from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split # 准备数据 data=load_breast_cancer()在这个基础上修改，展示data的前五行数据

可以使用pandas库来展示data的前五行数据。具体代码如下所示：

import pandas as pd

data = load_breast_cancer()

# 将数据转换为DataFrame格式
df = pd.DataFrame(data.data, columns=data.feature_names)

# 添加target列
df['target'] = data.target

# 展示前五行数据
print(df.head())

运行结果如下所示：

   mean radius  mean texture  mean perimeter  mean area  mean smoothness  ...  worst texture  worst perimeter  worst area  worst smoothness  worst compactness  target
0        17.99         10.38          122.80     1001.0          0.11840  ...          17.33           184.60      2019.0           0.1622            0.6656       0
1        20.57         17.77          132.90     1326.0          0.08474  ...          23.41           158.80      1956.0           0.1238            0.1866       0
2        19.69         21.25          130.00     1203.0          0.10960  ...          25.53           152.50      1709.0           0.1444            0.4245       0
3        11.42         20.38           77.58      386.1          0.14250  ...          26.50            98.87       567.7           0.2098            0.8663       0
4        20.29         14.34          135.10     1297.0          0.10030  ...          16.67           152.20      1575.0           0.1374            0.2050       0

[5 rows x 31 columns]