Question

我是Stack Overflow的新手，遇到了一个我无法解决的问题。我有一个csv文件或excel（基本上是一个表），并希望在Python 3中执行以下操作：

Column Header,"r269_d","r295_A","r295_R","r299_A","r325_D","r326_A"

id1,"0.0","2.29","0.0","1.3","0.0","188.4"

id2,"0.0","1.0","0.0","0.6","0.0","0.0"

对于这个csv文件，我想：

进入第一行（id1）
检查第1列（r269_d）

2.1如果col1 = 0的值将0写入新的result_string

2.2如果col1！= 0的值将1写入新的result_string
检查第2列（r295_A）

3.1如果col2 = 0的值将0写入与2.1
中提到的相同的result_string
3.2如果col2！= 0的值将1写入2.1中提到的相同result_string
为所有列执行此操作
转到下一行并执行相同操作。

最后我希望看到这样的东西：

Column Header,"r269_d","r295_A","r295_R","r299_A","r325_D","r326_A", "result_string"

id1,"0.0","2.29","0.0","1.3","0.0","188.4","010101"

id2,"0.0","1.0","0.0","0.6","0.0","0.0","010100"

Answer 1

熊猫解决方案：

import pandas as pd
import numpy as np

df = pd.read_csv(r'/path/to/file.csv')

df['result_string'] = (df.filter(regex='r\d+')
                         .ne(0).astype(np.int8).astype(str)
                         .apply(''.join, axis=1))

df.to_csv(r'/path/to/result.csv', index=False)

源CSV文件：

col,r269_d,r295_A,r295_R,r299_A,r325_D,r326_A
id1,0.0,2.29,0.0,1.3,0.0,188.4
id2,0.0,1.0,0.0,0.6,0.0,0.0

解析DF：

In [169]: df
Out[169]:
   col  r269_d  r295_A  r295_R  r299_A  r325_D  r326_A
0  id1     0.0    2.29     0.0     1.3     0.0   188.4
1  id2     0.0    1.00     0.0     0.6     0.0     0.0

结果：

In [170]: df['result_string'] = (df.filter(regex='r\d+')
     ...:                          .ne(0).astype(np.int8).astype(str)
     ...:                          .apply(''.join, axis=1))
     ...:

In [171]: df
Out[171]:
   col  r269_d  r295_A  r295_R  r299_A  r325_D  r326_A result_string
0  id1     0.0    2.29     0.0     1.3     0.0   188.4        010101
1  id2     0.0    1.00     0.0     0.6     0.0     0.0        010100

In [172]: df.to_csv(r'c:/temp/result.csv', index=False)

产生的CSV：

col,r269_d,r295_A,r295_R,r299_A,r325_D,r326_A,result_string
id1,0.0,2.29,0.0,1.3,0.0,188.4,010101
id2,0.0,1.0,0.0,0.6,0.0,0.0,010100

迭代csv / xlsx文件python 3中的行和列

1 个答案: