根据列值从 df 访问一行

Question

我试图找出可靠性 <0.70 的行，但 output 似乎也包括可靠性为 0.70 的行。 有什么问题？

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原DF：

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po_id po_name 产品年份 衡量率 分母 分子 is_reported 可靠性

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0 1051408 Aberdeen Care Alliance Commercial HMO/POS 18 CHLAMSCR 67.740000 62.0 42.0 True NaN 1 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 AMROV64 80.000000 20.0 16.0 True NaN 2 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 CISCOMBO10 17.650000 34.0 6.0 True NaN 3 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 OFCSTAFF 69.440000 NaN NaN True 0.76 4 1051408 Aberdeen Care Alliance Commercial HMO/POS 18 BCS5274 86.420000 302.0 261.0 True NaN 5 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 SPD1 57.810000 64.0 37.0 True NaN 6 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 PDCS 79.530000 127.0 101.0 True NaN 7 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 TCOC_250K_GEO_RISKADJ 289.281096 NaN NaN False NaN 8 1051408 Aberdeen Care Alliance Commercial HMO/POS 19 CBPD4 67.440000 129.0 87.0 True NaN 9 1051408 Aberdeen Care Alliance Commercial HMO/POS 18 COORDINATE3 55.370000 NaN NaN True 0.74

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添加代码以定位可靠性小于 0.70 的位置 Awards_test_df.loc[awards_test_df['reliability'] <0.70]

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Output：

po_id   po_name product year    measure rate    denominator numerator is_reported   reliability

191 1008200 Advancements Physicians Medical Center  Commercial HMO/POS  18  ACCESS3 58.13   NaN NaN True    0.60
515 1021102 Baird Medical Group Commercial HMO/POS  18  COORDINATE3 60.02   NaN NaN True    0.70
... ... ... ... ... ... ... ... ... ... ...
8606    1038400 Vf Healthcare   Commercial HMO/POS  18  OFCSTAFF    68.78   NaN NaN True    0.70
8620    1038400 Vf Healthcare   Commercial HMO/POS  18  MDINTERACT3 79.57   NaN NaN True    0.70
8800    1006001 Viva Physicians Commercial HMO/POS  18  ACCESS3 66.25   NaN NaN True    0.70
8869    1017708 Waltz Hospital  Commercial HMO/POS  19  MDINTERACT3 81.01   NaN NaN True    0.70
9142    1028100 Zeke Medical Group  Commercial HMO/POS  18  ACCESS3 56.37   NaN NaN True    0.70

Answer 1

复制代码时，您的代码看起来非常好：

import pandas as pd
data = [ { "po_id": 191, "po_name": 1008200, "product": "Advancements Physicians Medical Center  Commercial HMO/POS", "year": 18, "measure": "ACCESS3", "rate": 58.13, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.6 }, { "po_id": 515, "po_name": 1021102, "product": "Baird Medical Group Commercial HMO/POS", "year": 18, "measure": "COORDINATE3", "rate": 60.02, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.7 }, { "po_id": 8606, "po_name": 1038400, "product": "Vf Healthcare   Commercial HMO/POS", "year": 18, "measure": "OFCSTAFF", "rate": 68.78, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.7 }, { "po_id": 8620, "po_name": 1038400, "product": "Vf Healthcare   Commercial HMO/POS", "year": 18, "measure": "MDINTERACT3", "rate": 79.57, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.7 }, { "po_id": 8800, "po_name": 1006001, "product": "Viva Physicians Commercial HMO/POS", "year": 18, "measure": "ACCESS3", "rate": 66.25, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.7 }, { "po_id": 8869, "po_name": 1017708, "product": "Waltz Hospital  Commercial HMO/POS", "year": 19, "measure": "MDINTERACT3", "rate": 81.01, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.7 }, { "po_id": 9142, "po_name": 1028100, "product": "Zeke Medical Group  Commercial HMO/POS", "year": 18, "measure": "ACCESS3", "rate": 56.37, "denominator": "NaN", "numerator": "NaN", "is_reported": True, "reliability": 0.7 } ]
awards_test_df = pd.DataFrame(data)
awards_test_df.loc[awards_test_df['reliability'] <0.70]

Output：

|    |   po_id |   po_name | product                                                    |   year | measure   |   rate |   denominator |   numerator | is_reported   |   reliability |
|---:|--------:|----------:|:-----------------------------------------------------------|-------:|:----------|-------:|--------------:|------------:|:--------------|--------------:|
|  0 |     191 |   1008200 | Advancements Physicians Medical Center  Commercial HMO/POS |     18 | ACCESS3   |  58.13 |           nan |         nan | True          |           0.6 |

Answer 2

它只是显示格式。 试试下面检查

df = pd.DataFrame({"reliability":np.random.uniform(.65,.75, 100)})

df = df.loc[df.reliability.lt(.7)].assign(twodp=df.reliability.round(2)).query("twodp.eq(.7)")

可靠性	twodp
0.695661	0.7
0.699588	0.7
0.698993	0.7
0.697933	0.7
0.698356	0.7
0.699906	0.7
0.695279	0.7