Python Pandas 用逗号将列分成多列
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/37600711/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas split column into multiple columns by comma
提问by Anekdotin
I am trying to split a column into multiple columns based on comma/space separation.
我正在尝试根据逗号/空格分隔将一列拆分为多列。
My dataframe currently looks like
我的数据框目前看起来像
KEYS 1
0 FIT-4270 4000.0439
1 FIT-4269 4000.0420, 4000.0471
2 FIT-4268 4000.0419
3 FIT-4266 4000.0499
4 FIT-4265 4000.0490, 4000.0499, 4000.0500, 4000.0504,
I would like
我想
KEYS 1 2 3 4
0 FIT-4270 4000.0439
1 FIT-4269 4000.0420 4000.0471
2 FIT-4268 4000.0419
3 FIT-4266 4000.0499
4 FIT-4265 4000.0490 4000.0499 4000.0500 4000.0504
My code currently removes The KEYS column and I'm not sure why. Could anyone improve or help fix the issue?
我的代码目前删除了 KEYS 列，我不知道为什么。任何人都可以改进或帮助解决问题吗？
v = dfcleancsv[1]
#splits the columns by spaces into new columns but removes KEYS?
dfcleancsv = dfcleancsv[1].str.split(' ').apply(Series, 1)
回答by Anthony R
In case someone else wants to split a single column (deliminated by a value) into multiple columns - try this:
如果其他人想要将单列（由值分隔）拆分为多列 - 试试这个：
series.str.split(',', expand=True)
This answered the question I came here looking for.
这回答了我来这里寻找的问题。
Credit to EdChum'scode that includes adding the split columns back to the dataframe.
感谢EdChum的代码，包括将分离列返回到数据帧。
pd.concat([df[[0]], df[1].str.split(', ', expand=True)], axis=1)
Note: The first argument df[[0]]is DataFrame.
注意：第一个参数df[[0]]是DataFrame.
The second argument df[1].str.splitis the series that you want to split.
第二个参数df[1].str.split是您要拆分的系列。
回答by Anekdotin
Using Edchums answer of
使用 Edchums 的答案
pd.concat([df[[0]], df[1].str.split(', ', expand=True)], axis=1)
I was able to solve it by substituting my variables.
我能够通过替换我的变量来解决它。
dfcleancsv = pd.concat([dfcleancsv['KEYS'], dfcleancsv[1].str.split(', ', expand=True)], axis=1)
回答by Siraj S.
maybe this should work:
也许这应该有效：
df = pd.concat([df['KEYS'],df[1].apply(pd.Series)],axis=1)
回答by Paul Rougieux
The OP had a variable number of output columns. In the particular case of a fixed number of output columns another elegant solution to give name to the resulting columns is to use a multiple assignation
OP 具有可变数量的输出列。在固定数量的输出列的特殊情况下，另一个给结果列命名的优雅解决方案是使用多重分配
Load a sample dataset and reshape it to long format to obtain a variable
called organ_dimension.
加载示例数据集并将其整形为长格式以获得名为 的变量organ_dimension。
import seaborn
iris = seaborn.load_dataset('iris')
df = iris.melt(id_vars='species', var_name='organ_dimension', value_name='value')
Split the organ_dimensionvariable in 2 variables organand dimensionbased on the _separator.
Based on this answer"How to split a column into two columns?"
拆分organ_dimension变量2个变量organ并dimension基于该_分离器。基于这个答案“如何将一列拆分为两列？”
df['organ'], df['dimension'] = df['organ_dimension'].str.split('_', 1).str
df.head()
Out[10]:
species organ_dimension value organ dimension
0 setosa sepal_length 5.1 sepal length
1 setosa sepal_length 4.9 sepal length
2 setosa sepal_length 4.7 sepal length
3 setosa sepal_length 4.6 sepal length
4 setosa sepal_length 5.0 sepal length
回答by yafomars
Better and fester using vectorization below as :
使用下面的矢量化更好和更糟：
df = df.apply(lambda x:pd.Series(x))
回答by Kanishk Arya
Check this out
看一下这个
Responder_id LanguagesWorkedWith
0 1 HTML/CSS;Java;JavaScript;Python
1 2 C++;HTML/CSS;Python
2 3 HTML/CSS
3 4 C;C++;C#;Python;SQL
4 5 C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA
... ... ...
87564 88182 HTML/CSS;Java;JavaScript
87565 88212 HTML/CSS;JavaScript;Python
87566 88282 Bash/Shell/PowerShell;Go;HTML/CSS;JavaScript;W...
87567 88377 HTML/CSS;JavaScript;Other(s):
87568 88863 Bash/Shell/PowerShell;HTML/CSS;Java;JavaScript...`
###Split the LanguagesWorkedWith column into multiple columns by using` data= data1['LanguagesWorkedWith'].str.split(';').apply(pd.Series)`.###
` data1 = pd.read_csv('data.csv', sep=',')
data1.set_index('Responder_id',inplace=True)
data1
data1.loc[1,:]
data= data1['LanguagesWorkedWith'].str.split(';').apply(pd.Series)
data.head()`
