我有一个这样的 df:
col1 col2
[1,3,4,5] [3,3,6,2]
[1,4,5,5] [3,8,4,3]
[1,3,4,8] [8,3,7,2]
尝试将 col1 和 col2 中的列表中的元素划分在一起以获得结果列中的内容:col1 col2 result
[1,3,4,5] [3,3,6,2] [.33,1,.66,2.5]
[1,4,5,5] [3,8,4,3] [.33,.5,1.25,1.66]
[1,3,4,8] [8,3,7,2] [.33,1,.57,4]
尝试了很多不同的方法 - 但总是出错。 尝试 :
#attempt1
df['col1'].div(df['col2'], axis=0)
#attempt2
from operator import truediv
for i in df.col1:
a = np.array(df['col1'])
for t in df.col2:
b = np.array(df['col2'])
x = a/b
print(x)
#attempt3
for i in df.index:
a = col1
b = col2
x = map(truediv, a, b)
#attempt4
a = col1
b = col2
result = [x/y for x, y in zip(a, b)]
#then apply to df
#attempt5
a = col1
b = col2
result = a/b
print(percent_matched)
#then #apply to df
>>>TypeError: unsupported operand type(s) for /: 'list' and 'list'
有任何想法吗?
最佳答案
您可以将列表理解与应用一起使用,这取决于两个列表的长度相同
df['result'] = df.apply(lambda x: [np.round(x['col1'][i]/x['col2'][i], 2) for i in range(len(x['col1']))], axis = 1)
col1 col2 result
0 [1, 3, 4, 5] [3, 3, 6, 2] [0.33, 1.0, 0.67, 2.5]
1 [1, 4, 5, 5] [3, 8, 4, 3] [0.33, 0.5, 1.25, 1.67]
2 [1, 3, 4, 8] [8, 3, 7, 2] [0.12, 1.0, 0.57, 4.0]
编辑:正如@TrentonMcKinney 所建议的,这可以在不使用 LC 的情况下完成。该解决方案利用了 Numpy 的矢量化操作,df.apply(lambda x: np.round(np.array(x[0]) / np.array(x[1]), 3), axis=1)
关于python - 将两列 Pandas 列表彼此分开,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/63587821/