将 nan-values 替换为其列/属性的平均值

Question

我已经尝试了所有我能想出的东西，希望能得到一些帮助：:) 这是一种将返回数据框的估算部分的方法

from statistics import mean
from unicodedata import numeric


def imputation(df, columns_to_imputed):
    
    
    # Step 1: Get a part of dataframe using columns received as a parameter.
    import pandas as pd
    import numpy as np

    df.set_axis(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'], axis=1, inplace=True)#Sätter rubrikerna

    part_of_df = pd.DataFrame(df.filter(columns_to_imputed, axis=1))
    part_of_df = part_of_df.drop([0], axis=0)
   

    #Step 2: Change the zero values in the columns to np.nan
    part_of_df = part_of_df.replace('0', np.nan)

    # Step 3: Change the nan values to the mean of each attribute (column). 
                  #You can use the apply(), fillna() functions.

    part_of_df = part_of_df.fillna(part_of_df.mean(axis=0)) #####Ive tried everything on this row, can't get it to work. I want to fill each nan-value with the mean of the column its in..

    return part_of_df  ####Im returning this part to see if the nans are replaced but nothings happened...

Answer 1

你在正确的轨道上，你只需要做一个小的改变。 在这里，我创建了一个示例 Df 并介绍了一些 NaN：

dummy_df = pd.DataFrame({"col1":range(5), "col2":range(5)})
dummy_df['col1'][1] = None
dummy_df['col1'][3] = None
dummy_df['col2'][4] = None

得到了这个：

免责声明：不要使用我的赋值方法。 通过loc使用正确的索引。

现在，我使用apply()和lambda遍历每一列并用平均值填充 NaN：

dummy_df = dummy_df.apply(lambda x: x.fillna(x.mean()), axis=0)

这给了我：

希望这可以帮助！