Pandas：如何将具有多个值的单元格转换为多行？
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Pandas: how to convert a cell with multiple values to multiple rows?
提问by UserYmY
I have a DataFrame like this:
我有一个像这样的数据帧：
Name asn count
Org1 asn1,asn2 1
org2 asn3 2
org3 asn4,asn5 5
I would like to convert my DataFrame to look like this:
我想将我的 DataFrame 转换为如下所示：
Name asn count
Org1 asn1 1
Org1 asn2 1
org2 asn3 2
org3 asn4 5
Org3 asn5 5
I know used the following code to do it with two columns, but I am not sure how can I do it for three.
我知道使用以下代码对两列执行此操作，但我不确定如何为三列执行此操作。
df2 = df.asn.str.split(',').apply(pd.Series)
df2.index = df.Name
df2 = df2.stack().reset_index('Name')
Can anybody help?
有人可以帮忙吗？
回答by Alex Riley
Carrying on from the same idea, you could set a MultiIndex for df2and then stack. For example:
按照同样的想法，您可以设置一个 MultiIndex df2，然后堆叠。例如：
>>> df2 = df.asn.str.split(',').apply(pd.Series)
>>> df2.index = df.set_index(['Name', 'count']).index
>>> df2.stack().reset_index(['Name', 'count'])
Name count 0
0 Org1 1 asn1
1 Org1 1 asn2
0 org2 2 asn3
0 org3 5 asn4
1 org3 5 asn5
You can then rename the column and set an index of your choosing.
然后您可以重命名该列并设置您选择的索引。
回答by Vor
As an alternative:
作为备选：
import pandas as pd
from StringIO import StringIO
ctn = '''Name asn count
Org1 asn1,asn2 1
org2 asn3 2
org3 asn4,asn5 5'''
df = pd.read_csv(StringIO(ctn), sep='\s*', engine='python')
s = df['asn'].str.split(',').apply(pd.Series, 1).stack()
s.index = s.index.droplevel(-1)
s.name = 'asn'
del df['asn']
df = df.join(s)
print df
Result:
结果：
Name count asn
0 Org1 1 asn1
0 Org1 1 asn2
1 org2 2 asn3
2 org3 5 asn4
2 org3 5 asn5
