使用按peak_id分组的中位数替换Pandas DataFrame中highpoint_metres缺失值

完整可运行代码（基于你已编写的逻辑扩展） #

import pandas as pd
members = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv")

# 你已实现的按peak_id分组计算中位数逻辑
mediane_peak_id = members[["peak_id","highpoint_metres"]].groupby("peak_id",as_index=False).median()

# ------------- 新增代码开始 -------------
# 需求1：合并中位数到原表，新增列存储对应peak_id的中位数
members = members.merge(mediane_peak_id, on="peak_id", suffixes=("", "_median"))

# 需求2：空值替换，原highpoint_metres为空时用对应中位数填充
members["highpoint_metres"] = members["highpoint_metres"].fillna(members["highpoint_metres_median"])

# 可选：校验填充结果，输出填充后剩余的空值数量
print("填充后highpoint_metres列剩余空值数：", members["highpoint_metres"].isna().sum())

更简便的简化写法（无需单独生成分组中位数表） #

可以直接用groupby+transform一步生成中位数列，代码更简洁：

import pandas as pd
members = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv")

# 一步生成对应peak_id的中位数列
members["highpoint_metres_median"] = members.groupby("peak_id")["highpoint_metres"].transform("median")
# 空值填充
members["highpoint_metres"] = members["highpoint_metres"].fillna(members["highpoint_metres_median"])

逻辑说明 #

• 合并/生成的highpoint_metres_median列即为你需要的每行对应peak_id的highpoint_metres中位数值
• fillna()方法会自动识别highpoint_metres的空值，用同索引位置的中位数值替换，非空值不受影响
• 若输出的剩余空值数不为0，是因为对应peak_id的所有highpoint_metres原本都为空，无有效值计算中位数，这类空值需要额外处理

内容的提问来源于stack exchange，提问作者user17555649