如何将数据从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))
data1 = pd. # Is there a Pandas method to accomplish this?
发布于 2016-06-29 21:26:17
您可以手动使用pd.DataFrame
构造函数,给出一个数值数组(data
)和一个列名列表(columns
)。要将所有内容都放在一个DataFrame中,可以使用np.c_[...]
将特性和目标连接到一个numpy数组中(请注意[]
):
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'])
发布于 2017-04-22 06:40:42
from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()
这篇教程可能会很有趣:http://www.neural.cz/dataset-exploration-boston-house-pricing.html
发布于 2017-09-23 21:03:47
TOMDLt的解决方案对于scikit-learn中的所有数据集都不够通用。例如,它不适用于波士顿住房数据集。我提出了一个更通用的不同解决方案。也不需要使用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()
作为一般函数:
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())
https://stackoverflow.com/questions/38105539
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