["python","pandas","dataframe","lambda","apply"]
Trouble passing in lambda to apply for pandas DataFrame: "TypeError: <lambda>() got an unexpected keyword argument 'axis' "

I'm trying to apply a function to all rows of a pandas DataFrame (actually just one column in that DataFrame) I'm sure this is a syntax error but I'm know sure what I'm doing wrong df['col'].apply(lambda x, y:(x - y).total_seconds(), args=[d1], axis=1) The col column contains a bunch a datetime.datetime objects and d1 is the earliest of them. I'm trying to get a column of the total number of seconds for each of the rows. I keep getting the following error TypeError: <lambda>() got an unexpected keyword argument 'axis' I don't understand why axis is getting passed to my lambda function I've also tried doing def diff_dates(d1, d2): return (d1-d2).total_seconds() df['col'].apply(diff_dates, args=[d1], axis=1) And I get the same error.
Note there is no axis param for a Series.apply call, as distinct to a DataFrame.apply call. Series.apply(func, convert_dtype=True, args=(), **kwds) ... func : function convert_dtype : boolean, default True Try to find better dtype for elementwise function results. If False, leave as dtype=object args : tuple Positional arguments to pass to function in addition to the value **kwds Additional keyword arguments passed to func. There is one for a df but it's unclear how you're expecting this to work when you're calling it on a series but you're expecting it to work on a row?