[英]Impute mean of single column in dask-ml
Calculating and imputing the mean using dask-ml works fine when changing all the columns that are np.nan
:在更改所有
np.nan
列时,使用 dask-ml 计算和估算平均值可以正常工作:
imputer = impute.SimpleImputer(strategy='mean')
data = [[100, 2], [np.nan, np.nan], [70, 7]]
df = pd.DataFrame(data, columns = ['Weight', 'Age'])
x3 = imputer.fit_transform(df)
print(x3)
Weight Age
0 100.0 2.0
1 85.0 4.5
2 70.0 7.0
But what if I need to leave Age
untouched?但是,如果我需要保持
Age
不变呢? Is it possible to specify what columns to impute?是否可以指定要估算的列?
You should be able to specify colums by df.Weight = imputer.fit_transform(df.Weight)
or by indexing columns df.loc["Weight"]
您应该能够通过
df.Weight = imputer.fit_transform(df.Weight)
或通过索引列df.loc["Weight"]
来指定列
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