unable to perform min-CodePudding

I have a following dataframe:

import pandas as pd
import itertools

d =  {'A': range(1, 11), 'C': range(1, 16)}

df = pd.DataFrame(itertools.product(*d.values()),columns=d.keys())

I would like to add new column Gc which will take a value of two nested min:

df.assign(Gc=min(53*df["C"] - 0.9*df["C"]**2, min(62*df["A"] - 1.5*df["A"]**2, 15)))

But I got and error:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

What is the problem here, please?

CodePudding user response：

The issue is that min is a python function, which only works with scalar (not vectors).

You can use numpy:

import numpy as np

df.assign(Gc=np.minimum(53*df["C"] - 0.9*df["C"]**2,
                        np.minimum(62*df["A"] - 1.5*df["A"]**2, 15)))

output:

      A   C    Gc
0     1   1  15.0
1     1   2  15.0
2     1   3  15.0
3     1   4  15.0
4     1   5  15.0
..   ..  ..   ...
145  10  11  15.0
146  10  12  15.0
147  10  13  15.0
148  10  14  15.0
149  10  15  15.0

[150 rows x 3 columns]