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. What I can't figure out is how to call the function on column values.
So I can do this:
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. But how can I pass one of the column values? So for example if column d value for each index point is greater than 1. I realise I could just do the following:
people.groupby(people.a > 1).sum()
But I want to know how to do this in a user defined function for future reference.
Something like:
def GroupColFunc(x): if x > 1: return 'Group1' else: return 'Group2'
But how do I call this? I tried
people.groupby(GroupColFunc(people.a))
and similar variants but this does not work.
How do I pass the column values to the function? How would I pass multiple column values e.g. to group on whether people.a > people.b for example?
Simply use the apply method to each dataframe in the groupby object. This is the most straightforward way and the easiest to understand. Notice that the function takes a dataframe as its only argument, so any code within the custom function needs to work on a pandas dataframe.
Instead of using groupby aggregation together, we can perform groupby without aggregation which is applicable to aggregate data separately.
To group by a > 1, you can define your function like:
>>> def GroupColFunc(df, ind, col): ... if df[col].loc[ind] > 1: ... return 'Group1' ... else: ... return 'Group2' ...
An then call it like
>>> 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:
>>> 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:
>>> 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
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With