.groupby& .fillna与中位数

问题描述

# Create a groupby object: by_sex_class
by_sex_class = titanic.groupby(["sex","pclass"]).count()

# Write a function that imputes median
def impute_median(series):
    return series.fillna(series.median())

# Impute age and assign to titanic['age']
titanic.age = by_sex_class["age"].transform(impute_median)

# Print the output of titanic.tail(10)
print(titanic.tail(10))

我不清楚我们如何将列["age"]从修改(分组)的df by_sex_class分配给原始(未分组)的titanic的df.

I'm unclear how can we assign the column, ["age"] from modified (grouped) df, by_sex_class, to the original (un-grouped) df, titanic.

难道不把作业弄得一团糟吗?

Wouldn't the assignments be jumbled up?

预先感谢您的解释.

推荐答案

新值通过索引与原始数据帧匹配(当您分组时，仍保留原始索引). /p>

The new values are matched to the original dataframe by the index (when you group, you still keep the original index).

df['age'] = df.groupby(["sex","pclass"])['age'].transform(lambda x: x.fillna(x.median()))

这篇关于.groupby& .fillna与中位数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！