我需要一些python和pandas的帮助。
我实际上在seq1_id列中有一个数据帧，其中包括物种1的序列的seq_id和column 2的序列的sp2。
我实际上通过了对这些序列的过滤器并获得了两个数据帧（一个具有sp 1的所有序列通过过滤器）和（一个具有sp2的所有序列通过过滤器）。 / p>
然后我有3 dataframes.
因为在成对中，一个seq可以通过过滤而另一个没有，重要的是只保留两个先前过滤的配对基因，所以我需要做的是实际解析我的第一个df这样的：
Seq_1.id Seq_2.id
seq1_A seq8_B
seq2_A Seq9_B
seq3_A Seq10_B
seq4_A Seq11_B
并逐行检查seq1_A中是否存在（第1行）df2，如果df3中也存在seq8_B，请将此行保留在{ {1}}并将其添加到新的df1。
这是一个需要输出的例子：
df4然后因为first df:
Seq_1.id Seq_2.id
seq1_A seq8_B
seq2_A Seq9_B
seq3_A Seq10_B
seq4_A Seq11_B
df2 (sp1) (seq3_A is absent)
Seq_1.id
seq1_A
seq2_A
seq4_A
df3 (sp2) (Seq11_B is absent)
Seq_2.id
seq8_B
Seq9_B
Seq10_B
和Seq11_B不存在，seq3_A（输出）将是：
df4谢谢你：）
Seq_1.id Seq_2.id
seq1_A seq8_B
seq2_A Seq9_B
我得到了一个ampty输出，只有列名，但不应该那样。 如果您无法测试其上的代码，请输入以下数据：
DF1：
candidates_0035=pd.read_csv("candidates_genes_filtering_0035",sep='\t')
candidates_0042=pd.read_csv("candidates_genes_filtering_0042",sep='\t')
dN_dS=pd.read_csv("dn_ds.out_sorted",sep='\t')
df4 =dN_dS[dN_dS['seq1_id'].isin(candidates_0042['gene'])&dN_dS['seq2_id'].isin(candidates_0035['gene'])]
DF2：
Unnamed: 0 seq1_id seq2_id dN dS Dist_third_pos Dist_brute Length_seq_1 Length_seq_2 GC_content_seq1 GC_content_seq2 GC Mean_length
0 0 g66097.t1_0035_0035 g13600.t1_0042_0042 0.10455938989199982 0.3122332927029104 0.23600000000000002 0.142 535.0 1024.0 49.1588785046729 51.171875 50.165376752336456 535.0
1 1 g45594.t1_0035_0035 g1464.t1_0042_0042 0.5208761055250978 5.430485421797574 0.7120000000000001 0.489 246.0 222.0 47.967479674796756 44.594594594594604 46.28103713469567 222.0
2 2 g50055.t1_0035_0035 g34744.t1_0042_0035 0.08040473491714645 0.4233916132491867 0.262 0.139 895.0 749.0 56.312849162011176 57.67690253671562 56.994875849363396 749.0
3 3 g34020.t1_0035_0035 g12096.t1_0042_0042 0.4385191689737516 26.834927363887587 0.5760000000000001 0.433 597.0 633.0 37.85594639865997 39.810426540284354 38.83318646947217 597.0
4 4 g28436.t1_0035_0042 g35222.t1_0042_0035 0.055299811368483165 0.1181241496387666 0.1 0.069 450.0 461.0 45.111111111111114 44.90238611713666 45.006748614123886 450.0
5 5 g1005.t1_0035_0035 g11524.t1_0042_0042 0.3528036631463747 19.32549458735676 0.71 0.512 3177.0 3804.0 39.06200818382121 52.944269190325976 46.0031386870736 3177.0
6 6 g28456.t1_0035_0035 g31669.t1_0042_0035 0.4608959702286786 26.823981621115166 0.6859999999999999 0.469 516.0 591.0 49.224806201550386 53.46869712351946 51.346751662534935 516.0
7 7 g6202.t1_0035_0035 g193.t1_0042_0042 0.4679458383555545 17.81312422445775 0.66 0.462 804.0 837.0 41.91542288557214 47.67025089605735 44.79283689081474 804.0
8 8 g60667.t1_0035_0035 g14327.t1_0042_0042 0.046056273155280165 0.13320612138898 0.122 0.067 348.0 408.0 56.89655172413793 55.392156862745104 56.1443542934415 348.0
9 9 g30148.t1_0035_0042 g37790.t1_0042_0035 0.05631607180881047 0.19747150378706246 0.12300000000000001 0.08800000000000001 405.0 320.0 59.012345679012356 58.4375 58.72492283950618 320.0
10 10 g24481.t1_0035_0035 g37405.t1_0042_0035 0.2151957757290965 0.15106487998618026 0.135 0.17600000000000002 270.0 276.0 51.111111111111114 51.44927536231884 51.28019323671497 270.0
11 11 g33270.t1_0035_0035 g21201.t1_0042_0035 0.2773062983971916 21.13839474189674 0.6940000000000001 0.401 297.0 357.0 54.882154882154886 50.42016806722689 52.65116147469089 297.0
12 12 EOG090X03YJ_0035_0035_1 EOG090X03YJ_0042_0042_1 0.5402471721616758 19.278839157918302 0.7070000000000001 0.488 1321.0 1719.0 38.53141559424678 43.92088423502036 41.22614991463357 1321.0
13 13 g13075.t1_0035_0042 g504.t1_0042_0035 0.3317504066721263 4.790120127840871 0.65 0.38799999999999996 372.0 408.0 59.40860215053763 51.470588235294116 55.43959519291587 372.0
14 14 g1026.t1_0035_0035 g7716.t1_0042_0042 0.21445770772761286 13.92799368027682 0.626 0.344 336.0 315.0 38.095238095238095 44.444444444444436 41.26984126984127 315.0
15 15 g18238.t1_0035_0042 g35401.t1_0042_0035 0.3889830456691637 20.33679494952895 0.6759999999999999 0.44799999999999995 320.0 366.0 50.9375 49.453551912568315 50.19552595628416 320.0
DF3：
Unnamed: 0 gene scaf_name start end cov_depth GC
179806 g13600.t1_0042_0042 scaffold_6556 1 1149 2.42361684558216 0.528846153846154
315037 g34744.t1_0042_0035 scaffold_8076 17 765 3.49803921568627 0.386138613861386
317296 g35222.t1_0042_0035 scaffold_9018 1 614 93.071661237785 0.41
183513 g14327.t1_0042_0042 scaffold_9358 122 529 3.3184165232357996 0.36
328164 g37790.t1_0042_0035 scaffold_16356 1 320 2.73125 0.436241610738255
326617 g37405.t1_0042_0035 scaffold_14890 1 341 1.3061224489795902 0.36898395721925104
188515 g15510.t1_0042_0042 scaffold_20183 1 276 137.326086956522 0.669354838709677
184561 g14562.t1_0042_0042 scaffold_10427 1 494 157.993927125506 0.46145940390544704
290684 g30982.t1_0042_0035 scaffold_3800 440 940 174.499839537869 0.39823008849557506
179993 g13632.t1_0042_0042 scaffold_6654 29 1114 3.56506849315068 0.46153846153846206
181670 g13942.t1_0042_0042 scaffold_7830 1 811 5.307028360049321 0.529411764705882
196148 g20290.t1_0042_0035 scaffold_1145 2707 9712 78.84112231766741 0.367283950617284
313624 g34464.t1_0042_0035 scaffold_7610 1 480 7.740440324449589 0.549019607843137
303133 g32700.t1_0042_0035 scaffold_5119 1735 2373 118.436578171091 0.49074074074074103
答案 0 :(得分：1)
这应该这样做：
df4 = df1[df1['Seq_1.id'].isin(df2['Seq_1.id'])&df1['Seq_2.id'].isin(df3['Seq_2.id'])]
df4
# Seq_1.id Seq_2.id
#0 seq1_A seq8_B
#1 seq2_A Seq9_B
修改
你必须进行排列，这不会返回空白：
df4 = dN_dS[(dN_dS['seq1_id'].isin(candidates_0035['gene']))&(dN_dS['seq2_id'].isin(candidates_0042['gene']))]