(转)如何将Sklearn数据集Brunch格式转换为Pandas数据集DataFrame？

转载链接：[https://vimsky.com/article/4362.html]

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'])

type(load_iris())
sklearn.utils.Bunch

dir(load_iris())
['DESCR', 'data', 'feature_names', 'target', 'target_names']

 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],
 '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]),
 'target_names': array(['setosa', 'versicolor', 'virginica']

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())

dataframe转化成array:df=df.values

array转化成dataframe:
import pandas as pd
df = pd.DataFrame(df)

df=df.values.flatten() 需要的时候在末尾加一个flatten() 变成一行的方便统计分析