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   lt  df  heart rate    lt  101  amp          140  lt  df  systolic blood pressure    lt  160  amp          90   lt  df  dyastolic blood pressure    lt  100  amp          35   lt  df  temperature    lt  39  amp          11   lt  df  respiratory rate    lt  19  amp          95   lt  df  pulse oximetry    lt  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

User · Accepted Answer

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')

User · Answer

solution is easy   replace   mask    50   lt  df  heart rate    lt  101  amp              140  lt  df  systolic blood pressure    lt  160  amp              90   lt  df  dyastolic blood pressure    lt  100  amp              35   lt  df  temperature    lt  39  amp              11   lt  df  respiratory rate    lt  19  amp              95   lt  df  pulse oximetry    lt  100                excellent    critical     by   mask     50   lt  df  heart rate    lt  101   amp           140  lt  df  systolic blood pressure    lt  160   amp           90   lt  df  dyastolic blood pressure    lt  100   amp           35   lt  df  temperature    lt  39   amp           11   lt  df  respiratory rate    lt  19   amp           95   lt  df  pulse oximetry    lt  100             excellent    critical

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

Examples related to python

Examples related to pandas