kaggle学习二——丢失数据的处理

1.直接丢弃

当然一般效果不好

2.采用插值的方式处理

均值，中值等

sklearn.impute

3.插值的加强版

（说实话，没有太看懂）

Imputation is the standard approach, and it usually works well. However, imputed values may by systematically above or below their actual values (which weren't collected in the dataset). Or rows with missing values may be unique in some other way. In that case, your model would make better predictions by considering which values were originally missing. Here's how it might look:

# make copy to avoid changing original data (when Imputing)
new_data = original_data.copy()

# make new columns indicating what will be imputed
cols_with_missing = (col for col in new_data.columns 
                                 if new_data[col].isnull().any())
for col in cols_with_missing:
    new_data[col + '_was_missing'] = new_data[col].isnull()

# Imputation
my_imputer = SimpleImputer()
new_data = pd.DataFrame(my_imputer.fit_transform(new_data))
new_data.columns = original_data.columns

In some cases this approach will meaningfully improve results. In other cases, it doesn't help at all.