meanData = all_data.groupby(['Id'])[features].agg('mean') This groups the data by 'Id' value, selects the desired features, and aggregates each group by computing the 'mean' of each group. From the documentation, I know that the argument to .agg can be a string that names a function that will be used to aggregate the data.

Dec 28, 2017 · df["one"].agg("mean") df.agg({"one": "mean"}) df["one"].agg(np.mean) df.agg({"one": np.mean}) Looking at the source code, it appears that when you use average it's casting the DataFrame to be a numpy array, and then mean is taking the row-wise averages by default. Because in the base case (no weights) average actually calls mean. See

A sample query to remove duplicates while using STRING_AGG(). WITH cte AS ( SELECT DISTINCT product FROM activities ) SELECT STRING_AGG(product, ',') products FROM cte; Or you can use the following query. The result is same - SELECT STRING_AGG(product, ',') as products from ( SELECT product FROM Activities GROUP BY product ) as _ ;

For pandas >= 0.25. The functionality to name returned aggregate columns has been reintroduced in the master branch and is targeted for pandas 0.25.

group = df.groupby('date') agg = group.aggregate({'duration': np.sum}) agg duration date 2013-04-01 65 2013-04-02 45 What I'd like to do is sum the duration and count distincts at the same time, but I can't seem to find an equivalent for count_distinct:

# Assume `f1` and `f2` are defined for aggregating. df.groupby("dummy").agg({"returns": f1, "returns": f2}) Obviously, Python doesn't allow duplicate keys. Is there any other manner for expressing the input to agg()? Perhaps a list of tuples [(column, function)] would work better, to allow multiple functions applied to the same column?

May 29, 2018 · 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) Select DISTINCT from the splits; Apply STRING_AGG again to a …

Nov 7, 2019 · df.groupby(['Month']).agg({'Status' : ['count']}) The line above groups the dataframe by Month and counts the number of Status for each month.

Jul 31, 2017 · I have two tables: "debt_period" and "payments". I need to take a view of them. There is a situation when can be a few payments for one period. So in such case, I have to sum the payment values in one column and list the dates separated by a comma in the other column. I use string_agg(to_char(p.payment_date, 'DD.MM.YYYY') and window function.

Mar 27, 2013 · The above uses a window ARRAY_AGG to combine the values of a2.column_1 alongside the other other ARRAY_AGG, using the latter's result as one of the partitioning criteria. Without the DISTINCT, it would produce two {4,5} rows for your example.