如何将数据集从Scikit-learn Bunch对象转换为Pandas DataFrame？
from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
print(type(data)) #输出：<class 'sklearn.utils.Bunch'>data1 = pd. # Is there a Pandas method to accomplish this?
最佳思路
可以手动使用pd.DataFrame构造函数，提供一个numpy数组(data)和列名的列表(columns)。要将所有内容都放在一个DataFrame中，可以使用np.c_[...]将特征和目标(标签)连接到一个numpy数组中(请注意运算符[])：
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
# save load_iris() sklearn dataset to iris
# if you'd like to check dataset type use: type(load_iris())
# if you'd like to view list of attributes use: dir(load_iris())
iris = load_iris()
# np.c_ is the numpy concatenate function
# which is used to concat iris['data'] and iris['target'] arrays
# for pandas column argument: concat iris['feature_names'] list
# and string list (in this case one string); you can make this anything you'd like..
# the original dataset would probably call this ['Species']
data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
columns= iris['feature_names'] + ['target'])
第二种思路
对于scikit-learn中的所有数据集，上文”最佳思路”的解决方案不够通用。例如，它不适用于波士顿住房数据集。我提出了另一种更通用的解决方案。也无需使用numpy。
from sklearn import datasets
import pandas as pd
boston_data = datasets.load_boston()
df_boston = pd.DataFrame(boston_data.data,columns=boston_data.feature_names)
df_boston['target'] = pd.Series(boston_data.target)
df_boston.head()
作为通用函数：
def sklearn_to_df(sklearn_dataset):
df = pd.DataFrame(sklearn_dataset.data, columns=sklearn_dataset.feature_names)
df['target'] = pd.Series(sklearn_dataset.target)
return df
df_boston = sklearn_to_df(datasets.load_boston())
参考资料