Python Pandas 合并数据框中的同名列
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Python Pandas merge samed name columns in a dataframe
提问by Wizuriel
So I have a few CSV files I'm trying to work with, but some of them have multiple columns with the same name.
所以我有几个 CSV 文件我正在尝试使用，但其中一些有多个同名的列。
For example I could have a csv like this:
例如，我可以有一个这样的 csv：
ID Name a a a b b
1 test1 1 NaN NaN "a" NaN
2 test2 NaN 2 NaN "a" NaN
3 test3 2 3 NaN NaN "b"
4 test4 NaN NaN 4 NaN "b"
loading into pandasis giving me this:
加载到 pandasis 给我这个：
ID Name a a.1 a.2 b b.1
1 test1 1 NaN NaN "a" NaN
2 test2 NaN 2 NaN "a" NaN
3 test3 2 3 NaN NaN "b"
4 test4 NaN NaN 4 NaN "b"
What I would like to do is merge those same name columns into 1 column (if there are multiple values keeping those values separate) and my ideal output would be this
我想要做的是将这些相同名称的列合并为 1 列（如果有多个值将这些值分开），我的理想输出是这样的
ID Name a b
1 test1 "1" "a"
2 test2 "2" "a"
3 test3 "2;3" "b"
4 test4 "4" "b"
So wondering if this is possible?
所以想知道这是否可能？
回答by DSM
You could use groupbyon axis=1, and experiment with something like
你可以使用groupbyon axis=1，并尝试类似的东西
>>> def sjoin(x): return ';'.join(x[x.notnull()].astype(str))
>>> df.groupby(level=0, axis=1).apply(lambda x: x.apply(sjoin, axis=1))
ID Name a b
0 1 test1 1.0 a
1 2 test2 2.0 a
2 3 test3 2.0;3.0 b
3 4 test4 4.0 b
where instead of using .astype(str), you could use whatever formatting operator you wanted.
.astype(str)您可以使用您想要的任何格式运算符而不是使用。
回答by CT Zhu
Probably it is not a good idea to have duplicated column names, but it will work:
列名重复可能不是一个好主意，但它会起作用：
In [72]:
df2=df[['ID', 'Name']]
df2['a']='"'+df.T[df.columns.values=='a'].apply(lambda x: ';'.join(["%i"%item for item in x[x.notnull()]]))+'"' #these columns are of float dtype
df2['b']=df.T[df.columns.values=='b'].apply(lambda x: ';'.join([item for item in x[x.notnull()]])) #these columns are of objects dtype
print df2
ID Name a b
0 1 test1 "1" "a"
1 2 test2 "2" "a"
2 3 test3 "2;3" "b"
3 4 test4 "4" "b"
[4 rows x 4 columns]
回答by Paul H
Of course DSM and CT Zhu have marvelously concise answers that utilize a lot built in features of Python in general and dataframe in particular. Here's something a little -- [cough] -- verbose.
当然，DSM 和 CT Zhu 有非常简洁的答案，它们利用了 Python 的许多内置特性，特别是数据帧。这里有点——[咳嗽]——冗长。
def myJoiner(row):
newrow = []
for r in row:
if not pandas.isnull(r):
newrow.append(str(r))
return ';'.join(newrow)
def groupCols(df, key):
columns = df.select(lambda col: key in col, axis=1)
joined = columns.apply(myJoiner, axis=1)
joined.name = key
return pandas.DataFrame(joined)
import pandas
from io import StringIO # python 3.X
#from StringIO import StringIO #python 2.X
data = StringIO("""\
ID Name a a a b b
1 test1 1 NaN NaN "a" NaN
2 test2 NaN 2 NaN "a" NaN
3 test3 2 3 NaN NaN "b"
4 test4 NaN NaN 4 NaN "b"
""")
df = pandas.read_table(data, sep='\s+')
df.set_index(['ID', 'Name'], inplace=True)
AB = groupCols(df, 'a').join(groupCols(df, 'b'))
print(AB)
Which gives me:
这给了我：
a b
ID Name
1 test1 1.0 a
2 test2 2.0 a
3 test3 2.0;3.0 b
4 test4 4.0 b
