumPy, and Matplotlib to manipulate, analyze and visualize the data. And showcase how custom functions can be used to create bar graphs and how to clean and change rows using the apply method.
Watch the next parts:
📌 Part 2: Using Python Pandas To Answer Business Questions For An Uber Data Science Project: https://www.youtube.com/watch?v Lzi4Qoy2v4
📌 Part 3: Data Science and Uber: Solving Partner Business Modeling using Python: https://youtu.be/xlppLbv7nb0
🧑💻 Go to the question through the link below and follow along with us:
https://platform.stratascratch.com/data-projects/partner-business-modeling/?utm_source=youtube&utm_medium=click&utm_campaign=YT+data+preparation+uber+project
👉 Subscribe to my channel: https://bit.ly/2GsFxmA
👉 Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
👉 Playlist for data science interview tips: https://bit.ly/2G5hNoJ
👉 Practice more real data science interview questions: https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+data+preparation+uber+project
Timeline:
Intro: (0:00)
Data exploration and cleaning: (0:37)
Coding the solution: (6:49)
Conclusion: (8:57)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+data+preparation+uber+project), a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and python), statistics, probability, product sense, and business cases.
So, if you want more interview practice with real data science interview questions, visit https://platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+data+preparation+uber+project. All questions are free and you can even execute SQL and python code in the IDE, but if you want to check out the solutions from me or from other users, you can use ss15 for a 15% discount on the premium plans.
Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
#PythonDataProject #PythonInterviewQuestion")
