在具有不同产出的数据框架内,我有以下一栏:
col1
MLB|NBA|NFL
MLB|NBA
NFL|NHL|NBA|MLB
我愿利用分管职能,将一栏按最后管道分开,这样:
col1 col2
MLB|NBA NFL
MLB NBA
NFL|NHL|NBA MLB
在具有不同产出的数据框架内,我有以下一栏:
col1
MLB|NBA|NFL
MLB|NBA
NFL|NHL|NBA|MLB
我愿利用分管职能,将一栏按最后管道分开,这样:
col1 col2
MLB|NBA NFL
MLB NBA
NFL|NHL|NBA MLB
缩略语
df[ col1 ].str.rsplit( | , n=1, expand=True).rename(columns=lambda x: f col{x + 1} )
If the above throws you a SyntaxError, it means you re on a python version older than 3.6 (shame on you!). Use instead
df.col1.str.rsplit( | , 1, expand=True)
.rename(columns=lambda x: col{} .format(x + 1))
col1 col2
0 MLB|NBA NFL
1 MLB NBA
2 NFL|NHL|NBA MLB
There s also the faster loopy str.rsplit
equivalent.
pd.DataFrame(
[x.rsplit( | , 1) for x in df[ col1 ].tolist()],
columns=[ col1 , col2 ]
)
col1 col2
0 MLB|NBA NFL
1 MLB NBA
2 NFL|NHL|NBA MLB
P.S., yes, the second solution is faster:
df = pd.concat([df] * 100000, ignore_index=True)
%timeit df[ col1 ].str.rsplit( | , n=1, expand=True)
%timeit pd.DataFrame([x.rsplit( | , 1) for x in df[ col1 ].tolist()])
473 ms ± 13.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
128 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
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