colss 0.1.8

Less verbose Mathematical exp evaluator for pandas,numpy and polar

colss

colss is a lightweight expression evaluator for NumPy, Pandas, and Polars. It simplifies mathematical expressions while preserving memory efficiency and execution speed.

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Installation

pip install colss

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Requirements

All arrays passed to colss must be:

• 1D
• Preferably float64
• C-contiguous

If you have a 2D array:

a = a.ravel()

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Usage Examples

All functions accept string expressions.

query

import numpy as np
import colss

a = np.array([1.0, 2.0, 3.0], dtype=np.float64)
b = np.array([4.0, 5.0, 6.0], dtype=np.float64)

result = colss.query("a+b+7")

Ternary example:

colss.query("a > 1 ? 100 : 0")

Using mathematical functions:

colss.query("sqrt(a) + sin(a)")
colss.query("exp(a)")

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mean

import numpy as np
import colss

a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64)

m = colss.mean("a")

Expression example:

colss.mean("a+10")

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sigma (sum)

import numpy as np
import colss

a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64)

s = colss.sigma("a")

Expression example:

colss.sigma("a*2")

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prod (Product)

import numpy as np
import colss

a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64)

p = colss.prod("a")

Expression example:

colss.prod("a+1")

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Supported Operators

Arithmetic:

+  -  *  /  ^ %

Comparison:

>  <  >=  <=  ==  !=

Logical:

and  or not

Ternary:

condition ? value_if_true : value_if_false

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Available Functions Inside Expressions

abs(x)
sqrt(x)
pow(x, y)
log(x)
log10(x)
exp(x)
sin(x)
cos(x)
tan(x)
floor(x)
ceil(x)
min(x, y)
max(x, y)

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Using with Pandas

import pandas as pd
import numpy as np
import colss

 df = pd.DataFrame({
    "a": [1.0, 2.0, 3.0],
    "b": [4.0, 5.0, 6.0]
})

# Pandas evaluation
 df["c"] = df.eval("a + b + 7")

# colss evaluation
 a = df["a"].to_numpy(dtype=np.float64)
 b = df["b"].to_numpy(dtype=np.float64)

 df["d"] = colss.query("a+b+7")

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Notes

• All variables used in expressions must be registered inside colss before evaluation.
• All functions (query, mean, sigma, prod) accept string expressions.
• Designed for memory efficiency and predictable performance.