Update render script and Makefile

terminal/pythondatascience

@@ -102,7 +102,8 @@
 ⟡ CatBoost (https://github.com/catboost/catboost) - An open-source gradient boosting on decision trees library. 
 ⟡ ThunderGBM (https://github.com/Xtra-Computing/thundergbm) - Fast GBDTs and Random Forests on GPUs. 
 ⟡ NGBoost (https://github.com/stanfordmlgroup/ngboost) - Natural Gradient Boosting for Probabilistic Prediction.
-⟡ TensorFlow Decision Forests (https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. 
+⟡ TensorFlow Decision Forests
+ (https://github.com/tensorflow/decision-forests) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras. 
 
 Ensemble Methods
 ⟡ ML-Ensemble (http://ml-ensemble.com/) - High performance ensemble learning. 
@@ -239,10 +240,12 @@
 ⟡ Chaos Genius (https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
 
 Reinforcement Learning
-⟡ Gymnasium (https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym 
-(https://github.com/openai/gym)).
-⟡ PettingZoo (https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.
-⟡ MAgent2 (https://github.com/Farama-Foundation/MAgent2) - An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.
+⟡ Gymnasium (https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities 
+(formerly Gym (https://github.com/openai/gym)).
+⟡ PettingZoo
+ (https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.
+⟡ MAgent2
+ (https://github.com/Farama-Foundation/MAgent2) - An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.
 ⟡ Stable Baselines3 (https://github.com/DLR-RM/stable-baselines3) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
 ⟡ Shimmy (https://github.com/Farama-Foundation/Shimmy) - An API conversion tool for popular external reinforcement learning environments.
 ⟡ EnvPool (https://github.com/sail-sg/envpool) - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
@@ -260,7 +263,8 @@
 ⟡ garage (https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.
 ⟡ Horizon (https://github.com/facebookresearch/Horizon) - A platform for Applied Reinforcement Learning.
 ⟡ rlpyt (https://github.com/astooke/rlpyt) - Reinforcement Learning in PyTorch. 
-⟡ cleanrl (https://github.com/vwxyzjn/cleanrl) - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).
+⟡ cleanrl
+ (https://github.com/vwxyzjn/cleanrl) - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).
 ⟡ Machin (https://github.com/iffiX/machin) -  A reinforcement library designed for pytorch. 
 ⟡ SKRL (https://github.com/Toni-SM/skrl) - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym. 
 ⟡ Imitation (https://github.com/HumanCompatibleAI/imitation) - Clean PyTorch implementations of imitation and reward learning algorithms. 
@@ -304,7 +308,8 @@
 ⟡ InferPy (https://github.com/PGM-Lab/InferPy) - Deep Probabilistic Modelling Made Easy.  
 ⟡ PyStan (https://github.com/stan-dev/pystan) - Bayesian inference using the No-U-Turn sampler (Python interface).
 ⟡ sklearn-bayes (https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API. 
-⟡ skpro (https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute (https://www.turing.ac.uk/). 
+⟡ skpro (https://github.com/alan-turing-institute/skpro) - Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute 
+(https://www.turing.ac.uk/). 
 ⟡ PyVarInf (https://github.com/ctallec/pyvarinf) - Bayesian Deep Learning methods with Variational Inference for PyTorch. 
 ⟡ emcee (https://github.com/dfm/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC.
 ⟡ hsmmlearn (https://github.com/jvkersch/hsmmlearn) - A library for hidden semi-Markov models with explicit durations.
@@ -372,8 +377,8 @@
 ⟡ POT (https://github.com/rflamary/POT) - Python Optimal Transport library.
 ⟡ Talos (https://github.com/autonomio/talos) - Hyperparameter Optimization for Keras Models.
 ⟡ nlopt (https://github.com/stevengj/nlopt) - Library for nonlinear optimization (global and local, constrained or unconstrained).
-⟡ OR-Tools (https://developers.google.com/optimization) - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, 
-CPLEX, and Gurobi.
+⟡ OR-Tools (https://developers.google.com/optimization) - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, 
+GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.
 
 Feature Engineering
 
@@ -388,14 +393,16 @@
 ⟡ tsfresh (https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series. 
 ⟡ dirty_cat (https://github.com/dirty-cat/dirty_cat) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression). 
 ⟡ NitroFE (https://github.com/NITRO-AI/NitroFE) - Moving window features. 
-⟡ sk-transformer (https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps 
+⟡ sk-transformer
+ (https://github.com/chrislemke/sk-transformers) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps 
 
 
 Feature Selection
 ⟡ scikit-feature (https://github.com/jundongl/scikit-feature) - Feature selection repository in Python.
 ⟡ boruta_py (https://github.com/scikit-learn-contrib/boruta_py) - Implementations of the Boruta all-relevant feature selection method. 
 ⟡ BoostARoota (https://github.com/chasedehan/BoostARoota) - A fast xgboost feature selection algorithm. 
-⟡ scikit-rebate (https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. 
+⟡ scikit-rebate
+ (https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. 
 ⟡ zoofs (https://github.com/jaswinder9051998/zoofs) - A feature selection library based on evolutionary algorithms.
 
 Visualization
@@ -413,7 +420,8 @@
 ⟡ Bokeh (https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
 ⟡ Altair (https://altair-viz.github.io/) - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
 ⟡ bqplot (https://github.com/bqplot/bqplot) - Plotting library for IPython/Jupyter notebooks
-⟡ pyecharts (https://github.com/pyecharts/pyecharts) - Migrated from Echarts (https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing library.
+⟡ pyecharts (https://github.com/pyecharts/pyecharts) - Migrated from Echarts (https://github.com/apache/echarts), a charting and visualization library, to Python's interactive visual drawing 
+library.
 Map
 ⟡ folium (https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map
 ⟡ geemap (https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)
@@ -457,14 +465,15 @@
 ⟡ blaze (https://github.com/blaze/blaze) - NumPy and pandas interface to Big Data. 
 ⟡ pandasql (https://github.com/yhat/pandasql) -  Allows you to query pandas DataFrames using SQL syntax. 
 ⟡ pandas-gbq (https://github.com/pydata/pandas-gbq) - pandas Google Big Query. 
-⟡ xpandas (https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data containers with Transformers .functionality for data analysis by The Alan Turing Institute (https://www.turing.ac.uk/).
+⟡ xpandas (https://github.com/alan-turing-institute/xpandas) - Universal 1d/2d data containers with Transformers .functionality for data analysis by The Alan Turing Institute 
+(https://www.turing.ac.uk/).
 ⟡ pysparkling (https://github.com/svenkreiss/pysparkling) - A pure Python implementation of Apache Spark's RDD and DStream interfaces. 
 ⟡ modin (https://github.com/modin-project/modin) - Speed up your pandas workflows by changing a single line of code. 
 ⟡ swifter (https://github.com/jmcarpenter2/swifter) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
 ⟡ pandas-log (https://github.com/eyaltrabelsi/pandas-log) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
 ⟡ vaex (https://github.com/vaexio/vaex) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
-⟡ xarray (https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more 
-intuitive, concise, and less error-prone indexing routines.
+⟡ xarray (https://github.com/pydata/xarray) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named 
+dimensions for more intuitive, concise, and less error-prone indexing routines.
 
 Pipelines
 ⟡ pdpipe (https://github.com/shaypal5/pdpipe) - Sasy pipelines for pandas DataFrames.
@@ -477,7 +486,8 @@
 ⟡ meza (https://github.com/reubano/meza) - A Python toolkit for processing tabular data.
 ⟡ Prodmodel (https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
 ⟡ dopanda (https://github.com/dovpanda-dev/dovpanda) -  Hints and tips for using pandas in an analysis environment. 
-⟡ Hamilton (https://github.com/DAGWorks-Inc/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
+⟡ Hamilton
+ (https://github.com/DAGWorks-Inc/hamilton) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.
 
 Data-centric AI
 ⟡ cleanlab (https://github.com/cleanlab/cleanlab) - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
@@ -528,14 +538,16 @@
 ⟡ numdifftools (https://github.com/pbrod/numdifftools) - Solve automatic numerical differentiation problems in one or more variables.
 ⟡ quaternion (https://github.com/moble/quaternion) - Add built-in support for quaternions to numpy.
 ⟡ adaptive (https://github.com/python-adaptive/adaptive) - Tools for adaptive and parallel samping of mathematical functions.
-⟡ NumExpr (https://github.com/pydata/numexpr) - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for 
-intermediate results.
+⟡ NumExpr (https://github.com/pydata/numexpr) - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding 
+memory allocation for intermediate results.
 
 Web Scraping
 ⟡ BeautifulSoup (https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
 ⟡ Scrapy (https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
-⟡ Selenium (https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
-⟡ Pattern (https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
+⟡ Selenium
+ (https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
+⟡ Pattern
+ (https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
 ⟡ twitterscraper (https://github.com/taspinar/twitterscraper): Efficient library to scrape Twitter
 
 Spatial Analysis