将具有太多值的行转换为列

问题描述

让我们说我有一个像这样的数据集.

Lets say I have a dataset like this.

ID  Item
1   A
2   B
3   A
1   C
2   B
2   B
1   A
3   B

实际数据集具有5万行和8.5K个不同的Item值.

The actual dataset has 50K rows and 8.5K distinct Item values.

现在，我想将每个ID的行转置为列，以便我们为每个用户计算每个项目的值.像下面一样

Now I want to transpose the rows to columns for each Id such that we are counting the values for each item for each user. Like below

ID  A   B   C
1   2   0   1
2   0   3   0
3   1   1   0

现在，如果我想设置编号.项值，然后我可以对它们进行硬编码，以防发生汇总语句.

Now if I have like set no. of Item values then I can hardcode them in case aggregate statement.

类似

select ID, count(case when Item ='A' then 1 else Null end) A,
count(case when Item='B' then 1 else Null end) B... and so on.

但是在这种情况下，我有8.5K唯一物品.像上面在sql中一样，这将是太多的工作.

But in this case I have 8.5K unique Item. This would be too much task to do as above in sql.

如何实现这是Sql和ii)Python.请记住，我有5万行和8.5k个唯一项，因此python循环可能会变慢.

How can I achieve this is Sql and ii) Python. Remember I have 50K rows and 8.5k unique items, so python looping might get a bit slow.

但是我的首要目标是在sql中执行此操作，因为这是50K的示例数据.当数据变得更大时，sql仍然可以很好地保存它，但是python会变慢.在python中，我也可以尝试一些代码.但是当数据超过50K obs时，python也会变慢.

But my first aim is to do this in sql, since this is a sample data of 50K. when data grows bigger sql would still hold it well, but python there would become slow I feel.In python I can try some code too.But again when data is more than 50K obs then python would get it slow too

请告知

不使用Postgresql和使用HANA，因此特定于Postgres的功能可能不起作用.请提出通用的sql方式.

Not using Postgresql and using HANA, so function specific to Postgres might not work. Please suggest generic sql way.

推荐答案

如果您确实想在python中执行此操作，则应查看pandas库

If you did want to do this in python, you should look at the pandas library

df = pd.DataFrame({
    'ID': [1, 2, 3, 1, 2, 2, 1, 3],
    'Item': ['A', 'B', 'A', 'C', 'B', 'B' , 'A', 'B']
})

pd.crosstab(df['ID'], df['Item'])

输出:

Item A  B   C
ID
1   2   0   1
2   0   3   0
3   1   1   0

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