typed pandas

pandas_with_typing.py

import numpy as np


class Plant:
    """
    KISS implementation of a time series data class with a ndarray backing.
    Think of Pandas with typing.
    Properties can be also set by scalar values.
    """
    def __init__(self, dim: int):
        self.buffer = np.ndarray([dim, 6], dtype=float)

    def _fill(self, x: np.ndarray or float):
        if type(x) in (float, int):
            return np.repeat(x, self.buffer.shape[0])
        return x

    @property
    def power_min(self) -> np.ndarray:
        return self.buffer[:, 0]

    @power_min.setter
    def power_min(self, x: np.ndarray or float):
        x = self._fill(x)
        assert (x > 0).all()
        self.buffer[:, 0] = x

    @property
    def power_max(self) -> np.ndarray:
        return self.buffer[:, 0]

    @power_max.setter
    def power_max(self, x: np.ndarray or float):
        x = self._fill(x)
        assert (x > 0).all()
        self.buffer[:, 0] = x


plant = Plant(10)
plant.power_max = 3
print(plant.power_max)
print(plant.__dict__.keys())  # -> ['buffer']