Pandas merge后全为空Nan
问题：
我有两个如下的dataframe，index也是相同的
logFC AveExpr t P.Value adj.P.Val B
31124 3.284541 1.648456 50.427905 4.012965e-07 0.011210 7.769927
19930 1.549249 0.843535 28.414880 4.688255e-06 0.043655 5.321548
40422 17.256854 8.628910 19.096341 2.555958e-05 0.118327 3.404473
39569 14.772801 19.998569 14.766398 7.607675e-05 0.193197 2.122275
20181 68.110474 450.855255 14.273843 8.779938e-05 0.213272 1.952015
... ... ... ... ... ... ...
8708 32.318876 130.330466 4.438732 9.639254e-03 0.865688 -3.691248
17753 11.930959 26.719863 4.433230 9.682917e-03 0.866947 -3.696619
28713 1.127502 1.989897 4.420363 9.785952e-03 0.868792 -3.709198
4315 4.658730 15.094117 4.415421 9.825873e-03 0.868792 -3.714035
28263 3.949047 7.173845 4.399925 9.952321e-03 0.874255 -3.729227
[151 rows x 6 columns]
Fcon1 Fcon2 Fcon3 FTAI1 FTAI2 FTAI3 up/down Official_Symbol
363 3.968901 4.043801 3.329393 2.651096 2.495120 2.267443 up Plekha1
745 137.437256 137.844254 147.885880 113.702637 110.335915 100.270744 up Serpinb6a
933 6.431833 5.565875 3.619443 0.000000 0.000000 0.000000 up Tardbp
1187 31.972027 28.155848 29.948334 20.580564 21.249414 18.863104 up Fchsd2
1190 1.995128 1.429263 1.321291 0.567278 0.535526 0.485705 up Crebrf
... ... ... ... ... ... ... ... ...
54043 443.697540 448.293732 468.942200 354.451294 374.866638 378.524048 up Cyp4f14
54250 29.244991 27.789938 31.440329 23.169428 22.493416 20.367746 up Itga6
54349 5.590240 6.356446 7.214879 3.743275 1.146106 0.748171 up Ddc
55507 2.693715 2.128844 2.108521 0.000000 0.000000 0.000000 up Mindy3
55641 6.026206 7.566981 7.150218 2.788216 2.971823 2.464447 up Parp11
[151 rows x 8 columns]merge后
DEG_up = pd.merge(DEG_up,result_df,how='left',left_index=True,right_index=True)
---
Fcon1 Fcon2 Fcon3 FTAI1 FTAI2 FTAI3 up/down Official_Symbol logFC AveExpr t P.Value adj.P.Val B
363 3.968901 4.043801 3.329393 2.651096 2.495120 2.267443 up Plekha1 NaN NaN NaN NaN NaN NaN
745 137.437256 137.844254 147.885880 113.702637 110.335915 100.270744 up Serpinb6a NaN NaN NaN NaN NaN NaN
933 6.431833 5.565875 3.619443 0.000000 0.000000 0.000000 up Tardbp NaN NaN NaN NaN NaN NaN
1187 31.972027 28.155848 29.948334 20.580564 21.249414 18.863104 up Fchsd2 NaN NaN NaN NaN NaN NaN
1190 1.995128 1.429263 1.321291 0.567278 0.535526 0.485705 up Crebrf NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... .. ... ... ..
54043 443.697540 448.293732 468.942200 354.451294 374.866638 378.524048 up Cyp4f14 NaN NaN NaN NaN NaN NaN
54250 29.244991 27.789938 31.440329 23.169428 22.493416 20.367746 up Itga6 NaN NaN NaN NaN NaN NaN
54349 5.590240 6.356446 7.214879 3.743275 1.146106 0.748171 up Ddc NaN NaN NaN NaN NaN NaN
55507 2.693715 2.128844 2.108521 0.000000 0.000000 0.000000 up Mindy3 NaN NaN NaN NaN NaN NaN
55641 6.026206 7.566981 7.150218 2.788216 2.971823 2.464447 up Parp11 NaN NaN NaN NaN NaN NaN
[151 rows x 14 columns] 解决：
打印index的类型查看，发现不一致
print (DEG_up.index)
print (result_df.index)
---
Int64Index([ 363, 745, 933, 1187, 1190, 1603, 1627, 2192, 2310,
2492,
...
52788, 52990, 53373, 53681, 53829, 54043, 54250, 54349, 55507,
55641],
dtype='int64', length=151)
Index(['31124', '19930', '40422', '39569', '20181', '5572', '5625', '55507',
'9178', '41531',
...
'31718', '28243', '25591', '54349', '35914', '8708', '17753', '28713',
'4315', '28263'],
dtype='object', length=151)将索引的类型设置为一致的
DEG_up.index = DEG_up.index.astype("int64")
result_df.index = result_df.index.astype("int64")
---
Fcon1 Fcon2 Fcon3 FTAI1 FTAI2 FTAI3 ... logFC AveExpr t P.Value adj.P.Val B
363 3.968901 4.043801 3.329393 2.651096 2.495120 2.267443 ... 1.309479 3.125959 5.373214 0.004718 0.701119 -2.838175
745 137.437256 137.844254 147.885880 113.702637 110.335915 100.270744 ... 32.952698 124.579448 6.459141 0.002304 0.577388 -1.976445
933 6.431833 5.565875 3.619443 0.000000 0.000000 0.000000 ... 5.205717 2.602858 6.487810 0.002264 0.577388 -1.955324
1187 31.972027 28.155848 29.948334 20.580564 21.249414 18.863104 ... 9.794376 25.128215 7.739672 0.001116 0.445565 -1.102235
1190 1.995128 1.429263 1.321291 0.567278 0.535526 0.485705 ... 1.052391 1.055698 5.186436 0.005399 0.723379 -2.999702
... ... ... ... ... ... ... ... ... ... ... ... ... ...
54043 443.697540 448.293732 468.942200 354.451294 374.866638 378.524048 ... 84.363831 411.462575 8.105698 0.000925 0.434123 -0.875606
54250 29.244991 27.789938 31.440329 23.169428 22.493416 20.367746 ... 7.481556 25.750975 5.719150 0.003710 0.648525 -2.549867
54349 5.590240 6.356446 7.214879 3.743275 1.146106 0.748171 ... 4.508004 4.133186 4.450474 0.009547 0.863565 -3.679799
55507 2.693715 2.128844 2.108521 0.000000 0.000000 0.000000 ... 2.310360 1.155180 12.486301 0.000154 0.269285 1.279699
55641 6.026206 7.566981 7.150218 2.788216 2.971823 2.464447 ... 4.172973 4.827982 8.945901 0.000618 0.351973 -0.388451
[151 rows x 14 columns]