11

我正在学习如何在 Python 上使用 Imputer。

这是我的代码：

df=pd.DataFrame([["XXL", 8, "black", "class 1", 22],
["L", np.nan, "gray", "class 2", 20],
["XL", 10, "blue", "class 2", 19],
["M", np.nan, "orange", "class 1", 17],
["M", 11, "green", "class 3", np.nan],
["M", 7, "red", "class 1", 22]])

df.columns=["size", "price", "color", "class", "boh"]

from sklearn.preprocessing import Imputer

imp=Imputer(missing_values="NaN", strategy="mean" )
imp.fit(df["price"])

df["price"]=imp.transform(df["price"])

但是，这会引发以下错误：ValueError：值的长度与索引的长度不匹配

我的代码有什么问题？？？

感谢您的帮助

4

4 回答 4

17

这是因为Imputer通常使用 DataFrames 而不是 Series。一个可能的解决方案是：

imp=Imputer(missing_values="NaN", strategy="mean" )
imp.fit(df[["price"]])
df["price"]=imp.transform(df[["price"]]).ravel()

# Or even 
imp=Imputer(missing_values="NaN", strategy="mean" )
df["price"]=imp.fit_transform(df[["price"]]).ravel()
于 2016-07-26T10:49:45.727 回答
2

这是Simple Imputer的文档对于 fit 方法，它采用类数组或稀疏矩阵作为输入参数。你可以试试这个：

imp.fit(df.iloc[:,1:2]) 
df['price']=imp.transform(df.iloc[:,1:2])

提供索引位置以适应方法，然后应用转换。

>>> df
   size  price   color    class   boh
 0  XXL    8.0   black  class 1  22.0
 1    L    9.0    gray  class 2  20.0
 2   XL   10.0    blue  class 2  19.0
 3    M    9.0  orange  class 1  17.0
 4    M   11.0   green  class 3   NaN
 5    M    7.0     red  class 1  22.0

同样的方式你可以做boh 

imp.fit(df.iloc[:,4:5])
df['price']=imp.transform(df.iloc[:,4:5])
>>> df
    size  price   color    class   boh
 0  XXL    8.0   black  class 1  22.0
 1    L    9.0    gray  class 2  20.0
 2   XL   10.0    blue  class 2  19.0
 3    M    9.0  orange  class 1  17.0
 4    M   11.0   green  class 3  20.0
 5    M    7.0     red  class 1  22.0

如果我错了，请纠正我。建议将不胜感激。

于 2019-05-31T12:23:29.343 回答
2

我想你想为 imputer 指定轴，然后转置它返回的数组：

import pandas as pd
import numpy as np

df=pd.DataFrame([["XXL", 8, "black", "class 1", 22],
["L", np.nan, "gray", "class 2", 20],
["XL", 10, "blue", "class 2", 19],
["M", np.nan, "orange", "class 1", 17],
["M", 11, "green", "class 3", np.nan],
["M", 7, "red", "class 1", 22]])

df.columns=["size", "price", "color", "class", "boh"]

from sklearn.preprocessing import Imputer

imp=Imputer(missing_values="NaN", strategy="mean",axis=1 ) #specify axis
q = imp.fit_transform(df["price"]).T #perform a transpose operation


df["price"]=q
print df
于 2016-07-26T08:20:18.300 回答
1

简单的解决方案是提供一个二维数组

df=pd.DataFrame([["XXL", 8, "black", "class 1", 22],
["L", np.nan, "gray", "class 2", 20],
["XL", 10, "blue", "class 2", 19],
["M", np.nan, "orange", "class 1", 17],
["M", 11, "green", "class 3", np.nan],
["M", 7, "red", "class 1", 22]])

df.columns=["size", "price", "color", "class", "boh"]

from sklearn.preprocessing import Imputer

imp=Imputer(missing_values="NaN", strategy="mean" )
imp.fit(df[["price"]])

df["price"]=imp.transform(df[["price"]])

df['boh'] = imp.fit_transform(df[['price']])

这是你的数据框

清理数据框

于 2018-08-19T06:23:19.773 回答