entroscope 0.1.0

The definitive entropy toolkit for time series data

entroscope

The definitive entropy toolkit for time series data.

pip install entroscope and get every entropy measure you'd ever need, with one consistent interface that works directly on pandas Series and numpy arrays.

Born from NextOnMenu, where Shannon entropy of food-trend search interest had to be computed by hand. entroscope makes that a one-liner.

Install

pip install entroscope

Quick start

import pandas as pd
from entroscope import shannon

s = pd.Series([10, 20, 15, 80, 90, 85, 88, 92])
shannon.compute(s)              # single entropy value
shannon.rolling(s, window=20)   # rolling entropy over time
shannon.delta(s, window=20)     # rate of change
shannon.plot(s, window=20)      # matplotlib Figure

Measures

shannon · permutation · sample · approximate · spectral · differential · multiscale

Every measure shares the same API: compute, rolling, delta, plot (normalized where a theoretical maximum exists). Series in → Series out (index preserved); ndarray in → ndarray out.

Method	Returns
compute	float
rolling	Series/ndarray, same length
delta	Series/ndarray (first difference)
normalized	float in [0, 1] (where defined)
plot	matplotlib.figure.Figure

Real-world example — food-trend analysis (NextOnMenu)

import pandas as pd
from entroscope import shannon

matcha_trends = pd.read_csv("matcha_trends.csv")["interest"]
shannon.plot(matcha_trends, window=20, title="Matcha — entropy over time")
# entropy drops before a trend goes mainstream

A sustained drop in rolling Shannon entropy means search interest is becoming concentrated/structured rather than noisy — an early signal of a trend.

License

MIT