10

下面的 Python 代码只返回一个数组，但我希望缩放数据替换原始数据。

from sklearn.preprocessing import StandardScaler
df = StandardScaler().fit_transform(df[['cost', 'sales']])
df

输出

array([[ 1.99987622, -0.55900276],
       [-0.49786658, -0.45658181],
       [-0.5146864 , -0.505097  ],
       [-0.48104676, -0.47814412],
       [-0.50627649,  1.9988257 ]])

原始数据

id  cost    sales   item
1   300       50    pen
2   3         88    bottle
3   1         70    drink
4   5         80    cup
5   2        999    ink
4

3 回答 3

26

只需将其分配回去

df[['cost', 'sales']] = StandardScaler().fit_transform(df[['cost', 'sales']])
df
Out[45]: 
   id      cost     sales    item
0   1  1.999876 -0.559003     pen
1   2 -0.497867 -0.456582  bottle
2   3 -0.514686 -0.505097   drink
3   4 -0.481047 -0.478144     cup
4   5 -0.506276  1.998826     ink
于 2018-04-04T02:16:50.017 回答
2

或者如果使用列索引而不是列名：

import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.DataFrame({"cost": [300,3,1,5,2], "sales": [50,88,70,80,999], "item": ["pen","bottle","drink","cup","ink"]})

# Scale selected columns by index
df.iloc[:, 0:2] = StandardScaler().fit_transform(df.iloc[:, 0:2])

       cost     sales    item
0  1.999876 -0.559003     pen
1 -0.497867 -0.456582  bottle
2 -0.514686 -0.505097   drink
3 -0.481047 -0.478144     cup
4 -0.506276  1.998826     ink

也可以保存sclaer 对象，以便根据现有的缩放器缩放“新数据”：

df = pd.DataFrame({"cost": [300,3,1,5,2], "sales": [50,88,70,80,999], "item": ["pen","bottle","drink","cup","ink"]})
df_new = pd.DataFrame({"cost": [299,5,12,64,2], "sales": [55,99,48,20,999], "item": ["pen","bottle","drink","cup","ink"]})

# Set up scaler
scaler = StandardScaler().fit(df.iloc[:, 0:2])

# Scale original data
df.iloc[:, 0:2] = scaler.transform(df.iloc[:, 0:2])

# Scale new data 
df_new.iloc[:, 0:2] = scaler.transform(df_new.iloc[:, 0:2])
于 2021-01-21T17:23:19.233 回答
0

如果您想获得sklearn Pipeline 的好处（方便/封装、联合参数选择和泄漏安全），您可以使用ColumnTransformer：

preproc = ColumnTransformer(
    transformers=[
        ('scale', StandardScaler(), ["cost", "sales"]),
    ],
    remainder="passthrough",
)

（有几种方法可以指定哪些列进入缩放器，请查看文档）。现在您可以像@Peter 提到的那样保存缩放器对象，但您也不必继续重复切片：

df = preproc.fit_transform(df)
df_new = preproc.transform(df)
于 2021-06-15T16:31:29.953 回答