与fancyimpute和熊猫数据的插补

Question

我有一个大的熊猫数据成名df。它有相当多的missings。删除行/或COL-明智的是不是一种选择。归咎于中位数，是指或最频繁的值不是一个选项是（因此与pandas和/或scikit不幸好好尝试一下做的伎俩归集）。

我遇到什么似乎是一个被称为整洁的包装fancyimpute来（你可以找到它here）。但是，我有一些问题吧。

这是我做的：

#the neccesary imports
import pandas as pd
import numpy as np
from fancyimpute import KNN

# df is my data frame with the missings. I keep only floats
df_numeric = = df.select_dtypes(include=[np.float])

# I now run fancyimpute KNN, 
# it returns a np.array which I store as a pandas dataframe
df_filled = pd.DataFrame(KNN(3).complete(df_numeric))

然而，df_filled是单个载体以某种方式，而不是填充数据帧。如何获得与插补数据帧的联系呢？

Update

我意识到，fancyimpute需要numpay array。我使用df_numeric因此转换的as_matrix()到一个阵列。

# df is my data frame with the missings. I keep only floats
df_numeric = df.select_dtypes(include=[np.float]).as_matrix()

# I now run fancyimpute KNN, 
# it returns a np.array which I store as a pandas dataframe
df_filled = pd.DataFrame(KNN(3).complete(df_numeric))

输出与失踪列标签一个数据帧。任何方法来检索标签？

Answer 1

df=pd.DataFrame(data=mice.complete(d), columns=d.columns, index=d.index)

由该fancyimpute对象的np.array方法返回的.complete()（无论是小鼠或KNN）被供给作为熊猫数据帧，其COLS和索引中的相同的原始数据帧的内容(argument data=)。

Answer 2

添加以下行代码后：

df_filled.columns = df_numeric.columns
df_filled.index = df_numeric.index

Answer 3

我看到花哨转嫁给和熊猫的无奈。下面是一个使用递归的方法覆盖一个相当基本的包装。发生在和输出数据框 - 列名不变。这些类型的包装与管道工作。

from fancyimpute import SoftImpute

class SoftImputeDf(SoftImpute):
    """DataFrame Wrapper around SoftImpute"""

    def __init__(self, shrinkage_value=None, convergence_threshold=0.001,
                 max_iters=100,max_rank=None,n_power_iterations=1,init_fill_method="zero",
                 min_value=None,max_value=None,normalizer=None,verbose=True):

        super(SoftImputeDf, self).__init__(shrinkage_value=shrinkage_value, 
                                           convergence_threshold=convergence_threshold,
                                           max_iters=max_iters,max_rank=max_rank,
                                           n_power_iterations=n_power_iterations,
                                           init_fill_method=init_fill_method,
                                           min_value=min_value,max_value=max_value,
                                           normalizer=normalizer,verbose=False)



    def fit_transform(self, X, y=None):

        assert isinstance(X, pd.DataFrame), "Must be pandas dframe"

        for col in X.columns:
            if X[col].isnull().sum() < 10:
                X[col].fillna(0.0, inplace=True)

        z = super(SoftImputeDf, self).fit_transform(X.values)
        return pd.DataFrame(z, index=X.index, columns=X.columns)

Answer 4

我真的很感激@ jander081的做法，并在其上扩大一点点处理设置类别列。我有一个问题，即在分类栏会得到取消设置和创建培训过程中出现错误，所以修改了代码如下：

from fancyimpute import SoftImpute
import pandas as pd

class SoftImputeDf(SoftImpute):
    """DataFrame Wrapper around SoftImpute"""

    def __init__(self, shrinkage_value=None, convergence_threshold=0.001,
                 max_iters=100,max_rank=None,n_power_iterations=1,init_fill_method="zero",
                 min_value=None,max_value=None,normalizer=None,verbose=True):

        super(SoftImputeDf, self).__init__(shrinkage_value=shrinkage_value, 
                                           convergence_threshold=convergence_threshold,
                                           max_iters=max_iters,max_rank=max_rank,
                                           n_power_iterations=n_power_iterations,
                                           init_fill_method=init_fill_method,
                                           min_value=min_value,max_value=max_value,
                                           normalizer=normalizer,verbose=False)



    def fit_transform(self, X, y=None):

        assert isinstance(X, pd.DataFrame), "Must be pandas dframe"

        for col in X.columns:
            if X[col].isnull().sum() < 10:
                X[col].fillna(0.0, inplace=True)

        z = super(SoftImputeDf, self).fit_transform(X.values)
        df = pd.DataFrame(z, index=X.index, columns=X.columns)
        cats = list(X.select_dtypes(include='category'))
        df[cats] = df[cats].astype('category')

        # return pd.DataFrame(z, index=X.index, columns=X.columns)
        return df