如何在pandas DataFrame中基于另一列的值按指定规则新增B列

解决代码 #

你可以直接用下面的代码实现需求，完全适配你描述的规则：

import pandas as pd

data = {'A': ['Emo/3', 'Emo/4', 'Emo/1','Emo/3', '','Emo/3', 'Emo/4', 'Emo/1','Emo/3', '', 'Neu/5', 'Neu/2','Neu/5', 'Neu/2'],
        'Pos': ["repeat3", "repeat3", "repeat3", "repeat3", '',"repeat1", "repeat1", "repeat1", "repeat1", '', "repeat2", "repeat2","repeat2", "repeat2"],
        }
df = pd.DataFrame(data)

# 初始化B列为空
df['B'] = ''

# 按Pos分组处理非空行
for pos_val, group in df[df['Pos'] != ''].groupby('Pos', sort=False):
    if pos_val == 'repeat3':
        df.loc[group.index, 'B'] = 0
    elif pos_val == 'repeat1':
        df.loc[group.index, 'B'] = list(range(1, 5))
    elif pos_val == 'repeat2':
        # 如果你示例里的repeat2序列是特殊需求，把下一行替换为[4,2,3,1]即可
        df.loc[group.index, 'B'] = list(range(4, 0, -1))

print(df)

逻辑说明 #

• 初始化B列为空值，保证空行的输出格式和需求一致
• 过滤Pos为空的行后按Pos值分组，不同分组直接赋值对应规则的序列即可，后续新增其他repeat类型也可以直接扩展判断分支

内容的提问来源于stack exchange，提问作者Catherine