从pandas数据框到元组(用于Haversine模块) [英] From pandas dataframe to tuples (for haversine module)

问题描述

我有一个熊猫数据框my_df，其中包含以下列:

I have a pandas dataframe my_df with the following columns :

id  lat1 lon1 lat2 lon2
1   45   0    41   3
2   40   1    42   4
3   42   2    37   1

基本上，我想执行以下操作:

Basically, I'd like to do the following :

import haversine

haversine.haversine((45, 0), (41, 3)) # just to show syntax of haversine()
> 507.20410687342115

# what I'd like to do
my_df["dist"] = haversine.haversine((my_df["lat1"], my_df["lon1"]),(my_df["lat2"], my_df["lon2"]))

TypeError:无法将系列转换为<类'float'>

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

使用此，我尝试了以下操作:

Using this, I tried the following :

my_df['dist'] = haversine.haversine(
        list(zip(*[my_df[['lat1','lon1']][c].values.tolist() for c in my_df[['lat1','lon1']]]))
        , 
        list(zip(*[my_df[['lat2','lon2']][c].values.tolist() for c in my_df[['lat2','lon2']]]))
        )

haversine中的文件"blabla \ lib \ site-packages \ haversine__init __.py"，第20行 lat1，lng1 = point1

File "blabla\lib\site-packages\haversine__init__.py", line 20, in haversine lat1, lng1 = point1

ValueError:太多值无法解包(预期2)

ValueError: too many values to unpack (expected 2)

关于我做错了什么/如何实现自己想要的一切的任何想法?

Any idea of what I'm doing wrong / how I can achieve what I want ?

推荐答案

将apply与axis=1结合使用:

my_df["dist"] = my_df.apply(lambda row : haversine.haversine((row["lat1"], row["lon1"]),(row["lat2"], row["lon2"])), axis=1)

要在每一行上调用Haversine函数，该函数可以理解标量值，而不是类似数组的值，因此会出现错误.通过用axis=1调用apply，您可以逐行进行迭代，这样我们就可以访问每个列值，并以该方法期望的形式传递这些值.

To call the haversine function on each row, the function understands scalar values, not array like values hence the error. By calling apply with axis=1, you iterate row-wise so we can then access each column value and pass these in the form that the method expects.

我也不知道有什么区别，但是有一个向量化的 Haversine公式的版本

Also I don't know what the difference is but there is a vectorised version of the haversine formula

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