```

    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns 
    pd.read_csv('BioAsseT pt data.csv')
    df = pd.read_csv("BioAsseT pt data.csv")
    data = df.values
    print(data)
    
    [[nan 1 1 ... nan 1 '0']
     [nan 1 2 ... nan 1 '0']
     [nan 2 1 ... nan 1 '1']
     ...
     [nan 101 1 ... nan 1 '1']
     [nan 102 1 ... nan 0 '0']
     [nan 103 1 ... nan 1 '.']]
    
```

これを以下のアヤメデータの様にdataとtargetの様なものをつけたいです

```

    from sklearn.datasets import load_iris
    dataset=load_iris()
    print(dataset)
    
    {'data': array([[5.1, 3.5, 1.4, 0.2],
           [4.9, 3. , 1.4, 0.2],
           [4.7, 3.2, 1.3, 0.2],
           [4.6, 3.1, 1.5, 0.2],
           [5. , 3.6, 1.4, 0.2],
           [5.4, 3.9, 1.7, 0.4],
           [4.6, 3.4, 1.4, 0.3],
           [5. , 3.4, 1.5, 0.2],
           [4.4, 2.9, 1.4, 0.2],
           [4.9, 3.1, 1.5, 0.1],
           [5.4, 3.7, 1.5, 0.2],
           [4.8, 3.4, 1.6, 0.2],
           [4.8, 3. , 1.4, 0.1],
           [4.3, 3. , 1.1, 0.1],
           [5.8, 4. , 1.2, 0.2],
           [5.7, 4.4, 1.5, 0.4],
           [5.4, 3.9, 1.3, 0.4],
           [5.1, 3.5, 1.4, 0.3],
           [5.7, 3.8, 1.7, 0.3],
           [5.1, 3.8, 1.5, 0.3],
           [5.4, 3.4, 1.7, 0.2],
           [5.1, 3.7, 1.5, 0.4],
           [4.6, 3.6, 1. , 0.2],
           [5.1, 3.3, 1.7, 0.5],
           [4.8, 3.4, 1.9, 0.2],
           [5. , 3. , 1.6, 0.2],
           [5. , 3.4, 1.6, 0.4],
           [5.2, 3.5, 1.5, 0.2],
           [5.2, 3.4, 1.4, 0.2],
           [4.7, 3.2, 1.6, 0.2],
           [4.8, 3.1, 1.6, 0.2],
           [5.4, 3.4, 1.5, 0.4],
           [5.2, 4.1, 1.5, 0.1],
           [5.5, 4.2, 1.4, 0.2],
           [4.9, 3.1, 1.5, 0.2],
           [5. , 3.2, 1.2, 0.2],
           [5.5, 3.5, 1.3, 0.2],
           [4.9, 3.6, 1.4, 0.1],
           [4.4, 3. , 1.3, 0.2],
           [5.1, 3.4, 1.5, 0.2],
           [5. , 3.5, 1.3, 0.3],
           [4.5, 2.3, 1.3, 0.3],
           [4.4, 3.2, 1.3, 0.2],
           [5. , 3.5, 1.6, 0.6],
           [5.1, 3.8, 1.9, 0.4],
           [4.8, 3. , 1.4, 0.3],
           [5.1, 3.8, 1.6, 0.2],
           [4.6, 3.2, 1.4, 0.2],
           [5.3, 3.7, 1.5, 0.2],
           [5. , 3.3, 1.4, 0.2],
           [7. , 3.2, 4.7, 1.4],
           [6.4, 3.2, 4.5, 1.5],
           [6.9, 3.1, 4.9, 1.5],
           [5.5, 2.3, 4. , 1.3],
           [6.5, 2.8, 4.6, 1.5],
           [5.7, 2.8, 4.5, 1.3],
           [6.3, 3.3, 4.7, 1.6],
           [4.9, 2.4, 3.3, 1. ],
           [6.6, 2.9, 4.6, 1.3],
           [5.2, 2.7, 3.9, 1.4],
           [5. , 2. , 3.5, 1. ],
           [5.9, 3. , 4.2, 1.5],
           [6. , 2.2, 4. , 1. ],
           [6.1, 2.9, 4.7, 1.4],
           [5.6, 2.9, 3.6, 1.3],
           [6.7, 3.1, 4.4, 1.4],
           [5.6, 3. , 4.5, 1.5],
           [5.8, 2.7, 4.1, 1. ],
           [6.2, 2.2, 4.5, 1.5],
           [5.6, 2.5, 3.9, 1.1],
           [5.9, 3.2, 4.8, 1.8],
           [6.1, 2.8, 4. , 1.3],
           [6.3, 2.5, 4.9, 1.5],
           [6.1, 2.8, 4.7, 1.2],
           [6.4, 2.9, 4.3, 1.3],
           [6.6, 3. , 4.4, 1.4],
           [6.8, 2.8, 4.8, 1.4],
           [6.7, 3. , 5. , 1.7],
           [6. , 2.9, 4.5, 1.5],
           [5.7, 2.6, 3.5, 1. ],
           [5.5, 2.4, 3.8, 1.1],
           [5.5, 2.4, 3.7, 1. ],
           [5.8, 2.7, 3.9, 1.2],
           [6. , 2.7, 5.1, 1.6],
           [5.4, 3. , 4.5, 1.5],
           [6. , 3.4, 4.5, 1.6],
           [6.7, 3.1, 4.7, 1.5],
           [6.3, 2.3, 4.4, 1.3],
           [5.6, 3. , 4.1, 1.3],
           [5.5, 2.5, 4. , 1.3],
           [5.5, 2.6, 4.4, 1.2],
           [6.1, 3. , 4.6, 1.4],
           [5.8, 2.6, 4. , 1.2],
           [5. , 2.3, 3.3, 1. ],
           [5.6, 2.7, 4.2, 1.3],
           [5.7, 3. , 4.2, 1.2],
           [5.7, 2.9, 4.2, 1.3],
           [6.2, 2.9, 4.3, 1.3],
           [5.1, 2.5, 3. , 1.1],
           [5.7, 2.8, 4.1, 1.3],
           [6.3, 3.3, 6. , 2.5],
           [5.8, 2.7, 5.1, 1.9],
           [7.1, 3. , 5.9, 2.1],
           [6.3, 2.9, 5.6, 1.8],
           [6.5, 3. , 5.8, 2.2],
           [7.6, 3. , 6.6, 2.1],
           [4.9, 2.5, 4.5, 1.7],
           [7.3, 2.9, 6.3, 1.8],
           [6.7, 2.5, 5.8, 1.8],
           [7.2, 3.6, 6.1, 2.5],
           [6.5, 3.2, 5.1, 2. ],
           [6.4, 2.7, 5.3, 1.9],
           [6.8, 3. , 5.5, 2.1],
           [5.7, 2.5, 5. , 2. ],
           [5.8, 2.8, 5.1, 2.4],
           [6.4, 3.2, 5.3, 2.3],
           [6.5, 3. , 5.5, 1.8],
           [7.7, 3.8, 6.7, 2.2],
           [7.7, 2.6, 6.9, 2.3],
           [6. , 2.2, 5. , 1.5],
           [6.9, 3.2, 5.7, 2.3],
           [5.6, 2.8, 4.9, 2. ],
           [7.7, 2.8, 6.7, 2. ],
           [6.3, 2.7, 4.9, 1.8],
           [6.7, 3.3, 5.7, 2.1],
           [7.2, 3.2, 6. , 1.8],
           [6.2, 2.8, 4.8, 1.8],
           [6.1, 3. , 4.9, 1.8],
           [6.4, 2.8, 5.6, 2.1],
           [7.2, 3. , 5.8, 1.6],
           [7.4, 2.8, 6.1, 1.9],
           [7.9, 3.8, 6.4, 2. ],
           [6.4, 2.8, 5.6, 2.2],
           [6.3, 2.8, 5.1, 1.5],
           [6.1, 2.6, 5.6, 1.4],
           [7.7, 3. , 6.1, 2.3],
           [6.3, 3.4, 5.6, 2.4],
           [6.4, 3.1, 5.5, 1.8],
           [6. , 3. , 4.8, 1.8],
           [6.9, 3.1, 5.4, 2.1],
           [6.7, 3.1, 5.6, 2.4],
           [6.9, 3.1, 5.1, 2.3],
           [5.8, 2.7, 5.1, 1.9],
           [6.8, 3.2, 5.9, 2.3],
           [6.7, 3.3, 5.7, 2.5],
           [6.7, 3. , 5.2, 2.3],
           [6.3, 2.5, 5. , 1.9],
           [6.5, 3. , 5.2, 2. ],
           [6.2, 3.4, 5.4, 2.3],
           [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
           2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
           2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
    
```
CC BY-SA 4.0
2023-04-10T09:26:27.480
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2023-04-10T10:55:06.137
2023-04-10T10:55:06.137
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question
python numpy 機械学習
load_iris dataの様にdataとtargetを設定したいが交差検証を回したいができないどのようにやればよいのでしょうか？