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.

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

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

Pandas
Jupyter Tips
Python
Plotting
NumPy
Memory Optimization
Cool Tools
Run-time Optimization
Sklearn
Debugging
Missing Data
ML-AI News
Machine Learning
Statistics
Testing
Terminal
Documents
Animations

Pandas

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	🔗	🔗
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	🔗	🔗
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	🔗	🔗	🔗
Why You Should Not Read CSVs with Pandas	🔗	🔗	🔗
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	🔗	🔗	🔗
Parallelize Pandas with Pandarallel	🔗	🔗	🔗
Pretty Plotting With Pandas	🔗	🔗
How to Read Multiple CSV Files Efficiently	🔗	🔗	🔗
Configure Sklearn To Output Pandas DataFrame	🔗	🔗
Datatype For Handling Missing Valued Columns in Pandas	🔗	🔗	🔗
Vectorization Does Not Always Guarantee Better Performance	🔗	🔗

Jupyter Tips

Title	Notebook	Substack	Article
Declutter Your Jupyter Notebook Using Interactive Controls	🔗	🔗
🚀 Jupyter Notebook + Spreadsheet + AI — All in One Place With Mito	🔗	🔗
The Coolest GitHub-Colab Integration You Would Ever See	🔗	🔗
Break the Linear Presentation of Notebooks With Stickyland	🔗	🔗
Restart Jupyter Kernel Without Losing Variables	🔗	🔗
Annotate Data With The Click Of A Button Using Pigeon	🔗	🔗
Build Elegant Web Apps Right From Jupyter Notebook with Mercury	🔗	🔗
Supercharge Your Jupyter Kernel With ipyflow	🔗	🔗
PyGWalker: Analyze Pandas Dataframe in Jupyter using a Tableau-style Interface	🔗	🔗
Draw The Data You Are Looking For In Seconds	🔗	🔗
Never Search Jupyter Notebooks Manually Again To Find Your Code	🔗	🔗
Stop Previewing Raw DataFrames. Instead, Use DataTables	🔗	🔗
Label Your Data With The Click Of A Button	🔗	🔗
The Coolest Jupyter Notebook Hack	🔗	🔗
View Documentation in Jupyter Notebook	🔗	🔗
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	🔗	🔗	🔗
Restart Notebook Without Losing Variables	🔗	🔗	🔗
Retrieve Previously Computed Output In Jupyter Notebook	🔗	🔗	🔗
Transfer Variables Between Jupyter Notebooks	🔗	🔗	🔗

Python

Title	Notebook	Substack
7 Elegant Usages of Underscore in Python	🔗	🔗
How To Enforce Type Hints in Python?	🔗	🔗
A Common Misconception About Deleting Objects in Python	🔗	🔗
What Makes The Join() Method Blazingly Faster Than Iteration?	🔗	🔗
A Hidden Feature of a Popular String Method in Python	🔗	🔗
Execute Python Project Directory as a Script	🔗	🔗
Improve Python Run-time Without Changing A Single Line of Code	🔗	🔗
A Lesser-Known Difference Between For-Loops and List Comprehensions	🔗	🔗
A Lesser-Known Difference Between For-Loops and List Comprehensions	🔗	🔗
Magic Methods: An Underrated Gem of Python OOP	🔗	🔗
9 Command Line Flags To Run Python Scripts More Flexibly	🔗	🔗
Use Custom Python Objects In A Boolean Context	🔗	🔗
You Were Probably Given Incomplete Info About A Tuple's Immutability	🔗	🔗
A Counterintuitive Thing About Python Dictionaries	🔗	🔗
A Counterintuitive Thing About Python Dictionaries	🔗	🔗
Probably The Fastest Way To Execute Your Python Code	🔗	🔗
A Counterintuitive Fact About Python Functions	🔗	🔗
Manipulating Mutable Objects In Python Can Get Confusing At Times	🔗	🔗
Most Python Programmers Don't Know This About Python OOP	🔗	🔗
You Can Add a List As a Dictionary's Key (Technically)!	🔗	🔗
Why Python Does Not Offer True OOP Encapsulation	🔗	🔗
Most Python Programmers Don't Know This About Python For-loops	🔗	🔗
How To Enable Function Overloading In Python	🔗	🔗
The Right Way to Roll Out Library Updates in Python	🔗	🔗
F-strings Are Much More Versatile Than You Think	🔗	🔗
A Single Line That Will Make Your Python Code Faster	🔗	🔗
Make Dot Notation More Powerful in Python	🔗	🔗
An Elegant Way To Perform Shutdown Tasks in Python	🔗	🔗
What Are Class Methods and When To Use Them?	🔗	🔗
Hide Attributes While Printing A Dataclass Object	🔗	🔗
List : Tuple :: Set : ?	🔗	🔗
Post_init: Add Attributes To A Dataclass Post Initialization	🔗	🔗
Simplify Your Functions With Partial Functions	🔗	🔗
DotMap: A Better Alternative to Python Dictionary	🔗	🔗
Prevent Wild Imports With all in Python	🔗	🔗
Performance Comparison of Python 3.11 and Python 3.10	🔗	🔗
Why 256 is 256 But 257 is not 257?	🔗	🔗
Make a Class Object Behave Like a Function	🔗	🔗
Lesser-known Feature of Pickle Files	🔗	🔗
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
Don't Overuse Scatter, Line and Bar Plots. Try These Four Elegant Alternatives.	🔗	🔗
Sankey Diagrams: An Underrated Gem of Data Visualization	🔗	🔗
Enrich Your Heatmaps With This Simple Trick	🔗	🔗
The Coolest Matplotlib Hack to Create Subplots Intuitively	🔗	🔗
Waterfall Charts: A Better Alternative to Line/Bar Plot	🔗	🔗	🔗
Enrich Your Confusion Matrix With A Sankey Diagram	🔗	🔗
A Simple One-Liner to Create Professional Looking Matplotlib Plots	🔗	🔗
Visualise The Change In Rank Over Time With Bump Charts	🔗	🔗
A Simple Trick That Significantly Improves The Quality of Matplotlib Plots	🔗	🔗
A Lesser-known Feature of Creating Plots with Plotly	🔗	🔗
A Little Bit Of Extra Effort Can Hugely Transform Your Basic Matplotlib Plots	🔗	🔗
Interactively Visualise A Decision Tree With A Sankey Diagram	🔗	🔗
Use Histograms With Caution. They Are Highly Misleading!	🔗	🔗
Three Simple Ways To (Instantly) Make Your Scatter Plots Clutter Free	🔗	🔗
Matplotlib Has Numerous Hidden Gems. Here's One of Them.	🔗	🔗
A Simple Trick That Will Make Heatmaps More Elegant	🔗	🔗
The Limitations Of Heatmap That Are Slowing Down Your Data Analysis	🔗	🔗
An Underrated Technique To Improve Your Data Visualizations	🔗	🔗
Who Said Matplotlib Cannot Create Interactive Plots?	🔗	🔗
Don't Create Messy Bar Plots. Instead, Try Bubble Charts!	🔗	🔗
Use Box Plots With Caution! They May Be Misleading.	🔗	🔗
An Underrated Technique To Create Better Data Plots	🔗	🔗
An Interesting and Lesser-Known Way To Create Plots Using Pandas	🔗	🔗
Style Matplotlib Plots To Make Them More Attractive	🔗	🔗
Simple One-Liners to Preview a Decision Tree Using Sklearn	🔗	🔗
Create Data Plots Right From The Terminal	🔗	🔗
Make Your Matplotlib Plots More Professional	🔗	🔗
Perfplot: Measure, Visualize and Compare Run-time With Ease	🔗	🔗
Prettify Word Clouds In Python	🔗	🔗
Calendar Map As A Richer Alternative to Line Plot	🔗	🔗
Density Plot As A Richer Alternative to Scatter Plot	🔗	🔗	🔗
Python One-Liner To Create Sketchy Hand-drawn Plots	🔗	🔗
Create a Moving Bubbles Chart in Python	🔗	🔗
Visualizing Google Search Trends of 2022 using Python	🔗	🔗
Create A Racing Bar Chart In Python	🔗	🔗
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
A Major Limitation of NumPy Which Most Users Aren't Aware Of	🔗	🔗
Beware of This Unexpected Behaviour of NumPy Methods	🔗	🔗
Speedup NumPy Methods 25x With Bottleneck	🔗	🔗
Speed-up NumPy 20x with Numexpr	🔗	🔗
An Elegant Way To Perform Matrix Multiplication	🔗	🔗
Difference Between Dot and Matmul in NumPy	🔗	🔗
Don't Print NumPy Arrays! Use Lovely-NumPy Instead	🔗	🔗
Polynomial Linear Regression with NumPy	🔗	🔗

Memory Optimization

Title	Notebook	Substack	Article
70x Faster Pandas By Changing Just One Line of Code	🔗	🔗
Reduce Memory Usage Of A Pandas DataFrame By 90%	🔗	🔗	🔗
The Best File Format To Store A Pandas DataFrame	🔗	🔗	🔗
Define the Correct DataType for Categorical Columns	🔗	🔗	🔗
Datatype For Handling Missing Valued Columns in Pandas	🔗	🔗	🔗
Save Memory with Python Generators	🔗	🔗

Cool Tools

Title	Notebook	Substack	Article
CNN Explainer: Interactively Visualize a Convolutional Neural Network	🔗	🔗
Break the Linear Presentation of Notebooks With Stickyland	🔗	🔗
Annotate Data With The Click Of A Button Using Pigeon	🔗	🔗
Mito Just Got Supercharged With AI!	🔗	🔗
PyGWalker: Analyze Pandas Dataframe in Jupyter using a Tableau-style Interface	🔗	🔗
Supercharge Shell With Python Using Xonsh	🔗	🔗
Draw The Data You Are Looking For In Seconds	🔗	🔗
Preview Your README File Locally In GitHub Style	🔗	🔗
This GUI Tool Can Possibly Save You Hours Of Manual Work	🔗	🔗
Stop Previewing Raw DataFrames. Instead, Use DataTables.	🔗	🔗
Converting Python To LaTeX Has Possibly Never Been So Simple	🔗	🔗
Label Your Data With The Click Of A Button	🔗	🔗
Analyze A Pandas DataFrame Without Code	🔗	🔗
A No-Code Online Tool To Explore and Understand Neural Networks	🔗	🔗
Speed-up NumPy 20x with Numexpr	🔗	🔗
Debugging Made Easy With PySnooper	🔗	🔗
Deep Learning Network Debugging Made Easy	🔗	🔗
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	🔗	🔗	🔗
Modify Function During Run-Time	🔗	🔗	🔗
Importing Modules Made Easy with Pyforest	🔗	🔗
Create Pivot Tables, Aggregations and Plots Without Any Code	🔗	🔗	🔗

Run-time Optimization

Title	Notebook	Substack	Article
Pandas vs Polars — Run-time and Memory Comparison	🔗	🔗
The Limitation of KMeans Which Is Often Overlooked by Many	🔗	🔗
Most Sklearn Users Don't Know This About Its LinearRegression Implementation	🔗	🔗
Probably The Fastest Way To Execute Your Python Code	🔗	🔗
Why Are We Typically Advised To Never Iterate Over A DataFrame?	🔗	🔗
Speed-up Parquet I/O of Pandas by 5x	🔗	🔗
A Single Line That Will Make Your Python Code Faster	🔗	🔗
Make Sklearn KMeans 20x times faster	🔗	🔗
Speed-up NumPy 20x with Numexpr	🔗	🔗
The Best File Format To Store A Pandas DataFrame	🔗	🔗	🔗
The Best Way to Use Apply() in Pandas	🔗	🔗
Don't Create Conditional Columns in Pandas with Apply	🔗	🔗
Why you should not dump DataFrames to a CSV	🔗	🔗	🔗
Parallelize Pandas Apply() With Swifter	🔗	🔗
Parallelize Pandas with Pandarallel	🔗	🔗	🔗
How to Read Multiple CSV Files Efficiently	🔗	🔗	🔗

Sklearn

Title	Notebook	Substack
Why Sklearn's Linear Regression Has No Hyperparameters?	🔗	🔗
Scikit-LLM: Integrate Sklearn API with Large Language Models	🔗	🔗
Most Sklearn Users Don't Know This About Its LinearRegression Implementation	🔗	🔗
A Lesser-Known Feature of Sklearn To Train Models on Large Datasets	🔗	🔗
Sklearn One-liner to Generate Synthetic Data	🔗	🔗
Skorch: Use Scikit-learn API on PyTorch Models	🔗	🔗
Make Sklearn KMeans 20x times faster	🔗	🔗
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
Debugging Made Easy With PySnooper	🔗	🔗
Don't use print() to debug your code.	🔗	🔗	🔗
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
Decision Trees ALWAYS Overfit. Here's A Lesser-Known Technique To Prevent It.	🔗	🔗
Evaluate Clustering Performance Without Ground Truth Labels	🔗	🔗
The Most Common Misconception About Continuous Probability Distributions	🔗	🔗
A Common Misconception About Feature Scaling and Standardization	🔗	🔗
Random Forest May Not Need An Explicit Validation Set For Evaluation	🔗	🔗
A Visual and Overly Simplified Guide To Bagging and Boosting	🔗	🔗
10 Most Common (and Must-Know) Loss Functions in ML	🔗	🔗
A Visual and Overly Simplified Guide To Bagging and Boosting	🔗	🔗
10 Most Common (and Must-Know) Loss Functions in ML	🔗	🔗
Theil-Sen Regression: The Robust Twin of Linear Regression	🔗	🔗
The Limitations Of Elbow Curve And What You Should Replace It With	🔗	🔗
21 Most Important (and Must-know) Mathematical Equations in Data Science	🔗	🔗
Try This If Your Linear Regression Model is Underperforming	🔗	🔗
The Limitation of KMeans Which Is Often Overlooked by Many	🔗	🔗
Nine Most Important Distributions in Data Science	🔗	🔗
The Limitation of Linear Regression Which is Often Overlooked By Many	🔗	🔗
The Limitation of Linear Regression Which is Often Overlooked By Many	🔗	🔗
A Reliable and Efficient Technique To Measure Feature Importance	🔗	🔗
Does Every ML Algorithm Rely on Gradient Descent?	[🔗](https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science/blob/main/Machine%20Learning/Does Every ML Algorithm Rely on Gradient Descent?.ipynb)	🔗
Visualize The Performance Of Linear Regression With This Simple Plot	🔗	🔗
Confidence Interval and Prediction Interval Are Not The Same	🔗	🔗
The Ultimate Categorization of Performance Metrics in ML	🔗	🔗
The Most Overlooked Problem With One-Hot Encoding	🔗	🔗
9 Most Important Plots in Data Science	🔗	🔗
Is Categorical Feature Encoding Always Necessary Before Training ML Models?	🔗	🔗
The Counterintuitive Behaviour of Training Accuracy and Training Loss	🔗	🔗
A Highly Overlooked Point In The Implementation of Sigmoid Function	🔗	🔗
The Ultimate Categorization of Clustering Algorithms	🔗	🔗
A Lesser-Known Feature of Sklearn To Train Models on Large Datasets	🔗	🔗
Visualize The Performance Of Any Linear Regression Model With This Simple Plot	🔗	🔗
How To Truly Use The Train, Validation and Test Set	🔗	🔗
The Advantages and Disadvantages of PCA To Consider Before Using It	🔗	🔗
Loss Functions: An Algorithm-wise Comprehensive Summary	🔗	🔗
Is Data Normalization Always Necessary Before Training ML Models?	🔗	🔗
A Visual Guide to Stochastic, Mini-batch, and Batch Gradient Descent	🔗	🔗
The Taxonomy Of Regression Algorithms That Many Don't Bother To Remember	🔗	🔗
The Limitation of PCA Which Many Folks Often Ignore	🔗	🔗
Breathing KMeans: A Better and Faster Alternative to KMeans	🔗	🔗
How Many Dimensions Should You Reduce Your Data To When Using PCA?	🔗	🔗
A Visual Guide To Sampling Techniques in Machine Learning	🔗	🔗
A Visual and Overly Simplified Guide to PCA	🔗	🔗
The Limitation Of Euclidean Distance Which Many Often Ignore	🔗	🔗
Visualising The Impact Of Regularisation Parameter	🔗	🔗
A (Highly) Important Point to Consider Before You Use KMeans Next Time	🔗	🔗
Is Class Imbalance Always A Big Problem To Deal With?	🔗	🔗
A Visual Comparison Between Locality and Density-based Clustering	🔗	🔗
Why Don't We Call It Logistic Classification Instead?	🔗	🔗
A Typical Thing About Decision Trees Which Many Often Ignore	🔗	🔗
Always Validate Your Output Variable Before Using Linear Regression	🔗	🔗
Why Is It Important To Shuffle Your Dataset Before Training An ML Model	🔗	🔗
Why Are We Typically Advised To Set Seeds for Random Generators?	🔗	🔗
This Small Tweak Can Significantly Boost The Run-time of KMeans	🔗	🔗
Most ML Folks Often Neglect This While Using Linear Regression	🔗	🔗
Is This The Best Animated Guide To KMeans Ever?	🔗	🔗
An Effective Yet Underrated Technique To Improve Model Performance	🔗	🔗
How to Encode Categorical Features With Many Categories?	🔗	🔗
Why KMeans May Not Be The Apt Clustering Algorithm Always	🔗	🔗
Skorch: Use Scikit-learn API on PyTorch Models	🔗	🔗
A No-Code Online Tool To Explore and Understand Neural Networks	🔗	🔗
Make Sklearn KMeans 20x times faster	🔗	🔗
Deep Learning Network Debugging Made Easy	🔗	🔗
Build Baseline Models Effortlessly With Sklearn	🔗	🔗
Polynomial Linear Regression with NumPy	🔗	🔗

Statistics

Title	Notebook	Substack
Be Cautious Before Drawing Any Conclusions Using Summary Statistics	🔗	🔗
The Limitation Of Pearson Correlation Which Many Often Ignore	🔗	🔗
Pandas and NumPy Return Different Values for Standard Deviation. Why?	🔗	🔗
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
Supercharge Shell With Python Using Xonsh	🔗	🔗
Most Command-line Users Don't Know This Cool Trick About Using Terminals	🔗	🔗
Never Refactor Your Code Manually Again. Instead, Use Sourcery!	🔗	🔗
Create Data Plots Right From The Terminal	🔗	🔗
Visualize Commit History of Git Repo With Beautiful Animations	🔗	🔗
How Would You Identify Fuzzy Duplicates In A Data With Million Records?	🔗	🔗
Automated Code Refactoring With Sourcery	🔗	🔗	🔗
Explore CSV Data Right From The Terminal	🔗	🔗

Documents

Title	Document	Substack
Daily Dose of Data Science - Full Archive	🔗	🔗
35 Hidden Python Libraries That Are Absolute Gems	🔗	🔗
40 Open-Source Tools to Supercharge Your Pandas Workflow	🔗	🔗
37 Hidden Python Libraries That Are Absolute Gems	🔗	🔗
10 Automated EDA Tools That Will Save You Hours Of (Tedious) Work	🔗	🔗
30 Python Libraries to (Hugely) Boost Your Data Science Productivity	🔗	🔗

Animations

Title	Notebook	Substack	Video
Visualizing The Data Transformation of a Neural Network	🔗	🔗

ChawlaAvi/Daily-Dose-of-Data-Science

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