标准化 Python Pandas 数据框中的某些列？

Question

下面的 Python 代码只返回一个数组，但我希望缩放后的数据替换原始数据。

 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

原文由 BigData 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

只需将其分配回来

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

原文由 BENY 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 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

还可以保存 sclaer 对象，以便根据现有缩放器缩放“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])

原文由 Peter 发布，翻译遵循 CC BY-SA 4.0 许可协议