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

Installations & Python Packages
1

Hi



I am trying to write single line statement in Python as mentioned below



data['xyz'] =  data['Close'].shift(1).isna if data['high'] else data['low'] 



but I am getting ValueError as mentioned below, kindly advice 



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

2

Hi Ahmed,



Can you elaborate what are you trying to calculate in the column 'xyz' and share your entire code, so that we can look into it.



Thanks

3

Hi Satyapriya,



Thanks for the reply. Pleaes find the below code. Basically, what I am trying to do is that if the previous cell of the series is NAN then xyz columne will be populated with LOW else it will be populated with HIGH 



 

import datetime as dt

import yfinance as yf

import pandas as pd

import numpy as np

import math

import numpy as np





 



data = yf.download("MSFT",period="6mo")



data['xyz'] =   (data['Adj Close'].shift(1).isna() if data['Low'] else data['High'])



Thanks



Ahmed

4

Hi Ahmed,



You can use the below line of code to do the same



data = data.assign(xyz=np.where(data['Adj Close'].isnull(), data['Low'], data['High']))



Hope this would help you.

5

Hi SSatyapriay,



Million thanks to you

6

Hi Ahmed,



There was an overlook from my end. Since you are checking the previous term, we should also use the shift operator. Please modify the above code as 



data = data.assign(xyz=np.where(data['Adj Close'].shift(1).isnull(), data['Low'], data['High']))



Apologies for the inconvenience caused. Thanks.

7

Pandas follows the numpy convention of raising an error when you try to convert something to a bool. This happens in a if or when using the boolean operations, and, or, or not. It is not clear what the result of.



example

5 == pd.Series([12,2,5,10])

The result you get is a Series of booleans, equal in size to the pd.Series in the right hand side of the expression. So, you get an error. The problem here is that you are comparing a pandas pd.Series with a value, so you'll have multiple True and multiple False values, as in the case above. This of course is ambiguous, since the condition is neither True or False. You need to further aggregate the result so that a single boolean value results from the operation. For that you'll have to use either any or all depending on whether you want at least one (any) or all values to satisfy the condition.

(5 == pd.Series([12,2,5,10])).all()
# False

or

(5 == pd.Series([12,2,5,10])).any()
# True