[英]pandas dataframe how to remove values from cell that is a list based on other column
I have a dataframe with 2 columns that represent a list:我有一个 dataframe,其中 2 列代表一个列表:
a. b. vals. locs
1. 2. [1,2,3,4,5]. [2,3]
5 1. [1,7,2,4,9]. [0,1]
8. 2. [1,9,4,7,8]. [3]
I want, for each row, exclude from the columns vals all the locations that are in locs.我希望对于每一行,从 vals 列中排除 locs 中的所有位置。 so I will get:
所以我会得到:
a. b. vals. locs. new_vals
1. 2. [1,2,3,4,5]. [2,3]. [1,2,5]
5 1. [1,7,2,4,9]. [0,1]. [2,4,9]
8. 2. [1,9,4,7,8]. [3]. [1,9,4,8]
What is the best way to do so?最好的方法是什么?
Thanks!谢谢!
You can use a list comprehension with an internal filter based on enumerate
:您可以将列表理解与基于
enumerate
的内部过滤器结合使用:
df['new_vals'] = [[v for i,v in enumerate(a) if i not in b]
for a,b in zip(df['vals'], df['locs'])]
however this will become quickly inefficient when b get large.然而,当 b 变大时,这将很快变得低效。
A much better approach would be to use python sets that enable a fast (O(1) complexity) identification of membership:一个更好的方法是使用python 集来实现快速(O(1) 复杂度)成员身份识别:
df['new_vals'] = [[v for i,v in enumerate(a) if i not in S]
for a,b in zip(df['vals'], df['locs']) for S in [set(b)]]
output: output:
a b vals locs new_vals
0 1 2 [1, 2, 3, 4, 5] [2, 3] [1, 2, 5]
1 5 1 [1, 7, 2, 4, 9] [0, 1] [2, 4, 9]
2 8 2 [1, 9, 4, 7, 8] [3] [1, 9, 4, 8]
Use list comprehension with enumerate
and converting values to set
s:将列表理解与
enumerate
和转换值一起使用以set
s:
df['new_vals'] = [[z for i, z in enumerate(x) if i not in y]
for x, y in zip(df['vals'], df['locs'].apply(set))]
print (df)
a b vals locs new_vals
0 1 2 [1, 2, 3, 4, 5] [2, 3] [1, 2, 5]
1 5 1 [1, 7, 2, 4, 9] [0, 1] [2, 4, 9]
2 8 2 [1, 9, 4, 7, 8] [3] [1, 9, 4, 8]
One way to do this is to create a function that works on row,一种方法是创建一个适用于行的 function,
def func(row):
ans = [v for v in row['vals'] if row['vals'].index(v) not in row['locs']]
return ans
The call this function for each row using apply.使用 apply 为每一行调用此 function。
df['new_value'] = df.apply(func, axis=1)
This will work well, if the lists are short.如果列表很短,这会很有效。
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