16

I have a large pandas data fame df. It has quite a few missings. Dropping row/or col-wise is not an option. Imputing medians, means or the most frequent values is not an option either (hence imputation with pandas and/or scikit unfortunately doens't do the trick).

I came across what seems to be a neat package called fancyimpute (you can find it here). But I have some problems with it.

Here is what I do: 

#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))

However, df_filled is a single vector somehow, instead of the filled data frame. How do I get a hold of the data frame with imputations?

Update

I realized, fancyimpute needs a numpay array. I hence converted the df_numeric to a an array using as_matrix(). 

# 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))

The output is a dataframe with the column labels gone missing. Any way to retrieve the labels?

4

4 回答 4

7

在您的代码后添加以下行：

df_filled.columns = df_numeric.columns
df_filled.index = df_numeric.index
于 2017-07-21T14:17:42.727 回答
5

我看到了对花哨的估算和熊猫的挫败感。这是一个使用递归覆盖方法的相当基本的包装器。接收并输出一个数据框 - 完整的列名。这些包装器可以很好地与管道配合使用。

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)
于 2019-02-03T22:23:08.410 回答
3

我真的很欣赏@jander081 的方法，并对其进行了一点扩展以处理设置分类列。我遇到了一个问题，即分类列在训练期间会被取消设置并产生错误，因此修改代码如下：

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
于 2019-06-27T17:25:50.633 回答
2 
df=pd.DataFrame(data=mice.complete(d), columns=d.columns, index=d.index)

由fancyimpute 对象（无论是mice 还是KNN）的方法返回的值作为pandas 数据帧的内容提供np.array，其cols 和indexes 与原始数据帧相同。.complete()(argument data=)

于 2018-03-29T17:38:45.280 回答