python pandas将两行或更多行文本合并为一行 [英] python pandas merge two or more lines of text into one line

问题描述

我的数据框包含以下文本数据，

    name | address                  | number 
1   Bob    bob                        No.56
2          @gmail.com           
3   Carly  [e-mail]            No.90
4   Gorge  greg@yahoo     
5          .com                   
6                                     No.100

并希望使其像这样的框架.

    name | address               | number 
1   Bob    [e-mail]           No.56
2   Carly  [e-mail]         No.90                 
3   Gorge  [e-mail]          No.100

我正在使用熊猫读取文件，但不确定如何使用merge或concat.

解决方案

如果name列包含唯一值，

print df

    name          address  number
0    Bob              bob   No.56
1    NaN       @gmail.com     NaN
2  Carly  [e-mail]   No.90
3  Gorge       greg@yahoo     NaN
4    NaN             .com     NaN
5    NaN              NaN  No.100

df['name'] = df['name'].ffill()
print df.fillna('').groupby(['name'], as_index=False).sum()

    name          address  number
0    Bob    [e-mail]   No.56
1  Carly  [e-mail]   No.90
2  Gorge   [e-mail]  No.100

您可能需要ffill()，bfill()，[::-1]，.groupby('name').apply(lambda x: ' '.join(x['address']))，strip()，lstrip()，rstrip()，replace()之类的东西，才能将上述代码扩展为更复杂的数据. /p>

I have data frame with text data like below,

    name | address                  | number 
1   Bob    bob                        No.56
2          @gmail.com           
3   Carly  [e-mail]            No.90
4   Gorge  greg@yahoo     
5          .com                   
6                                     No.100

and want to make it like this frame.

    name | address               | number 
1   Bob    [e-mail]           No.56
2   Carly  [e-mail]         No.90                 
3   Gorge  [e-mail]          No.100

I am using pandas to read file but not sure how to use merge or concat.

解决方案

In case of name column consists of unique values,

print df

    name          address  number
0    Bob              bob   No.56
1    NaN       @gmail.com     NaN
2  Carly  [e-mail]   No.90
3  Gorge       greg@yahoo     NaN
4    NaN             .com     NaN
5    NaN              NaN  No.100

df['name'] = df['name'].ffill()
print df.fillna('').groupby(['name'], as_index=False).sum()

    name          address  number
0    Bob    [e-mail]   No.56
1  Carly  [e-mail]   No.90
2  Gorge   [e-mail]  No.100

you may need ffill(), bfill(), [::-1], .groupby('name').apply(lambda x: ' '.join(x['address'])), strip(), lstrip(), rstrip(), replace() kind of thing to extend above code to more complicated data.

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