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This Week in Python
6 projects | dev.to | 20 May 2022
open-data-anonymizer – Python Data Anonymization & Masking Library For Data Science Tasks
My first contribution to open-source community - anonympy package!
3 projects | reddit.com/r/Python | 7 Feb 2022
Nahh I really don't have any tests. The closest to "expected output" I have is this examples.ipynb notebook which provides usage examples.Thanks to you I already made my mind to learn and provide tests.3 projects | reddit.com/r/Python | 7 Feb 2022
u/Fomx Hey there! Here https://github.com/ArtLabss/open-data-anonymizer I did have both requirements.txt and setup.py.
What are some alternatives?
attractors - Package for simulation and visualization of strange attractors.
open-data-anonimizer - Python Data Anonymization & Masking Library For Data Science Tasks [Moved to: https://github.com/ArtLabss/open-data-anonymizer]
julia - The Julia Programming Language
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
concrete-numpy - Concrete Numpy is an open-source library which simplifies the use of fully homomorphic encryption.
WaveNCC - An app to compute the normalization coefficients of a given set of orthogonal 1D complex wave functions.
hiitpi - A workout trainer Dash/Flask app that helps track your HIIT workouts by analyzing real-time video streaming from your sweet Pi using machine learning and Edge TPU..
GreyNSights - Privacy-Preserving Data Analysis using Pandas
napari - napari: a fast, interactive, multi-dimensional image viewer for python
Django - The Web framework for perfectionists with deadlines.
quaternion - Add built-in support for quaternions to numpy
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.