在 Pandas 中，如何根据多列的组合创建唯一 ID？
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In Pandas, how to create a unique ID based on the combination of many columns?
提问by ??????
I have a very large dataset, that looks like
我有一个非常大的数据集，看起来像
df = pd.DataFrame({'B': ['john smith', 'john doe', 'adam smith', 'john doe', np.nan], 'C': ['indiana jones', 'duck mc duck', 'batman','duck mc duck',np.nan]})
df
Out[173]:
B C
0 john smith indiana jones
1 john doe duck mc duck
2 adam smith batman
3 john doe duck mc duck
4 NaN NaN
I need to create a ID variable, that is unique for every B-C combination. That is, the output should be
我需要创建一个 ID 变量，它对每个 BC 组合都是唯一的。也就是说，输出应该是
B C ID
0 john smith indiana jones 1
1 john doe duck mc duck 2
2 adam smith batman 3
3 john doe duck mc duck 2
4 NaN NaN 0
I actually dont care about whether the index starts at zero or not, and whether the value for the missing columns is 0 or any other number. I just want something fast, that does not take a lot of memory and can be sorted quickly. I use:
我实际上并不关心索引是否从零开始，以及缺失列的值是 0 还是任何其他数字。我只是想要一些快速的东西，它不需要很多内存并且可以快速排序。我用：
df['combined_id']=(df.B+df.C).rank(method='dense')
but the output is float64and takes a lot of memory. Can we do better?
Thanks!
但输出是float64并且需要大量内存。我们能做得更好吗？谢谢！
回答by jezrael
回答by Nolan Conaway
Making jezrael's answer a little more general (what if the columns were not string?), you can use this compact function:
使 jezrael 的答案更笼统一些（如果列不是字符串怎么办？），您可以使用此紧凑函数：
def make_identifier(df):
str_id = df.apply(lambda x: '_'.join(map(str, x)), axis=1)
return pd.factorize(str_id)[0]
df['combined_id'] = make_identifier(df[['B','C']])
