Python 如何在 Pandas 中按数据框分组并保留列
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How to GroupBy a Dataframe in Pandas and keep Columns
提问by Adrian Ribao
given a dataframe that logs uses of some books like this:
给定一个数据框，它记录了一些书籍的使用情况，如下所示：
Name Type ID
Book1 ebook 1
Book2 paper 2
Book3 paper 3
Book1 ebook 1
Book2 paper 2
I need to get the count of all the books, keeping the other columns and get this:
我需要计算所有书籍的数量，保留其他列并得到这个：
Name Type ID Count
Book1 ebook 1 2
Book2 paper 2 2
Book3 paper 3 1
How can this be done?
如何才能做到这一点？
Thanks!
谢谢！
采纳答案by EdChum
You want the following:
您需要以下内容：
In [20]:
df.groupby(['Name','Type','ID']).count().reset_index()
Out[20]:
Name Type ID Count
0 Book1 ebook 1 2
1 Book2 paper 2 2
2 Book3 paper 3 1
In your case the 'Name', 'Type' and 'ID' cols match in values so we can groupbyon these, call countand then reset_index.
在您的情况下，“名称”、“类型”和“ID”列的值匹配，因此我们可以groupby对这些列进行调用count，然后调用reset_index.
An alternative approach would be to add the 'Count' column using transformand then call drop_duplicates:
另一种方法是使用添加“计数”列transform，然后调用drop_duplicates：
In [25]:
df['Count'] = df.groupby(['Name'])['ID'].transform('count')
df.drop_duplicates()
Out[25]:
Name Type ID Count
0 Book1 ebook 1 2
1 Book2 paper 2 2
2 Book3 paper 3 1
回答by jpobst
I think as_index=False should do the trick.
我认为 as_index=False 应该可以解决问题。
df.groupby(['Name','Type','ID'], as_index=False).count()
回答by NeStack
If you have many columns in a df it makes sense to use df.groupby(['foo']).agg(...), see here. The .agg()function allows you to choose what to do with the columns you don't want to apply operations on. If you just want to keep them, use .agg({'col1': 'first', 'col2': 'first', ...}. Instead of 'first', you can also apply 'sum', 'mean'and others.
如果 df 中有很多列，使用它是有意义的df.groupby(['foo']).agg(...)，请参见此处。该.agg()函数允许您选择如何处理不想对其应用操作的列。如果您只想保留它们，请使用.agg({'col1': 'first', 'col2': 'first', ...}. 相反的'first'，你也可以申请'sum'，'mean'和其他人。
