我正在使用一个如下所示的资料框:
A B C D E F G H
ctg.s1.000000F_arrow CDS gene 21215 22825 0 . DAFEIOHN_00017
ctg.s1.000000F_arrow CDS gene 21215 22825 0 . DAFEIOHN_00017
ctg.s1.000000F_arrow CDS gene 64501 66033 0 - . DAFEIOHN_00049
ctg.s1.000000F_arrow CDS gene 70234 78846 0 . DAFEIOHN_00053
ctg.s1.000000F_arrow CDS gene 103455 106526 0 . DAFEIOHN_00074
ctg.s1.000000F_arrow CDS gene 161029 161712 0 . DAFEIOHN_00132
ctg.s1.000000F_arrow CDS gene 170711 171520 0 . DAFEIOHN_00142
ctg.s1.000000F_arrow CDS gene 203959 204450 0 - . DAFEIOHN_00174
ctg.s1.000000F_arrow CDS gene 211381 212196 0 . DAFEIOHN_00184
ctg.s1.000000F_arrow CDS gene 236673 238499 0 . DAFEIOHN_00209
ctg.s1.000000F_arrow CDS gene 533077 533850 0 . DAFEIOHN_00475
ctg.s1.000000F_arrow CDS gene 533995 535194 0 . DAFEIOHN_00572
ctg.s1.000000F_arrow CDS gene 641146 643083 0 . DAFEIOHN_00572
如您所见,H
列中有重复的元素,例如DAFEIOHN_00017
或DAFEIOHN_00572
。我想修改这个资料框以获得这样的东西:
A B C D E F G H I
ctg.s1.000000F_arrow CDS gene 21215 22825 0 . DAFEIOHN_00017 2
ctg.s1.000000F_arrow CDS gene 64501 66033 0 - . DAFEIOHN_00049 1
ctg.s1.000000F_arrow CDS gene 70234 78846 0 . DAFEIOHN_00053 1
ctg.s1.000000F_arrow CDS gene 103455 106526 0 . DAFEIOHN_00074 1
ctg.s1.000000F_arrow CDS gene 161029 161712 0 . DAFEIOHN_00132 1
ctg.s1.000000F_arrow CDS gene 170711 171520 0 . DAFEIOHN_00142 1
ctg.s1.000000F_arrow CDS gene 203959 204450 0 - . DAFEIOHN_00174 1
ctg.s1.000000F_arrow CDS gene 211381 212196 0 . DAFEIOHN_00184 1
ctg.s1.000000F_arrow CDS gene 236673 238499 0 . DAFEIOHN_00209 1
ctg.s1.000000F_arrow CDS gene 533077 533850 0 . DAFEIOHN_00475 1
ctg.s1.000000F_arrow CDS gene 533995 535194 0 . DAFEIOHN_00572 2
在第二个资料框中,重复元素仅显示一次,并且有一个新列I
,其中H
提供了该列中每个元素的出现次数。
我怎样才能做到这一点?
谢谢你。
uj5u.com热心网友回复:
您可以使用drop_duplicates
洗掉了在一个特定的列复制行,并使用assign
创建与组合回传值的新列groupby('H')
,并transform('count')
以确定的每个唯一值的计数H
:
df = df.drop_duplicates(subset='H').assign(I=df.groupby('H')['H'].transform('count'))
输出:
>>> df
A B C D E F G H I
0 ctg.s1.000000F_arrow CDS-gene 21215 22825 0 . DAFEIOHN_00017 2
2 ctg.s1.000000F_arrow CDS-gene 64501 66033 0 - . DAFEIOHN_00049 1
3 ctg.s1.000000F_arrow CDS-gene 70234 78846 0 . DAFEIOHN_00053 1
4 ctg.s1.000000F_arrow CDS-gene 103455 106526 0 . DAFEIOHN_00074 1
5 ctg.s1.000000F_arrow CDS-gene 161029 161712 0 . DAFEIOHN_00132 1
6 ctg.s1.000000F_arrow CDS-gene 170711 171520 0 . DAFEIOHN_00142 1
7 ctg.s1.000000F_arrow CDS-gene 203959 204450 0 - . DAFEIOHN_00174 1
8 ctg.s1.000000F_arrow CDS-gene 211381 212196 0 . DAFEIOHN_00184 1
9 ctg.s1.000000F_arrow CDS-gene 236673 238499 0 . DAFEIOHN_00209 1
10 ctg.s1.000000F_arrow CDS-gene 533077 533850 0 . DAFEIOHN_00475 1
11 ctg.s1.000000F_arrow CDS-gene 533995 535194 0 . DAFEIOHN_00572 2
uj5u.com热心网友回复:
我们可以groupby
像这样使用 a并计算元素:
df.groupby('H').count()
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