Pandas, order groups in groupby object

Is there no pandas specific forum?

Anyway, given a dataframe D and column A & B, how do you group D by A, then order the groups by the sum of column B?

groupby = D.groupby(‘A’)
groupby = groupby[groupby.B.sum().reset_index().sort_values().index] #this line doesn’t work


Thanks for your answer

I am not sure you can index groupby-object in the same way as a dataframe.
How do you want to use it further? Which dataframe do you want to get at the end?

def groupby_sorted(data,columns,group_key_f,ascending=1):
    gangbang = data.groupby(columns)
    return map(lambda a:(a[0],data.loc[a[1],:]),sorted(gangbang.groups.items(),key=lambda a:group_key_f(data.loc[a[1],:]),reverse=not ascending))

this is what i want

But how do you want to use it further? It does not seem that groupby-objects are designed for preserving the order. Probably, you can remember the proper order of group keys from here, and later use it when you are working with the resulting dataframe.

FYI There is a special Discourse for pandas:

(Not sure if this is really alive anymore, though - definitely doesn’t seem to be getting much traffic.)

This is the (correct, and pretty clean) code that ChatGPT came up with (I only added one more print statement).

import pandas as pd

# Sample DataFrame
data = {'A': ['Group1', 'Group2', 'Group1', 'Group2', 'Group3'],
        'B': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)


# Group by column A and calculate the sum of column B for each group
grouped = df.groupby('A')['B'].sum()

# Sort the groups by the sum in descending order
sorted_groups = grouped.sort_values(ascending=False)

# Display the sorted groups

If you want the df back with columns A and B at the end, you could also add:

sorted_groups = sorted_groups.reset_index()

I should note however that ChatGPT warns beginners:

Using AI to generate code without understanding it can hinder your programming growth and result in poor-quality solutions.

Coding style is always a bit subjective, but if I may comment on that, I’d say that generally speaking it’s better to not use special function names (such as “groupby”) also as variable names: this can quickly make the code unreadable (and invite bugs).

Also, when using pandas I think it’s almost never necessary to us map functions, and lambdas are only needed when calling df.apply(...) on some DataFrame (and even then it may be better to replace them by actual function calls, unless it’s a very simple column conversion).

sorting the groupby only yields a series of the grouped by column though, but i want to iterate the groups (including the rows contained in each group sorted by the sum of each group)

This doesn’t make sense to me - I don’t quite know what you mean by “iterate the groups, including the rows contained…”. An aggregation is a kind of destructive operation: the original rows are merged together, so you cannot iterate over those anymore. A groupby is essentially a reindexing based on some column values, so to make a new date frame and keep the other columns, you have to apply some aggregation to those other columns too, using the implied mapping of indices to sets of indices. If you can apply the same aggregation to each of the other columns, it’s easy:

df.groupby('A')[['B', 'C']).sum() \
  .sort_values('B', ascending=False) \

If you have multiple aggregation functions, you could apply them for instance like this:

df.groupby('A')[df.columns].agg({'B': sum, 'C':list}) \
  .sort_values('B', ascending=False) \

If you want to keep the original B values (using list as aggregation for instance) but yet sort on the sums of the rows in column B, you could do that for instance like this:

def vecsum(series):
      return [sum(x) for x in series]

df.groupby('A')['B'].agg(list) \
  .reset_index() \
  .sort_values('B', axis=0, key=vecsum, ascending=False)

Applied to

        A   B   C
0  Group1  10  10
1  Group2  20  20
2  Group1  30  30
3  Group2  40  40
4  Group3  50  50

this generates

        A         B
1  Group2  [20, 40]
2  Group3      [50]
0  Group1  [10, 30]

(If from that last dataframe you want to go back to a dataframe that contains numbers instead of lists, there is also the explode function. Call df.explode('B') on the last df.)