python - TypeError: cannot convert the series to <class 'float'>

20
votes

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.

5
pythonpython-3.x
Check if there are any non float values like empty strings or strings with something other than numbers - GlacierSG
math.log expects a single float value. It doesn't work on pandas Series objects. - Craig
can you try to convert just a small portion of the data to float and see if that works - GlacierSG
why not df["B"] = (df["A"] / df["A"].shift(1)).apply(lambda x: math.log(x))? - plasmon360

5 Answers

44
votes

You can use numpy.log instead. Math.log is expecting a single number, not array.

15
votes

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.

1
votes

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.

0
votes

I had the same issue, for me the answer was to look at the cause of why I had series in the first place. After looking for a long time about how to change the series into the different assigned data type, I realised that I had defined the same column name twice in the dataframe and that was why I had a series.

Removing the accidental duplication of column name removes this issue :)

0
votes

I used in a different way but it is same as @cemosambora

(df.A).apply(lambda x: float(x)) Here, df is the pandas dataframe and A is a column name