python - Use a.empty, a.bool(), a.item(), a.any() or a.all()

Question

import random
import pandas as pd

heart_rate = [random.randrange(45,125) for _ in range(500)]
blood_pressure_systolic = [random.randrange(140,230) for _ in range(500)]
blood_pressure_dyastolic = [random.randrange(90,140) for _ in range(500)]
temperature = [random.randrange(34,42) for _ in range(500)]
respiratory_rate = [random.randrange(8,35) for _ in range(500)]
pulse_oximetry = [random.randrange(95,100) for _ in range(500)]


vitalsign = {'heart rate' : heart_rate,
             'systolic blood pressure' : blood_pressure_systolic,
             'dyastolic blood pressure' : blood_pressure_dyastolic,
             'temperature' : temperature,
             'respiratory rate' : respiratory_rate,
             'pulse oximetry' : pulse_oximetry}


df = pd.DataFrame(vitalsign)


df.to_csv('vitalsign.csv')


mask = (50  < df['heart rate'] < 101 &
        140 < df['systolic blood pressure'] < 160 &
        90  < df['dyastolic blood pressure'] < 100 &
        35  < df['temperature'] < 39 &
        11  < df['respiratory rate'] < 19 &
        95  < df['pulse oximetry'] < 100
        , "excellent", "critical")

df.loc[mask, "class"]

it seems to be that,

error that i am receiving :

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

. how can i sort it out

Answer 1

As user2357112 mentioned in the comments, you cannot use chained comparisons here. For elementwise comparison you need to use &. That also requires using parentheses so that & wouldn't take precedence.

It would go something like this:

mask = ((50  < df['heart rate']) & (101 > df['heart rate']) & (140 < df['systolic...

In order to avoid that, you can build series for lower and upper limits:

low_limit = pd.Series([90, 50, 95, 11, 140, 35], index=df.columns)
high_limit = pd.Series([160, 101, 100, 19, 160, 39], index=df.columns)

Now you can slice it as follows:

mask = ((df < high_limit) & (df > low_limit)).all(axis=1)
df[mask]
Out: 
     dyastolic blood pressure  heart rate  pulse oximetry  respiratory rate  
17                        136          62              97                15   
69                        110          85              96                18   
72                        105          85              97                16   
161                       126          57              99                16   
286                       127          84              99                12   
435                        92          67              96                13   
499                       110          66              97                15   

     systolic blood pressure  temperature  
17                       141           37  
69                       155           38  
72                       154           36  
161                      153           36  
286                      156           37  
435                      155           36  
499                      149           36  

And for assignment you can use np.where:

df['class'] = np.where(mask, 'excellent', 'critical')