我想估算 pandas DataFrame 上的所有列...我能想到的唯一方法是逐列如下所示...
有没有一种操作可以让我在不遍历列的情况下估算整个 DataFrame?
#!/usr/bin/python
from sklearn.preprocessing import Imputer
import numpy as np
import pandas as pd
#Imputer
fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)
#Model 1
DF = pd.DataFrame([[0,1,np.nan],[2,np.nan,3],[np.nan,2,5]])
DF.columns = "c1.c2.c3".split(".")
DF.index = "i1.i2.i3".split(".")
#Impute Series
imputed_DF = DF
for col in DF.columns:
imputed_column = fill_NaN.fit_transform(DF[col]).T
#Fill in Series on DataFrame
imputed_DF[col] = imputed_column
#DF
#c1 c2 c3
#i1 0 1 NaN
#i2 2 NaN 3
#i3 NaN 2 5
#imputed_DF
#c1 c2 c3
#i1 0 1.0 4
#i2 2 1.5 3
#i3 1 2.0 5
最佳答案
如果你想要 mean
或 median
你可以这样做:
fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)
imputed_DF = pd.DataFrame(fill_NaN.fit_transform(DF))
imputed_DF.columns = DF.columns
imputed_DF.index = DF.index
如果你想用 0 或其他东西填充它们,你总是可以这样做:
DF[DF.isnull()] = 0
关于python - 使用 Scikit-learn (sklearn) 估算整个 DataFrame(所有列)而不迭代列,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33660836/