Python 麻烦传入lambda来申请pandas DataFrame
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Trouble passing in lambda to apply for pandas DataFrame
提问by sedavidw
I'm trying to apply a function to all rows of a pandas DataFrame (actually just one column in that DataFrame)
我正在尝试将一个函数应用于 Pandas DataFrame 的所有行（实际上只是该 DataFrame 中的一列）
I'm sure this is a syntax error but I'm know sure what I'm doing wrong
我确定这是一个语法错误，但我确定我做错了什么
df['col'].apply(lambda x, y:(x - y).total_seconds(), args=[d1], axis=1)
The colcolumn contains a bunch a datetime.datetimeobjects and and d1is the earliest of them. I'm trying to get a column of the total number of seconds for each of the rows
该col列包含一堆datetime.datetime对象，并且d1是其中最早的对象。我正在尝试获取每一行的总秒数列
EDITI keep getting the following error
编辑我不断收到以下错误
TypeError: <lambda>() got an unexpected keyword argument 'axis'
I don't understand why axisis getting passed to my lambdafunction
我不明白为什么axis要传递给我的lambda函数
EDIT 2
编辑 2
I've also tried doing
我也试过做
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
我得到了同样的错误
采纳答案by EdChum
Note there is no axisparam for a Series.applycall, as distinct to a DataFrame.applycall.
请注意，call没有axis参数，这与Series.applycall不同DataFrame.apply。
Series.apply(func, convert_dtype=True, args=(), **kwds)
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
There is one for a dfbut 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?
df有一个，但不清楚当你在一个系列上调用它时你期望它如何工作，但你期望它连续工作？
