我下面有以下数据。
+----+-------------+----------+--------+------+-------+-------+---------+
| ID | PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare |
+----+-------------+----------+--------+------+-------+-------+---------+
| 0 | 1 | 0 | 3 | 22.0 | 1 | 0 | 7.2500 |
| 1 | 2 | 1 | 1 | 38.0 | 1 | 0 | 71.2833 |
| 2 | 3 | 1 | 3 | 26.0 | 0 | 0 | 7.9250 |
| 3 | 4 | 1 | 1 | 35.0 | 1 | 0 | 53.1000 |
| 4 | 5 | 0 | 3 | 35.0 | 0 | 0 | 8.0500 |
| 5 | 6 | 0 | 3 | NaN | 0 | 0 | 8.4583 |
+----+-------------+----------+--------+------+-------+-------+---------+
转换后（通过插补），假定数据类型从int / bool变为float。
+----+-------------+----------+--------+-----------+-------+-------+---------+
| ID | PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare |
+----+-------------+----------+--------+-----------+-------+-------+---------+
| 0 | 1.0 | 0.0 | 3.0 | 22.000000 | 1.0 | 0.0 | 7.2500 |
| 1 | 2.0 | 1.0 | 1.0 | 38.000000 | 1.0 | 0.0 | 71.2833 |
| 2 | 3.0 | 1.0 | 3.0 | 26.000000 | 0.0 | 0.0 | 7.9250 |
| 3 | 4.0 | 1.0 | 1.0 | 35.000000 | 1.0 | 0.0 | 53.1000 |
| 4 | 5.0 | 0.0 | 3.0 | 35.000000 | 0.0 | 0.0 | 8.0500 |
| 5 | 6.0 | 0.0 | 3.0 | 28.000000 | 0.0 | 0.0 | 8.4583 |
+----+-------------+----------+--------+-----------+-------+-------+---------+
我的代码如下：
import pandas as pd
import numpy as np
#https://www.kaggle.com/shivamp629/traincsv/downloads/traincsv.zip/1
data = pd.read_csv("train.csv")
data2 = data[['PassengerId', 'Survived','Pclass','Age','SibSp','Parch','Fare']].copy()
from sklearn.preprocessing import Imputer
fill_NaN = Imputer(missing_values=np.nan, strategy='median', axis=0)
data2_im = pd.DataFrame(fill_NaN.fit_transform(data2), columns = data2.columns)
data2_im
有没有办法保留数据类型？谢谢你的帮助。
最佳答案
无法保留dtype，因为sklearn在转换之前从data2提取基础数据，并出于性能原因将dtypes均质化为float。
您始终可以使用astype恢复初始dtype：
v = fill_NaN.fit_transform(data2)
df = pd.DataFrame(v, columns=data2.columns).astype(data2.dtypes.to_dict())
df
PassengerId Survived Pclass Age SibSp Parch Fare
0 1 0 3 22.0 1 0 7.2500
1 2 1 1 38.0 1 0 71.2833
2 3 1 3 26.0 0 0 7.9250
3 4 1 1 35.0 1 0 53.1000
4 5 0 3 35.0 0 0 8.0500
5 6 0 3 35.0 0 0 8.4583