如何在pandas DataFrame中分组统计次数并计算百分比占比

直接运行以下代码即可得到符合要求的结果：

import pandas as pd

# 示例数据初始化
df = pd.DataFrame({
    "column A": ["Atlanta", "Atlanta", "New York", "New York","New York"], 
    "column B": ["AT", "AT", "NY", "NY", "AT"]
})

# 1. 新增occurrence_columnA：统计column A各值的出现次数
df['occurrence_columnA'] = df.groupby('column A')['column A'].transform('count')

# 2. 新增occurrence_both_columns：统计column A和column B值组合的出现次数
df['occurrence_both_columns'] = df.groupby(['column A', 'column B'])['column A'].transform('count')

# 3. 新增Ratio：计算百分比占比，如需和示例一致保留两位截断小数可把round参数改为round(2, rounding_mode='trunc')
df['Ratio'] = (df['occurrence_both_columns'] / df['occurrence_columnA'] * 100).round(2).apply(lambda x: f"{x}%")

# 可选：和示例输出对齐，把column A里的New York修改为Newyork
df['column A'] = df['column A'].replace('New York', 'Newyork')

运行后输出结果如下：

内容的提问来源于stack exchange，提问作者Daven1