<jupyter_start><jupyter_text>Pandas support Without anything else, physipy is kinda supported in pandas, but performances will be quite degraded. It seems a 1d quantity array will be split element-wise and stored, hence all operations will be done "loop"-wise, loosing the power of numpy arrays.See [physipandas](https://github.com/mocquin/physipandas) for better interface between pandas and physipy.<jupyter_code>import numpy as np
import pandas as pd

import physipy
from physipy import K, s, m, kg, units
arr = np.arange(10)
heights = np.random.normal(1.8, 0.1, 10)*m
heights.favunit = units["mm"]
weights = np.random.normal(74, 3, 10)*kg<jupyter_output><empty_output><jupyter_text>Dataframe<jupyter_code>df = pd.DataFrame({
    "heights":heights,
    "temp":arr*K,
    "weights":weights, 
    "arr":arr,
})
df["heights"]
df
df.info()
df.describe()
%timeit df["heights"].min()
%timeit df["arr"].min()<jupyter_output>57.5 µs ± 581 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
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