I want to calculate the mean value of each object on this dataframe:
| index | detection_class_names | detection_class_entities | detection_class_labels | detection_scores |
|---|---|---|---|---|
| 0 | /m/01g317 | Person | 69 | 0.7965893 |
| 1 | /m/01g317 | Person | 69 | 0.7868858 |
| 2 | /m/01g317 | Person | 69 | 0.785902 |
| 3 | /m/01g317 | Person | 69 | 0.77137744 |
| 4 | /m/01g317 | Person | 69 | 0.770353 |
| 5 | /m/01g317 | Person | 69 | 0.7686965 |
| 6 | /m/01g317 | Person | 69 | 0.7597503 |
| 7 | /m/01g317 | Person | 69 | 0.75952464 |
| 8 | /m/01g317 | Person | 69 | 0.7312174 |
| 9 | /m/01g317 | Person | 69 | 0.69465923 |
| 10 | /m/01g317 | Person | 69 | 0.6754475 |
| 11 | /m/01g317 | Person | 69 | 0.63933325 |
| 12 | /m/01g317 | Person | 69 | 0.5939468 |
| 13 | /m/01g317 | Person | 69 | 0.54122645 |
| 14 | /m/01g317 | Person | 69 | 0.47619903 |
| 15 | /m/01g317 | Person | 69 | 0.3954147 |
| 16 | /m/01g317 | Person | 69 | 0.24747418 |
| 17 | /m/04yx4 | Man | 308 | 0.21528831 |
| 18 | /m/01g317 | Person | 69 | 0.19553629 |
| 19 | /m/01g317 | Person | 69 | 0.18318504 |
| 20 | /m/01g317 | Person | 69 | 0.17788896 |
| 21 | /m/01g317 | Person | 69 | 0.16685289 |
| 22 | /m/01g317 | Person | 69 | 0.15162204 |
| 23 | /m/04yx4 | Man | 308 | 0.14820576 |
| 24 | /m/01g317 | Person | 69 | 0.14413418 |
| 25 | /m/01g317 | Person | 69 | 0.12246804 |
| 26 | /m/04yx4 | Man | 308 | 0.11826622 |
| 27 | /m/04yx4 | Man | 308 | 0.11152366 |
| 28 | /m/04yx4 | Man | 308 | 0.11139107 |
| 29 | /m/01g317 | Person | 69 | 0.10962985 |
| 30 | /m/01g317 | Person | 69 | 0.10439652 |
| 31 | /m/083wq | Wheel | 409 | 0.090862416 |
| 32 | /m/06msq | Sculpture | 359 | 0.08330029 |
| 33 | /m/01g317 | Person | 69 | 0.08234371 |
| 34 | /m/05y5lj | Sports equipment | 337 | 0.078681745 |
| 35 | /m/09j2d | Clothing | 433 | 0.0768458 |
| 36 | /m/04yx4 | Man | 308 | 0.075884864 |
| 37 | /m/04yx4 | Man | 308 | 0.06740342 |
| 38 | /m/01g317 | Person | 69 | 0.06324577 |
| 39 | /m/04yx4 | Man | 308 | 0.06278986 |
| 40 | /m/01g317 | Person | 69 | 0.06277132 |
| 41 | /m/04yx4 | Man | 308 | 0.060511187 |
| 42 | /m/04yx4 | Man | 308 | 0.055817537 |
| 43 | /m/083wq | Wheel | 409 | 0.05531507 |
| 44 | /m/09j2d | Clothing | 433 | 0.050744954 |
| 45 | /m/04yx4 | Man | 308 | 0.05006243 |
| 46 | /m/09j2d | Clothing | 433 | 0.049922056 |
| 47 | /m/09j2d | Clothing | 433 | 0.049681067 |
| 48 | /m/04yx4 | Man | 308 | 0.04912561 |
| 49 | /m/0b_rs | Swimming pool | 445 | 0.048854742 |
| 50 | /m/05y5lj | Sports equipment | 337 | 0.04200941 |
| 51 | /m/01g317 | Person | 69 | 0.041352615 |
| 52 | /m/04yx4 | Man | 308 | 0.04089966 |
| 53 | /m/09j2d | Clothing | 433 | 0.040262185 |
| 54 | /m/04yx4 | Man | 308 | 0.0390447 |
| 55 | /m/0h8mhzd | Sports uniform | 540 | 0.038814023 |
| 56 | /m/01g317 | Person | 69 | 0.038691193 |
| 57 | /m/04yx4 | Man | 308 | 0.03564315 |
| 58 | /m/04yx4 | Man | 308 | 0.03502448 |
| 59 | /m/01g317 | Person | 69 | 0.03491944 |
| 60 | /m/09j2d | Clothing | 433 | 0.03437933 |
| 61 | /m/01g317 | Person | 69 | 0.03309837 |
| 62 | /m/01g317 | Person | 69 | 0.032974593 |
| 63 | /m/09j2d | Clothing | 433 | 0.032671154 |
| 64 | /m/04rky | Mammal | 298 | 0.032538544 |
| 65 | /m/01g317 | Person | 69 | 0.031221595 |
| 66 | /m/01g317 | Person | 69 | 0.03066326 |
| 67 | /m/04yx4 | Man | 308 | 0.030130534 |
| 68 | /m/04rky | Mammal | 298 | 0.030032743 |
| 69 | /m/09j2d | Clothing | 433 | 0.029718297 |
| 70 | /m/09j2d | Clothing | 433 | 0.0291651 |
| 71 | /m/09j2d | Clothing | 433 | 0.028960558 |
| 72 | /m/09j2d | Clothing | 433 | 0.028387893 |
| 73 | /m/04yx4 | Man | 308 | 0.027450493 |
| 74 | /m/01g317 | Person | 69 | 0.027107958 |
| 75 | /m/04yx4 | Man | 308 | 0.027106939 |
| 76 | /m/01g317 | Person | 69 | 0.025961738 |
| 77 | /m/01g317 | Person | 69 | 0.025673656 |
| 78 | /m/09j2d | Clothing | 433 | 0.025575927 |
| 79 | /m/01g317 | Person | 69 | 0.02499498 |
| 80 | /m/04yx4 | Man | 308 | 0.024569038 |
| 81 | /m/09j2d | Clothing | 433 | 0.024464408 |
| 82 | /m/04rky | Mammal | 298 | 0.024349347 |
| 83 | /m/01g317 | Person | 69 | 0.024307335 |
| 84 | /m/01g317 | Person | 69 | 0.023867775 |
| 85 | /m/04rky | Mammal | 298 | 0.023107737 |
| 86 | /m/04yx4 | Man | 308 | 0.02282769 |
| 87 | /m/04rky | Mammal | 298 | 0.022633953 |
| 88 | /m/0138tl | Toy | 11 | 0.022467108 |
| 89 | /m/01g317 | Person | 69 | 0.022245709 |
| 90 | /m/04yx4 | Man | 308 | 0.021241672 |
| 91 | /m/01g317 | Person | 69 | 0.021194538 |
| 92 | /m/09j2d | Clothing | 433 | 0.019970622 |
| 93 | /m/04rky | Mammal | 298 | 0.019421574 |
| 94 | /m/04rky | Mammal | 298 | 0.019058326 |
| 95 | /m/01rzcn | Watercraft | 106 | 0.018701846 |
| 96 | /m/04rky | Mammal | 298 | 0.017770693 |
| 97 | /m/09j2d | Clothing | 433 | 0.017455762 |
| 98 | /m/01g317 | Person | 69 | 0.017210666 |
| 99 | /m/04yx4 | Man | 308 | 0.017077602 |
df2.groupby('detection_class_entities', as_index=False)['detection_scores'].mean()
result:
/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: FutureWarning: Dropping invalid columns in DataFrameGroupBy.mean is deprecated. In a future version, a TypeError will be raised. Before calling .mean, select only columns which should be valid for the function.
exec(code_obj, self.user_global_ns, self.user_ns)
| index | detection_class_entities |
|---|---|
| 0 | Clothing |
| 1 | Mammal |
| 2 | Man |
| 3 | Person |
| 4 | Sculpture |
| 5 | Sports equipment |
| 6 | Sports uniform |
| 7 | Swimming pool |
| 8 | Toy |
| 9 | Watercraft |
| 10 | Wheel |
How can fix?
Thank
83% thành viên diễn đàn không hỏi bài tập, còn bạn thì sao?