使用熊猫替换缺失值

Question

{'Country': 'USA', 'Age': '52', 'Sal': '12345', 'OnWork': 'No'}
{'Country': 'UK', 'Age': '23', 'Sal': '1142', 'OnWork': 'Yes'}
{'Country': 'MAL', 'Age': '25', 'Sal': '4456', 'OnWork': 'No'}
{'Country': 'MAL', 'Age': '25', 'Sal': '4456', 'OnWork': 'No'}
{'Country': 'MAL', 'Age': '?', 'Sal': '2345', 'OnWork': 'Yes'}
{'Country': 'MAL', 'Age': '25', 'Sal': '3342', 'OnWork': 'Yes'}
{'Country': 'MAL', 'Age': '25', 'Sal': '3452', 'OnWork': 'No'}
{'Country': 'MAL', 'Age': '?', 'Sal': '3562', 'OnWork': 'No'}

在这里，我必须根据“ OnWork”值替换丢失的平均值。组是，其平均值进入第5行年龄。组编号及其值应转到最后一行。

df = pd.read_csv("Mycal.csv", na_values = missing_values, nrows=50)

查找并替换平均值（正在运行）

df["F8"].fillna(df['F8'].mean(), inplace=True)

在这里我可以找到平均值，但是我不能替换它。

df[df["Class"]=="Yes"]["F8"].mean()

我希望“是”值可以分组并填充“缺失”值，而“平均值”可以填充“否”。请帮助我

Answer 1

将mask和fillna用作：

df['Age'] = df['Age'].mask(df['Age'].eq('?'), np.nan).astype(float)
df['Age'] = (df['Age'].fillna(df.groupby('OnWork')['Age'].transform(np.nanmean))
                      .astype(int))

print(df)
  Country  Age    Sal OnWork
0     USA   52  12345     No
1      UK   23   1142    Yes
2     MAL   25   4456     No
3     MAL   25   4456     No
4     MAL   24   2345    Yes
5     MAL   25   3342    Yes
6     MAL   25   3452     No
7     MAL   31   3562     No

如果要一次替换多个列值，请使用：

df = df.fillna(df.groupby('OnWork').transform('mean'))

Answer 2

如果您要平均替换每个组的缺失值，那么这是解决方法之一：

df_mean = df.groupby('Class')['F8'].mean().reset_index()
df_mean.columns = ['Class','F8_mean']
df = pd.merge(df, df_mean, on='Class', how='left')
df.loc[df['F8'].isnull(), 'F8'] = df['F8_mean']
df.drop('F8_mean', axis=1, inplace=True)

Answer 3

#import libries
import pandas as pd
import numpy as np

# Data dictionary
data_dict = {'Country': ['USA','UK','MAL','MAL','MAL','MAL','MAL','MAL'], 
              'Age': ['52','23','25','25','?','25','25','?'], 'Sal': ['12345','1142','4456','4456','2345','3342','3452','3562'], 
              'OnWork': ['No','Yes','No','No','Yes','Yes','No','No']} 

# Convert dictionary to dataframe
df = pd.DataFrame(data_dict)

# print input df
print(df)

**

       Country Age  Sal    OnWork
    0     USA  52  12345     No
    1      UK  23   1142    Yes
    2     MAL  25   4456     No
    3     MAL  25   4456     No
    4     MAL   ?   2345    Yes
    5     MAL  25   3342    Yes
    6     MAL  25   3452     No
    7     MAL   ?   3562     No

**

# '?' Values replace with NaN
df.Age=df.Age.where(df.Age!='?')

# Convert string values to numeric 
df["Age"] = pd.to_numeric(df["Age"])

# Get mean values Separately
mean_list = df.groupby('OnWork')['Age'].mean().astype(int)

# print mean values 
print(mean_list)

**

No     31
Yes    24

**

# Replace the missing age value 
df['Age'] = df.apply(
    lambda row: mean_list['Yes'] if np.isnan(row['Age'])&(row['OnWork']=='Yes')  else mean_list['No'] if np.isnan(row['Age'])&(row['OnWork']=='No') else row['Age'],
    axis=1
)

# print final df
print(df)

**

  Country   Age    Sal OnWork
0     USA  52.0  12345     No
1      UK  23.0   1142    Yes
2     MAL  25.0   4456     No
3     MAL  25.0   4456     No
4     MAL  24.0   2345    Yes
5     MAL  25.0   3342    Yes
6     MAL  25.0   3452     No
7     MAL  31.0   3562     No

**