Pandas - 计算每列的不同值 [英] Pandas - count distinct values per column

问题描述

我有一个如下所示的数据框:

I have a dataframe that looks like this:

Id ActivityId ActivityCode

1   2           3
1   2           4
1   3           2

我需要获取与 ID 相关的不同活动 ID 的计数.

I need to get a count of the distinct Activity IDs that the Id is related to.

在上面的示例中，id 1 将返回 2，因为该 id 有 2 个不同的活动 id.

In the example above, id 1 would return 2 since there're 2 distinct activity ids for that id.

SQL 看起来像这样:

The SQL would look this way:

SELECT COUNT(DISTINCT ActivityId) FROM table GROUP BY Id

我如何在熊猫中做到这一点?

How do I do this in pandas?

(如果可能的话，我想知道是否有办法在字典中获取结果，而无需手动迭代)

(And if possible, I'd like to know if there's a way to get the result in a dictionary, without iterating manually)

推荐答案

我认为你需要 groupby 和 nunique :

I think you need groupby with nunique :

print (df)
   Id  ActivityId  ActivityCode
0   1           2             3
1   1           2             4
2   1           3             2
3   2           8             7

df = df.groupby('Id')['ActivityId'].nunique()
print (df)
Id
1    2
2    1
Name: ActivityId, dtype: int64

对于 dict 添加 Series.to_dict:

And for dict add Series.to_dict:

d = df.groupby('Id')['ActivityId'].nunique().to_dict()
print (d)
{1: 2, 2: 1}

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