71 agg is the same as aggregate. It's callable is passed the columns (Series objects) of the DataFrame, one at a time. You could use idxmax to collect the index labels of the rows with the maximum count:
As @unutbu mentioned, the issue is not with the number of lambda functions but rather with the keys in the dict passed to agg() not being in data as columns. OP seems to have tried using named aggregation, which assign custom column headers to aggregated columns.
df_ret[dcol] = grouped.agg({dcol:min}) return df_ret The function df_wavg() returns a dataframe that's grouped by the "groupby" column, and that returns the sum of the weights for the weights column. Other columns are either the weighted averages or, if non-numeric, the min() function is used for aggregation.
group = df.groupby('date') agg = group.aggregate({'duration': np.sum}) agg['uv'] = df.groupby('date').user_id.nunique() agg duration uv date 2013-04-01 65 2 2013-04-02 45 1 I'm thinking I just need to provide a function that returns the count of distinct items of a Series object to the aggregate function, but I don't have a lot of exposure to the various libraries at my disposal. Also, it ...
Another possibility to get unique strings from STRING_AGG would be to perform these three steps after fetching the comma separated string: Split the string (STRING_SPLIT)
I'm finding a way to aggregate strings from different rows into a single row. I'm looking to do this in many different places, so having a function to facilitate this would be nice. I've tried solu...
I've had success using the groupby function to sum or average a given variable by groups, but is there a way to aggregate into a list of values, rather than to get a single result? (And would this ...
