Question

[Solved] TypeError: cannot convert the series to

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.

Enquirer: Stacey

||

Solution #1:

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

Respondent: user3582076

Solution #2:

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.

Respondent: cemosambora

Solution #3:

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.

Respondent: William Welsh

Solution #4:

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 🙂

Respondent: Laura Baker

Solution #5:

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

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