在列表中查找列表索引的优雅方法？
在列表中查找列表索引的优雅方法？
提问于 2019-04-30 07:24:52
对python来说还是个新手。我正在尝试按行/列索引CSV文件中的项目。我发现的唯一方法是实现一个for循环来搜索列表中的每一行。
readCSV = [['', 'A', 'B', 'C', 'D'],
[1.0, 3.1, 5.0, 1.7, 8.2],
[2.0, 6.2, 7.0, 2.2, 9.3],
[3.0, 8.8, 5.5, 4.4, 6.0]]
row_column = []
for row in readCSV:
if my_item in row:
row_column.append(row[0])
row_column.append(readCSV[0][row.index(my_item)])所以对于my_item = 6.2，我得到了row_column = [2.0, 'A']。
这很好用，但我不禁想到还有一个更优雅的解决方案。
回答 2
Stack Overflow用户
回答已采纳
发布于 2019-04-30 15:27:02
试试这个：
result = [(i, j) for i, k in enumerate(readCSV) for j, n in enumerate(k) if my_item == n]Stack Overflow用户
发布于 2019-04-30 16:05:58
import pandas as pd
import numpy as np
df = pd.DataFrame(readCSV[1:],columns=readCSV[0])
#### Output ####
No A B C D
0 1.0 3.1 5.0 1.7 8.2
1 2.0 6.2 7.0 2.2 9.3
2 3.0 8.8 5.5 4.4 6.0
##This provides the row in which there is a hit.
df1 = df[(df.A == my_item) | (df.B == my_item) |(df.C == my_item) | (df.D == my_item)]
print(df1)
#### Output ####
No A B C D
1 2.0 6.2 7.0 2.2 9.3
##If you want only those column values which is a hit for your my_item.
z1 = pd.concat([df[df['A'] == my_item][['No','A']],df[df['B'] == my_item][['No','B']],df[df['C'] == my_item][['No','C']],df[df['D'] == my_item][['No','D']]])
print(z1)
#### Output ####
A B C D No
1 6.2 NaN NaN NaN 2.0
## Incase if you want drop the nan , you can use np.isnan
z1 = np.array(z1)
print(z1[:,~np.any(np.isnan(z1), axis=0)])
#### Output ####
[[6.2 2. ]]页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/55912007
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