插补似乎改变了非 NaN 值

Question

运行

imputed_training=impyute.imputation.cs.em(X_train2.values, loops=50)
xtrain2_imputed=pd.DataFrame(imputed_training)
columns=('interest-over-time','hash-rate',...) # very long list
xtrain2_imputed.columns = columns

Returns 包含与原始数据帧 (xtrain2) 完全不同的值的数据帧。我如何使用期望最大化来推算我的 NaN，使 returns 具有与原始 df 相同的列、列顺序和行顺序的数据框？

Answer 1

执行此操作后，您可以将其分配回去

mputed_training=impyute.imputation.cs.em(X_train2.values, loops=50)
X_train2[:]= mputed_training

插补似乎改变了非 NaN 值

Imputation seems to change non NaN values

python

machine-learning

pandas

imputation