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I have a dataframe (df) that looks like:
date A 2001-01-02 1.0022 2001-01-03 1.1033 2001-01-04 1.1496 2001-01-05 1.1033 2015-03-30 126.3700 2015-03-31 124.4300 2015-04-01 124.2500 2015-04-02 124.8900 For the entire time-series I'm trying to divide today's value by yesterdays and log the result using the following:
df["B"] = math.log(df["A"] / df["A"].shift(1)) However I get the following error:
TypeError: cannot convert the series to <class 'float'> How can I fix this? I've tried to cast as float using:
df["B"] .astype(float) But can't get anything to work.
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We can convert a string to float in Python using the float() function.
The Python "ValueError: could not convert string to float" occurs when we pass a string that cannot be converted to a float (e.g. an empty string or one containing characters) to the float() class. To solve the error, remove all unnecessary characters from the string.
Use pandas DataFrame. astype() function to convert column from string/int to float, you can apply this on a specific column or on an entire DataFrame. To cast the data type to 54-bit signed float, you can use numpy. float64 , numpy.
You can use numpy.log instead. Math.log is expecting a single number, not array.
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