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I am trying to propagate uncertainty in Python by adding two columns together of a Pandas data frame and then taking the square root. However, when I try to use Python's math.sqrt function, I receive a TypeError: cannot convert the series to <class 'float'>.

Here is what I've been doing:

joinedDF['combined_error'] = math.sqrt((joinedDF.error1**2 + joinedDF.error2**2).astype(float))

Using joinedDF.dtypes, I've verified that both error columns are float64.

I've even tried seperating everything out, but I still receive a TypeError. Any suggestions about how to mend this?

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1 Answers1

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Two options come to mind.

Option 1: use numpy.sqrt:

import numpy as np

joinedDF['combined_error'] = np.sqrt((joinedDF['error1']**2 +
                                      joinedDF['error2']**2))

Option 2: if you want/need to avoid numpy for some reason, you can apply math.sqrt to the numeric column. This is likely slower than Option 1, but works in my testing:

   joinedDF['combined_error'] = (joinedDF['error1']**2 + 
                                 joinedDF['error2']**2).apply(math.sqrt)

Minor style comment: it's generally recommended to refer to DataFrame columns using indexing (square brackets) rather than attribute access (dot notation), so I modified your code accordingly. More reading: What is the difference between using squared brackets or dot to access a column?

Peter Leimbigler
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