如何在Pandas中添加带前缀的唯一标识符列

Got it, let's break down how to add that custom unique ID column with the "ACC-" prefix based on the City values. Here are a few reliable approaches depending on your needs:

方法1：自动按City出现顺序分配ID（简洁版） #

If you just need each unique city to get a sequential number starting from 1 (same cities share the same ID regardless of their order), use groupby().ngroup():

import pandas as pd

# 示例DataFrame
df = pd.DataFrame({'City': ['Atlanta', 'Newyork', 'Atlanta', 'Chicago', 'Newyork']})

# 直接生成目标列
df['ACC_ID'] = 'ACC-' + (df.groupby('City').ngroup() + 1).astype(str)

ngroup() assigns a unique integer to each group starting from 0, so we add 1 to make it start at 1, then convert to string and prepend "ACC-".

方法2：用factorize显式生成编码 #

Another common method is pd.factorize(), which maps unique values to integer codes. This works similarly but lets you see the intermediate ID if needed:

# 生成City对应的数字ID（从1开始）
df['City_Code'] = pd.factorize(df['City'])[0] + 1

# 拼接前缀得到最终列
df['ACC_ID'] = 'ACC-' + df['City_Code'].astype(str)

方法3：手动指定City对应的ID（自定义映射） #

If you need to enforce specific numbers for certain cities (e.g., Atlanta must be ACC-1, Newyork must be ACC-2 no matter their order in the DataFrame), create a custom mapping dictionary:

# 自定义City到ID的映射
city_to_id = {'Atlanta': 1, 'Newyork': 2, 'Chicago': 3}

# 应用映射并拼接前缀
df['ACC_ID'] = 'ACC-' + df['City'].map(city_to_id).astype(str)

最终效果示例 #

After running any of the above, your DataFrame will look like this:

内容的提问来源于stack exchange，提问作者Daven1