将 Pandas 数据集转换为数组以在 Scikit-Learn 中建模
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Pandas dataset into an array for modelling in Scikit-Learn
提问by user40465
Can we run scikit-learn models on Pandas DataFrames or do we need to convert DataFrames into NumPy arrays?
我们可以在 Pandas DataFrames 上运行 scikit-learn 模型还是需要将 DataFrames 转换为 NumPy 数组？
回答by Akavall
You can use pandas.DataFramewith sklearn, for example:
您可以使用pandas.DataFramewith sklearn，例如：
import pandas as pd
from sklearn.cluster import KMeans
data = [(0.2, 10),
(0.3, 12),
(0.24, 14),
(0.8, 30),
(0.9, 32),
(0.85, 33.3),
(0.91, 31),
(0.1, 15),
(-0.23, 45)]
p_df = pd.DataFrame(data)
kmeans = KMeans(init='k-means++', n_clusters=3, n_init=10)
kmeans.fit(p_df)
Result:
结果：
>>> kmeans.labels_
array([0, 0, 0, 2, 2, 2, 2, 0, 1], dtype=int32)
回答by Greg
Pandas DataFrames are very good at acting like Numpy arrays when they need to. If in doubt, you can always use the valuesattribute to get a Numpy representation (df.valueswill give you a Numpy array of the values in DataFrame df.
Pandas DataFrames 非常擅长在需要时像 Numpy 数组一样工作。如果有疑问，您始终可以使用该values属性来获取 Numpy 表示（df.values将为您提供 DataFrame 中值的 Numpy 数组df。
