I found this to be quite useful, and it might be beneficial for you as well. There's a YouTube video course available, along with a GitHub page focusing on R and Python.
PDF can be found here: @reza_jafari_ai

PyGWalker: A Python Library for Exploratory Data Analysis with Visualization
docs
colab

Some of them are not free.

Vectorizing in JAX

def dot(v1, v2):
    return jax.numpy.dot(v1, v2)

1️⃣ Naively vectorizing

dot_naive =[dot(v1, v2) for v1, v2 in zip(v1s, v2s)]

2️⃣ Manual vectorizing

def dot_vectorized(v1s, v2s):
    return jnp.einsum("ij,ij->i", v1s, v2s)

3️⃣ Automatic vectorizing

dot_vmapped = jax.vmap(dot)

⏰ Timing

%timeit [dot(v1, v2) for v1, v2 in zip(v1s, v2s)]
%timeit dot_vectorized(v1s, v2s).block_until_ready()
%timeit dot_vmapped(v1s, v2s).block_until_ready()

# 5.15 ms ± 54.3 µs per loop
# 135 µs ± 171 ns per loop
# 543 µs ± 1.38 µs per loop

Adding JIT

dot_vectorized_jitted = jax.jit(dot_vectorized)
dot_vmapped_jitted = jax.jit(dot_vmapped)

Timing

bash
6.5 µs ± 12.9 ns per loop
6.39 µs ± 13.4 ns per loop

Notebook
ویرگول

Statistical hypothesis tests
Github

Here are some of the most important and frequently used commands in scikit-learn (sklearn):

1. Model Selection:
- train_test_split(): Split arrays or matrices into random train and test subsets.
- cross_val_score(): Evaluate a score by cross-validation.
- GridSearchCV(): Exhaustive search over specified parameter values for an estimator.
- StratifiedKFold(): Provides train/test indices to split data into train/test sets while maintaining class distribution.

2. Preprocessing:
- StandardScaler(): Standardize features by removing the mean and scaling to unit variance.
- MinMaxScaler(): Transform features by scaling each feature to a given range.
- OneHotEncoder(): Encode categorical integer features as one-hot numeric arrays.

3. Model Building:
- LinearRegression(): Ordinary least squares Linear Regression.
- LogisticRegression(): Logistic Regression (for classification tasks).
- RandomForestClassifier(): Random Forest Classifier.
- RandomForestRegressor(): Random Forest Regressor.
- GradientBoostingClassifier(): Gradient Boosting Classifier.
- GradientBoostingRegressor(): Gradient Boosting Regressor.
- DecisionTreeClassifier(): Decision Tree Classifier.

4. Model Evaluation:
- accuracy_score(): Accuracy classification score.
- precision_score(), recall_score(), f1_score(): Compute precision, recall, F-measure, and support for classification.
- mean_squared_error(): Mean squared error regression loss.
- r2_score(): R^2 (coefficient of determination) regression score function.

5. Pipeline and Feature Union:
- Pipeline(): Chain multiple estimators into one.
- FeatureUnion(): Combine several transformer objects into a new transformer.

6. Dimensionality Reduction:
- PCA(): Principal Component Analysis.
- TruncatedSVD(): Dimensionality reduction using truncated singular value decomposition.

7. Clustering:
- KMeans(): K-Means clustering.
- AgglomerativeClustering(): Agglomerative hierarchical clustering.

These are just a few of the many functionalities provided by scikit-learn for machine learning tasks.

Seaborn is a popular Python visualization library built on top of Matplotlib. Here are some of the most frequently used functions in Seaborn:

1. Data Visualization:
- sns.scatterplot(): Scatter plot.
- sns.lineplot(): Line plot.
- sns.barplot(): Bar plot.
- sns.countplot(): Count plot.
- sns.boxplot(): Box plot.
- sns.violinplot(): Violin plot.
- sns.heatmap(): Heatmap.
- sns.pairplot(): Pairwise plot.
- sns.jointplot(): Joint plot.
- sns.distplot(): Distribution plot.
- sns.regplot(): Regression plot.

2. Styling and Aesthetics:
- sns.set_style(): Set aesthetic style of plots.
- sns.set_context(): Set the context for plot elements.
- sns.set_palette(): Set the color palette for the plot.

3. Categorical Data Visualization:
- sns.catplot(): Figure-level interface for drawing categorical plots.
- sns.factorplot(): Draw categorical plots onto a FacetGrid.

4. Matrix Plots:
- sns.clustermap(): Plot a matrix dataset as a hierarchically-clustered heatmap.
- sns.heatmap(): Plot rectangular data as a color-encoded matrix.

5. Time Series Visualization:
- sns.tsplot(): Time series plot.

6. Faceting:
- sns.FacetGrid(): Multi-plot grid for plotting conditional relationships.

7. Regression Plots:
- sns.lmplot(): Plot data and regression model fits across a FacetGrid.
- sns.regplot(): Plot data and a linear regression model fit.

8. Distribution Plots:
- sns.distplot(): Flexibly plot a univariate distribution of observations.
- sns.kdeplot(): Fit and plot a univariate or bivariate kernel density estimate.

*️⃣ Data Science Dojo has added more than 43 data sets to this repository.
1️⃣ The repository carries a diverse range of themes, difficulty levels, sizes and attributes.
2️⃣ They offer hands-on practice to boost their skills in exploratory data analysis, data visualization, data wrangling and machine learning.
3️⃣ The data sets below have been sorted with increasing level of difficulty for convenience (Beginner, Intermediate, Advanced).
https://code.datasciencedojo.com/datasciencedojo/datasets

Python 100 programs.

Partial functions allow us to fix a certain number of arguments of a function and generate a new function

from functools import partial
 
def add(a, b, c):
    return 100 * a + 10 * b + c
 
# A partial function with b = 1 and c = 2
add_part = partial(add, c = 2, b = 1)
 
# Calling partial function,  input is a
print(add_part(3))

Post-doctoral in Marseille.

Project Title: Higher-order interactions in human brain networks supporting causal learning

I have a package let's call it my_package in python. how to implement a function to call it like my_package.tests() to run all tests?


## Create a tests Module

1. In your my_package directory, create a new directory called tests.
2. Inside the tests directory, create an empty __init__.py file to make it a Python package.
3. Create a new Python file, e.g., test_suite.py, where you will define your test suite.

## Define the tests() Function

1. In the test_suite.py file, import the necessary testing framework (e.g., unittest or `pytest`).
2. Define a function called tests() that will run all the tests in your package.

Here's an example using the unittest framework:

import unittest
from . import test_module1, test_module2

def tests():
    suite = unittest.TestSuite()
    suite.addTests(unittest.TestLoader().loadTestsFromModule(test_module1))
    suite.addTests(unittest.TestLoader().loadTestsFromModule(test_module2))
    
    runner = unittest.TextTestRunner(verbosity=2)
    runner.run(suite)

test_module1.py is something like this:

import unittest
import numpy as np 

class test_module_add(unittest.TestCase):
    def test_add(self):
        self.assertEqual(np.add(1, 2), 3)

In this example, the tests() function creates a TestSuite object, adds tests from the test_module1 and test_module2 modules, and then runs the test suite using a TextTestRunner.

## Import the tests() Function

1. In your my_package/__init__.py file, import the tests() function from the test_suite.py file:

from .tests.test_suite import tests

Now, you can call the tests() function from your package like this:

import my_package
my_package.tests()

This will run all the tests in your my_package package.

Pre-commit:

The pre-commit package is a tool used in software development to manage and maintain code quality. It allows developers to set up and run a series of checks on their code before committing it to version control systems like Git. These checks can include formatting, syntax, style, and even running tests to ensure that the code meets predefined standards and doesn't introduce any errors or bugs. By catching issues early in the development process, pre-commit helps to ensure that the codebase remains clean, consistent, and maintainable.

how to install and set up:
https://pre-commit.com/

you need to put a .pre-commit-config.yaml at the root of your project.
then just run to check and correct over all files:

bash
pre-commit install
pre-commit run --all-files

An example of configuration file:

yaml
repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
  rev: v0.3.3
  hooks:
    - id: ruff
    - id: ruff-format
      args: [--diff]
- repo: https://github.com/pre-commit/pre-commit-hooks
  rev: v4.5.0
  hooks:
    - id: check-added-large-files
    - id: check-merge-conflict
    - id: check-toml
    - id: check-yaml
    - id: end-of-file-fixer
    - id: mixed-line-ending
      args: [--fix=lf]
    - id: trailing-whitespace

I had trouble installing R and 'tidyverse' on Ubuntu because the standard repositories offer older versions of R, which caused some packages not to install properly.
This resolved the issue:

Link

#seaborn
#visualization

Matplotlib tips
Github