Uploaded April 2023 | Updated September 2026, 1 hour ago
In this video, we'll overview the rank() function, including how it works by default, different methods for ranking, and how to handle ties and decimal places. We also explore options for ensuring consecutive ranks with no gaps.
Watch the full tutorial:
π How to Rank() Your Data In Python Pandas: youtu.be/2uu0prXMhfU
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π Subscribe to my channel: bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: bit.ly/3jifw81
π Playlist for data science interview tips: bit.ly/2G5hNoJ
π Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
π Practice more real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+shorts+python+ranking
______________________________________________________________________
About The Platform:
I'm using StrataScratch (platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+shorts+python+ranking), 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?utm_source=youtube&utm_medium=click&utm_campaign=YT+shorts+python+ranking. 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.
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Contact:
If you have any questions, comments, or feedback, please leave them here!
Feel free to also email me at nathan@stratascratch.com
_______________________________________________________________________
#pandaspythontutorial #pythondatascience #pythoninterviewquestion
In this video, we'll overview the rank() function, including how it works by default, different methods for ranking, and how to handle ties and decimal places. We also explore options for ensuring consecutive ranks with no gaps.
Watch the full tutorial:
π How to Rank() Your Data In Python Pandas: youtu.be/2uu0prXMhfU
______________________________________________________________________
π Subscribe to my channel: bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: bit.ly/3jifw81
π Playlist for data science interview tips: bit.ly/2G5hNoJ
π Playlist for data science projects: bit.ly/StrataScratchProjectsYouTube
π Practice more real data science interview questions: platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+shorts+python+ranking
______________________________________________________________________
About The Platform:
I'm using StrataScratch (platform.stratascratch.com/coding?utm_source=youtube&utm_medium=click&utm_campaign=YT+shorts+python+ranking), 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?utm_source=youtube&utm_medium=click&utm_campaign=YT+shorts+python+ranking. 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
_______________________________________________________________________
#pandaspythontutorial #pythondatascience #pythoninterviewquestion
![[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings
Welcome back to our data project series on Rotten Tomatoes! In this video, we dive into the decision tree classifier, a popular machine learning algorithm used for classification tasks. Watch as we create and train a decision tree classifier with a maximum of three leaf nodes, and evaluate its performance on test data. We assess accuracy, precision, and recall values, and visualize the decision workflow using a plot tree method. We also compare the results with a random forest classifier and explore feature importance. Dont forget to subscribe and stay tuned for the next video in our series!
Watch the full tutorial:
π [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: https://youtu.be/cM12QtrhdLo
π [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings:
https://youtu.be/J4rheMtvu6g
Go to the project through the link below and follow along with meπ
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2
π Subscribe to my channel: https://bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
π Playlist for data science interview tips: https://bit.ly/2G5hNoJ
π Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
π Practice more real data science interview questions: https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2
Timeline:
Intro: (0:00βββ)
Decision tree classifier: (0:16)
Instantiating the decision tree classifier: (1:44)
Visualizing the decision workflow: (3:00)
Accuracy score improving: (5:30)
Random forest classifier: (7:46)
Take away: (10:25ββ)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2), 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?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+2. 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 me, 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
#datascienceproject [Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings](https://i.ytimg.com/vi/Ih6G5hnn30Q/mqdefault.jpg)
![[Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings
Welcome back to our data project series from Rotten Tomatoes! In this video, we continue our journey to enhance our machine-learning models. We explore the power of feature selection and weighted random forest classifier to improve performance. Join us in the next video for more exciting insights. See you there!
Watch the previous parts:
π [Part 1]: Exploring and Visualizing Data For A Facebook Data Science Project of Movie Ratings: https://youtu.be/cM12QtrhdLo
π[Part 2]: Exploring Decision Tree Classifiers For A Facebook Data Science Project of Movie Ratings: https://youtu.be/Ih6G5hnn30Q
Go to the project through the link below and follow along with meπ
https://platform.stratascratch.com/data-projects/rotten-tomatoes-movies-rating-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3
π Subscribe to my channel: https://bit.ly/2GsFxmA
π Playlist for more data science interview questions and answers: https://bit.ly/3jifw81
π Playlist for data science interview tips: https://bit.ly/2G5hNoJ
π Playlist for data science projects: https://bit.ly/StrataScratchProjectsYouTube
π Practice more real data science interview questions: https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3
Timeline:
Intro: (0:00βββ)
Splitting the Data: (0:58)
Feature Selection: (1:26)
Weighted Random Forest Classifier: (2:47)
Smote technique: (3:41)
Take away: (5:18ββ)
About The Platform:
Im using StrataScratch (https://platform.stratascratch.com/coding?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3), 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?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+rotten+tomatoes+3. 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 me, 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
#datascienceproject [Part 3]: Random Forest Classifier For A Facebook Data Science Project of Movie Ratings](https://i.ytimg.com/vi/J4rheMtvu6g/mqdefault.jpg)








