麻烦传入 lambda 以申请 Pandas DataFrame [英] Trouble passing in lambda to apply for pandas DataFrame

问题描述

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 trying to apply a function to all rows of a pandas DataFrame (actually just one column in that DataFrame)

我确定这是一个语法错误，但我知道我做错了什么

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)

col 列包含一堆 datetime.datetime 对象，d1 是其中最早的一个.我正在尝试获取每一行的总秒数列

The col column contains a bunch a datetime.datetime objects and 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

编辑我不断收到以下错误

TypeError: <lambda>() got an unexpected keyword argument 'axis'

我不明白为什么 axis 会传递给我的 lambda 函数

I don't understand why axis is getting passed to my lambda function

编辑 2

我也试过

def diff_dates(d1, d2):
    return (d1-d2).total_seconds()

df['col'].apply(diff_dates, args=[d1], axis=1)

我也遇到同样的错误

推荐答案

注意 Series.apply 调用，区别于 DataFrame.apply 打电话.

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)

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

有一个用于 df 但不清楚当你在一个系列中调用它时你期望它如何工作，但你期望它连续工作?

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?

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