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ChawlaAvi / repository
A collection of code snippets from the publication Daily Dose of Data Science on Substack: http://www.dailydoseofds.com/
Daily Dose of Data Science is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless.
This repository is a collection of all the code snippets presented in my publication. If you want to receive these tips in your mailbox daily, you can subscribe to my Substack newsletter.
To download the tips listed here, you can clone this repo.
git clone https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science
| Title | Notebook | Substack | Article |
|---|---|---|---|
| One-Minute Guide To Becoming a Polars-savvy Data Scientist | 🔗 | 🔗 | |
| Avoid Using Pandas' Apply() Method At All Times | 🔗 | 🔗 | |
| Pandas vs Polars — Run-time and Memory Comparison | 🔗 | 🔗 | |
| A Lesser-Known Feature of the Merge Method in Pandas | 🔗 | 🔗 | |
| A Highly Overlooked Approach To Analysing Pandas DataFrames | 🔗 | 🔗 | |
| The Most Common Misconception About Inplace Operations in Pandas | 🔗 | 🔗 |
Selected from shared topics, language and repository description—not editorial ratings.
Olow304 /
The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.
cedrickchee /
Data science Python notebooks—a collection of Jupyter notebooks on machine learning, deep learning, statistical inference, data analysis and visualization.
| Become A Bilingual Data Scientist With These Pandas to SQL Translations | 🔗 | 🔗 |
| Avoid This Costly Mistake When Indexing A DataFrame | 🔗 | 🔗 |
| AutoProfiler: Automatically Profile Your DataFrame As You Work | 🔗 | 🔗 |
| Why You Should Avoid Appending Rows To A DataFrame | 🔗 | 🔗 |
| Are You Sure You Are Using The Correct Pandas Terminologies? | 🔗 | 🔗 |
| If You Are Not Able To Code A Vectorized Approach, Try This. | 🔗 | 🔗 |
| Why Are We Typically Advised To Never Iterate Over A DataFrame? | 🔗 | 🔗 |
| PyGWalker: Analyze Pandas Dataframe in Jupyter using a Tableau-style Interface | 🔗 | 🔗 |
| A Simple Trick to Make The Most Out of Pivot Tables in Pandas | 🔗 | 🔗 |
| Never Worry About Parsing Errors Again While Reading CSV with Pandas | 🔗 | 🔗 |
| An Interesting and Lesser-Known Way To Create Plots Using Pandas | 🔗 | 🔗 |
| Generate Helpful Hints As You Write Your Pandas Code | 🔗 | 🔗 |
| Speed-up Parquet I/O of Pandas by 5x | 🔗 | 🔗 |
| Stop Using The Describe Method in Pandas. Instead, use Skimpy. | 🔗 | 🔗 |
| Stop Using The Describe Method in Pandas. Instead, use Summarytools. | 🔗 | 🔗 |
| Analyze A Pandas DataFrame Without Code | 🔗 | 🔗 |
| 70x Faster Pandas By Changing Just One Line of Code | 🔗 | 🔗 |
| Reduce Memory Usage Of A Pandas DataFrame By 90% | 🔗 | 🔗 | 🔗 |
| Speed-up Pandas Apply 5x with NumPy | 🔗 | 🔗 |
| A Lesser-Known Feature of Apply Method In Pandas | 🔗 | 🔗 |
| Create Pandas DataFrame from Dataclass | 🔗 | 🔗 |
| Run SQL in Jupyter To Analyze A Pandas DataFrame | 🔗 | 🔗 |
| When You Should Not Use the head() Method In Pandas | 🔗 | 🔗 |
| Three Lesser-known Tips For Reading a CSV File Using Pandas | 🔗 | 🔗 |
| The Best File Format To Store A Pandas DataFrame | 🔗 | 🔗 | 🔗 |
| Lesser-Known Feature of the Merge Method in Pandas | 🔗 | 🔗 |
| The Best Way to Use Apply() in Pandas | 🔗 | 🔗 |
| A No-code Tool To Understand Your Data Quickly | 🔗 | 🔗 |
| Display Progress Bar With Apply() in Pandas | 🔗 | 🔗 |
| Supercharge value_counts() Method in Pandas With Sidetable | 🔗 | 🔗 |
| Explore CSV Data Right From The Terminal | 🔗 | 🔗 |
| Define the Correct DataType for Categorical Columns | 🔗 | 🔗 | 🔗 |
| Don't Create Conditional Columns in Pandas with Apply | [🔗](https://github.com/ChawlaAvi/Dail |
CoenMeintjes /
A comprehensive collection of Jupyter Notebook templates for data science tasks, developed to improve workflow efficiency and cover a wide range of topics, including exploratory data analysis, hypothesis testing, regression models, and machine learning models.
computationalcore /
A very useful collection of Jupyter Notebooks, which aims to introduce the Python programming language.
Tinny-Robot /
A collection of Jupyter notebooks for AI and ML tasks. Explore, learn, and contribute to advance your skills in artificial intelligence and machine learning. #Hacktoberfest friendly!
Dheeraj-02NK /
This repository, named "ml_practice," serves as a learning ground for various machine learning concepts and techniques. The repository contains a collection of Python scripts and Jupyter notebooks, each dedicated to exploring and implementing different machine learning algorithms, models, and methodologies.