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问使用pandas将多行不同的字符串合并为一个字符串
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Stack Overflow用户

提问于 2021-08-18 14:17:24

回答 2查看 48关注 0票数 0

我有一个有多行的数据帧。有没有什么方法可以把它们组合成一行？标有黄色的。我只想把它们合并成一行。请记住，当您合并时，我希望忽略空行。参见附加图像的输出部分(“Problem.jpg”)。

Problem

我希望我的输出是这样的。

Output

我找不到这个问题的逻辑。有什么想法吗？

已尝试此代码。但它不起作用。

import pandas as pd
all_dfs_1 = pd.read_csv("Test.csv",header=None)
all_dfs_1.groupby(0)[1].apply(' '.join).reset_index()

附加文件：- Test.csv

python

pandas

dataframe

回答 2

Stack Overflow用户

发布于 2021-08-18 14:30:07

如果将"Drilling good ground all shift“移动到最左侧的列，使文件看起来如下所示：

Drilling good ground all shift
2 x Gyro Surveys
Mixing muds to condition the hole
Driller travelled home for shift change at end of shift

Equipment onsite=

然后我相信您需要结合使用','.join(array)和split(',,', ',')来去掉空行，如下所示：

>>> import numpy as np
>>> data = np.loadtxt('Test.csv')
>>> data
array(['Drilling good ground all shift', '2 x Gyro Surveys',
       'Mixing muds to condition the hole',
       'Driller travelled home for shift change at end of shift', '',
       'Equipment onsite='], dtype='<U55')
>>> ','.join(data).replace(',,', ',')
'Drilling good ground all shift,2 x Gyro Surveys,Mixing muds to condition the hole,Driller travelled home for shift change at end of shift,Equipment onsite='

如果您不想手动更改Test.csv，可以使用Pandas执行此操作，将其转换为数组，然后按照上面的步骤进行操作：

>>> import pandas as pd
>>> all_dfs_1 = pd.read_csv(r"Test.csv", header=None)
>>> all_dfs_1
                                                  0                               1   2   3   4   ...  7   8   9   10  11
0                        Comments & Equip. Transfers  Drilling good ground all shift NaN NaN NaN  ... NaN NaN NaN NaN NaN
1                                   2 x Gyro Surveys                             NaN NaN NaN NaN  ... NaN NaN NaN NaN NaN
2                  Mixing muds to condition the hole                             NaN NaN NaN NaN  ... NaN NaN NaN NaN NaN
3  Driller travelled home for shift change at end...                             NaN NaN NaN NaN  ... NaN NaN NaN NaN NaN
4                                                NaN                             NaN NaN NaN NaN  ... NaN NaN NaN NaN NaN
5                                  Equipment onsite=                             NaN NaN NaN NaN  ... NaN NaN NaN NaN NaN

[6 rows x 12 columns]
>>> all_dfs_1.iloc[0, 0] = all_dfs_1.iloc[0, 1]
>>> all_dfs_1[0]
0                       Drilling good ground all shift
1                                     2 x Gyro Surveys
2                    Mixing muds to condition the hole
3    Driller travelled home for shift change at end...
4                                                  NaN
5                                    Equipment onsite=
Name: 0, dtype: object
>>> data = all_dfs_1[0].values
>>> data
array(['Drilling good ground all shift', '2 x Gyro Surveys',
       'Mixing muds to condition the hole',
       'Driller travelled home for shift change at end of shift', '',
       'Equipment onsite='], dtype='<U55')
>>> ','.join(data).replace(',,', ',')
'Drilling good ground all shift,2 x Gyro Surveys,Mixing muds to condition the hole,Driller travelled home for shift change at end of shift,Equipment onsite='

票数 1

Stack Overflow用户

发布于 2021-08-18 14:31:06

不确定这是否是您想要的，它返回一个序列，该序列是数据框中的单个行，所有这些值连接在一起

import pandas as pd
import io

#"reads in" the csv file from a string so it can be tested without the file
all_dfs_1 = pd.read_csv(
    io.StringIO(
"""
Comments & Equip. Transfers
2 x Gyro Surveys
Mixing muds to condition the hole
Driller travelled home for shift change at end of shift

Equipment onsite=
"""
    ), 
    header=None
)

#you'll want to do this instead since you have the file
#all_dfs_1 = pd.read_csv("Test.csv",header=None)


single_row = all_dfs_1.apply(lambda v: ','.join(v))
print(single_row)

输出为

0    Comments & Equip. Transfers,2 x Gyro Surveys,M...
dtype: object

如果你只是想要一个字符串，你也可以这样做：

','.join(all_dfs_1[0].values)

票数 0

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/68834138

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