pandas 在 df 中创建一个布尔列

Question

Here's some made data:

import pandas as pd
import numpy as np
import operator

rows,cols = 8760,3
data = np.random.rand(rows,cols) 
tidx = pd.date_range('2019-01-01', periods=rows, freq='1T') 
df = pd.DataFrame(data, columns=['Mix_Temp','Outside_Temp','Return_Temp'], index=tidx)

How can I create another pandas column that is Boolean True or False based on a fault condition that I incorporated into a function:

def fault_condition_two_(dataframe):
    return operator.truth(dataframe.Mix_Temp + dataframe.mat_err < min((dataframe.Return_Temp - dataframe.rat_err) , (dataframe.Outside_Temp - dataframe.oat_err)))

There are some additional params below if I try this:

# params
rat_err = 2.
mat_err = 5.
oat_err = 5. 

# make columns out of params
df['rat_err'] = rat_err
df['mat_err'] = mat_err
df['oat_err'] = oat_err

# run data thru function
df['bool_flag'] = fault_condition_two_(df)

Ill get this famous ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). which I cant seem to find out a better solution. Any tips greatly appreciated.

Note that I did have luck using the Python operator with this function below on a different fault condition equation where this worked great. What's best practice? This function works fine, no errors:

def fault_condition_one(dataframe):
    return operator.and_(dataframe.duct_static < dataframe.duct_static_setpoint, dataframe.vfd_speed >= dataframe.vfd_speed_percent_max - dataframe.vfd_speed_percent_err_thres)

Answer 1

IIUC use numpy version for min by numpy.minimum. , here operator.truth is not necessary:

def fault_condition_two_(dataframe):
    return (dataframe.Mix_Temp + dataframe.mat_err < np.minimum((dataframe.Return_Temp - dataframe.rat_err) , (dataframe.Outside_Temp - dataframe.oat_err)))

Answer 2

An alternative using only direct dataframe manipulation:

((df.Mix_Temp + df.mat_err) < (df.Return_Temp - df.rat_err))\
& ((df.Mix_Temp + df.mat_err) < (df.Return_Temp - df.rat_err))