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.

# 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?

2017年12月28日 · 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 _ ;

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 ...

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.

2020年1月9日 · when I use STRING_AGG like this which is accurate and returns the desired result SELECT STRING_AGG(activityName + ' - ' + CONVERT(varchar, createdDate), ' | ') AS tag, deviceID, UserID FROM (SELECT tag, deviceID, UserID FROM tbl_DailyLogMaster WHERE CONVERT(date, createdDate) = CONVERT(date, GETDATE()) GROUP BY userID) a …

2013年8月13日 · Is there a way to write an aggregation function as is used in DataFrame.agg method, that would have access to more than one column of the data that is being aggregated? Typical use cases would be weighted average, weighted standard deviation funcs.

With pandas v0.24.0 the .to_flat_index() function was introduced to columns. Which slightly changes the command to: res.columns = ["_".join(col_name).rstrip('_') for ...

2017年7月31日 · 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.