dataframe TypeError: cannot convert the series to class ‘float’ is an error which may occur for many reasons, maybe your dataframe is faulty or maybe you used Math.log with an array.
In the blog post I attempt to explain why this error takes place and how you can solve it, I will also add other solutions that could solve the error if possible.
Exploring dataframe TypeError: cannot convert the series to class ‘float’
This is an error which may occur for many reasons, maybe your dataframe is faulty or maybe you used Math.log with an array.
You should avoid mixing between different errors. The error message should look like the error message bellow.
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TypeError: cannot convert the series to <class 'float'>
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Bellow is a number of tested solutions that I have tried and have worked for me.
Solution 1 : use the lambda operator
The issue can be solved using the lambda operator, the values on the column are taken as a variable and the lambda operator will return the values after applying the chosen changes to each one of them.
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df['VRRAM'].apply(lambda i: float(i))
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In the example above the values of the column will be returned back as float values.
This solution should be enough to solve the error please try the second and last solution if this method did not work for you.
Solution 2 : use numpy.log instead of using Math.log.
The error can also occur after using math.log which does not work for arrays and only works with single floats/numbers.
The solution is to use numpy.log instead of using Math.log.
numpy.log is the natural logarithm log, which is the inverse of the exponential function. For example :
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numpy.log(4**4) # this will output 5.54
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The solutions above should be enough to solve the problem, if you like our effort make sure to consider donating to our Kofi account, there is a red button that you can use if you are feeling generous.
Summing-up
I hope this article has helped you achieve your objective, If you like the effort we did here, please consider donating to our Kofi account located at the top of this page.
Keep coding, cheers. If you want to learn more about Python, please check out the Python Documentation : https://docs.python.org/3/