Daily Dose of Data Science

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.

Run These Code Snippets in Your Local Machine

To download the tips listed here, you can clone this repo.

git clone https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science

Table of Contents

1. Pandas
2. Jupyter Tips
3. Python
4. Plotting
5. NumPy
6. Memory Optimization
7. Cool Tools
8. Run-time Optimization
9. Sklearn
10. Debugging
11. Missing Data
12. ML-AI News
13. Machine Learning
14. Statistics
15. Testing
16. Terminal

Pandas

Title	Notebook	Substack	Article
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	🔗	🔗	Medium
Don't Create Conditional Columns in Pandas with Apply	🔗	🔗
Write Your Own Flavor Of Pandas	🔗	🔗
Create DataFrame Hassle-free By Using Clipboard	🔗	🔗
Alter the Datatype of Multiple Columns at Once	🔗	🔗
Why you should not dump DataFrames to a CSV	🔗	🔗	Medium
Why You Should Not Read CSVs with Pandas	🔗	🔗	Medium
Parallelize Pandas Apply() With Swifter	🔗	🔗
A Hidden Feature of Describe Method In Pandas	🔗	🔗
Enrich Your Notebook With Interactive Controls	🔗	🔗
Data Analysis Using No-Code Pandas In Jupyter	🔗	🔗
Create Pivot Tables, Aggregations and Plots Without Any Code	🔗	🔗	Medium
Parallelize Pandas with Pandarallel	🔗	🔗	Medium
Pretty Plotting With Pandas	🔗	🔗
How to Read Multiple CSV Files Efficiently	🔗	🔗	Medium
Configure Sklearn To Output Pandas DataFrame	🔗	🔗
Datatype For Handling Missing Valued Columns in Pandas	🔗	🔗	Medium
Vectorization Does Not Always Guarantee Better Performance	🔗	🔗

Jupyter Tips

Title	Notebook	Substack	Article
Get Notified When Jupyter Cell Has Executed	🔗	🔗
Clear Cell Output In Jupyter Notebook During Run-time	🔗	🔗
CodeSquire: The AI Coding Assistant You Should Use Over GitHub Copilot	🔗	🔗
Find Your Code Hiding In Some Jupyter Notebook With Ease	🔗	🔗
Enrich Your Notebook With Interactive Controls	🔗	🔗
Data Analysis Using No-Code Pandas In Jupyter	🔗	🔗
Create Pivot Tables, Aggregations and Plots Without Any Code	🔗	🔗	Medium
Restart Notebook Without Losing Variables	🔗	🔗	Medium
Retrieve Previously Computed Output In Jupyter Notebook	🔗	🔗	Medium
Transfer Variables Between Jupyter Notebooks	🔗	🔗	Medium

Python

Title	Notebook	Substack	Article
Specify Loops and Runs In %%timeit	🔗	🔗
Don't Use time.time() To Measure Execution Time	🔗	🔗
Import Your Python Package as a Module	🔗	🔗
Fine-grained Error Tracking With Python 3.11	🔗	🔗
Run Python Project Directory As A Script	🔗	🔗
Use Slotted Class To Improve Your Python Code	🔗	🔗
Using Dictionaries In Place of If-conditions	🔗	🔗
In Defense of Match-case Statements in Python	🔗	🔗

Plotting

Title	Notebook	Substack	Article
Elegantly Plot the Decision Boundary of a Classifier	🔗	🔗
Dot Plot: A Potential Alternative to Bar Plot	🔗	🔗
Hexbin Plots As A Richer Alternative to Scatter Plots	🔗	🔗
Enrich Your Notebook With Interactive Controls	🔗	🔗
Regression Plot Made Easy with Plotly	🔗	🔗
Pretty Plotting With Pandas	🔗	🔗
Polynomial Linear Regression Plot Made Easy With Seaborn	🔗	🔗
Analyse Flow Data With Sankey Diagrams	🔗	🔗
Waterfall Charts: A Better Alternative to Line/Bar Plot	🔗	🔗

NumPy

Title	Notebook	Substack	Article
Polynomial Linear Regression with NumPy	🔗	🔗

Memory Optimization

Title	Notebook	Substack	Article
Define the Correct DataType for Categorical Columns	🔗	🔗	Medium
Datatype For Handling Missing Valued Columns in Pandas	🔗	🔗	Medium
Save Memory with Python Generators	🔗	🔗

Cool Tools

Title	Notebook	Substack	Article
CodeSquire: The AI Coding Assistant You Should Use Over GitHub Copilot	🔗	🔗
Find Unused Python Code With Ease	🔗	🔗
Enrich Your Notebook With Interactive Controls	🔗	🔗
Data Analysis Using No-Code Pandas In Jupyter	🔗	🔗
Modify Python Code During Run-Time	🔗	🔗	Medium
Modify Function During Run-Time	🔗	🔗	Medium
Importing Modules Made Easy with Pyforest	🔗	🔗
Create Pivot Tables, Aggregations and Plots Without Any Code	🔗	🔗	Medium

Run-time Optimization

Title	Notebook	Substack	Article
Don't Create Conditional Columns in Pandas with Apply	🔗	🔗
Why you should not dump DataFrames to a CSV	🔗	🔗	Medium
Parallelize Pandas Apply() With Swifter	🔗	🔗
Parallelize Pandas with Pandarallel	🔗	🔗	Medium
How to Read Multiple CSV Files Efficiently	🔗	🔗	Medium

Sklearn

Title	Notebook	Substack	Article
Build Baseline Models Effortlessly With Sklearn	🔗	🔗
Polynomial Linear Regression with NumPy	🔗	🔗
An Elegant Way to Import Metrics From Sklearn	🔗	🔗
Feature Tracking Made Simple In Sklearn Transformers	🔗	🔗
Configure Sklearn To Output Pandas DataFrame	🔗	🔗

Debugging

Title	Notebook	Substack	Article
Don't use print() to debug your code.	🔗	🔗	Medium
Inspect Program Flow with IceCream	🔗	🔗
Lesser-known Feature of f-strings in Python	🔗	🔗

Missing Data

Title	Notebook	Substack	Article
Handle Missing Data With Missingno	🔗	🔗
Datatype For Handling Missing Valued Columns in Pandas	🔗	🔗

ML-AI News

Title	Notebook	Substack	Article
Now You Can Use DALL·E With OpenAI API	🔗	🔗

Machine Learning

Title	Notebook	Substack	Article
Build Baseline Models Effortlessly With Sklearn	🔗	🔗
Polynomial Linear Regression with NumPy	🔗	🔗

Statistics

Title	Notebook	Substack	Article
Why Correlation (and Other Statistics) Can Be Misleading	🔗	🔗

Testing

Title	Notebook	Substack	Article
Generate Your Own Fake Data In Seconds	🔗	🔗

Terminal

Title	Notebook	Substack	Article
Explore CSV Data Right From The Terminal	🔗	🔗