如何将Pandas中包含多条信息的单列拆分为多个独立列？

你当前遇到的单列问题是因为groupby()搭配value_counts()返回的是带MultiIndex的Series，索引层级默认不会转为普通列，只需要用reset_index()即可完成拆分，之后用布尔索引过滤即可。

完整可运行代码 #

import pandas as pd

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

success_members_exp = pd.merge(members, 
expeditions[['expedition_id','termination_reason']], on='expedition_id', how='inner')

# 1. 计算百分比同时把MultiIndex转为普通列
success_members_exp_pourcent = success_members_exp.groupby('expedition_id')['success'].value_counts(normalize=True).reset_index(name='pourcent')
success_members_exp_pourcent['pourcent'] = success_members_exp_pourcent['pourcent'] * 100

# 2. 过滤掉success为False的记录，只保留成功相关的占比数据
filtered_df = success_members_exp_pourcent[success_members_exp_pourcent['success'] == True]

代码说明 #

• reset_index(name='pourcent')：把原来的两层索引expedition_id、success全部转为普通列，同时把统计的占比值命名为pourcent，执行后你会得到3个独立列：expedition_id、success、pourcent
• 布尔索引success_members_exp_pourcent['success'] == True：直接筛选出所有成功相关的行，自动剔除success为False的记录

内容的提问来源于stack exchange，提问作者user17501078