•1 min read•from Machine Learning
"colss" a math-style expression evaluator for NumPy arrays [P]
Our take
Introducing **colss**, a streamlined Python library designed to simplify the evaluation of mathematical expressions for NumPy arrays. By leveraging a more compact and math-inspired syntax, colss allows users to write complex queries with enhanced readability. Built using C++, OpenMP, pybind11, and ExprTk, colss supports logical expressions, arithmetic operations, and conditional statements, making it a powerful tool for data manipulation. Join us in transforming your data experience with concise and intuitive expressions. Explore colss on GitHub: https://github.com/SivaPA08/colss. Your feedback is welcome as we
Built a small Python library called "colss" that lets you write NumPy expressions using a shorter, more mathematical syntax.
Built using C++, OpenMP, pybind11, ExprTk, and NumPy.
Github: https://github.com/SivaPA08/colss
Example:
a = np.array([1,2,3,4]) b = np.array([4,5,6,7]) c = 2 res = colss.query("sin(a+b) + log(b)^c + 12") It supports:
- logical expressions
- arithmetic operations
- ternary operators
- conditional expressions
Example:
a = np.array([1,2,3,4]) res = colss.query("a > 2 ? sqrt(a) : log(a+1)") res = colss.query("if( a>b , a+1 , b-1 )") Compared to plain NumPy syntax, the goal is mainly:
- shorter expressions
- math-like notation
- improved readability for larger and complex formulas
Still early-stage and looking for suggestions/feedback.
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