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.
You can use numpy.log instead. Math.log is expecting a single number, not array.
You can use lambda operator to apply your functions to the pandas data frame or to the series. More specifically if you want to convert each element on a column to a floating point number, you should do it like this:
df['A'].apply(lambda x: float(x))
here the lambda operator will take the values on that column (as x) and return them back as a float value.
If you just write df["A"].astype(float) you will not change df. You would need to assign the output of the astype method call to something else, including to the existing series using df['A'] = df['A'].astype(float). Also you might want to either use numpy as @user3582076 suggests, or use .apply on the Series that results from dividing today's value by yesterday's.