[英]Groupby with User Defined Functions Pandas
I understand that passing a function as a group key calls the function once per index value with the return values being used as the group names. 我知道将函数作为组键传递每个索引值调用一次函数,返回值用作组名。 What I can't figure out is how to call the function on column values.
我无法弄清楚的是如何在列值上调用函数。
So I can do this: 所以我可以这样做:
people = pd.DataFrame(np.random.randn(5, 5),
columns=['a', 'b', 'c', 'd', 'e'],
index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
def GroupFunc(x):
if len(x) > 3:
return 'Group1'
else:
return 'Group2'
people.groupby(GroupFunc).sum()
This splits the data into two groups, one of which has index values of length 3 or less, and the other with length three or more. 这将数据分成两组,其中一组的索引值为3或更小,另一组的长度为3或更多。 But how can I pass one of the column values?
但是我如何传递其中一个列值? So for example if column d value for each index point is greater than 1. I realise I could just do the following:
因此,例如,如果每个索引点的列d值大于1.我意识到我可以执行以下操作:
people.groupby(people.a > 1).sum()
But I want to know how to do this in a user defined function for future reference. 但我想知道如何在用户定义的函数中执行此操作以供将来参考。
Something like: 就像是:
def GroupColFunc(x):
if x > 1:
return 'Group1'
else:
return 'Group2'
But how do I call this? 但是我怎么称呼这个? I tried
我试过了
people.groupby(GroupColFunc(people.a))
and similar variants but this does not work. 和类似的变体,但这不起作用。
How do I pass the column values to the function? 如何将列值传递给函数? How would I pass multiple column values eg to group on whether people.a > people.b for example?
我如何传递多个列值,例如分组是否people.a> people.b?
To group by a > 1, you can define your function like: 要按> 1分组,您可以定义您的函数,如:
>>> def GroupColFunc(df, ind, col):
... if df[col].loc[ind] > 1:
... return 'Group1'
... else:
... return 'Group2'
...
An then call it like 然后称之为
>>> people.groupby(lambda x: GroupColFunc(people, x, 'a')).sum()
a b c d e
Group2 -2.384614 -0.762208 3.359299 -1.574938 -2.65963
Or you can do it only with anonymous function: 或者你只能使用匿名函数:
>>> people.groupby(lambda x: 'Group1' if people['b'].loc[x] > people['a'].loc[x] else 'Group2').sum()
a b c d e
Group1 -3.280319 -0.007196 1.525356 0.324154 -1.002439
Group2 0.895705 -0.755012 1.833943 -1.899092 -1.657191
As said in documentation , you can also group by passing Series providing a label -> group name mapping: 如文档中所述,您还可以通过传递系列提供标签 - >组名称映射进行分组:
>>> mapping = np.where(people['b'] > people['a'], 'Group1', 'Group2')
>>> mapping
Joe Group2
Steve Group1
Wes Group2
Jim Group1
Travis Group1
dtype: string48
>>> people.groupby(mapping).sum()
a b c d e
Group1 -3.280319 -0.007196 1.525356 0.324154 -1.002439
Group2 0.895705 -0.755012 1.833943 -1.899092 -1.657191
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