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
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')
solution is easy:
replace
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")
by
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")
Source: Stackoverflow.com