group-by

问题 I have a table (data frame) with many columns. Now I would like to average values in one of the columns. It means that I need to group by over all columns except the one over which I need to average. Of course I can write: df.groupby(['col1', 'col2', 'col3', 'col4', 'col5'])['vals'].mean() But it would be nice if I could do something like: df.groupby(['col6'], something='reverse')['vals'].mean() Is it possible in pandas? 回答1: You are searching for the complementary columns to a list you have

问题 Within my application, I have a set of filters that can be applied when listing resources, which builds up a query by adding WHERE clauses, etc. before executing the query. This is using SQL Server 2008. I have two pertinent tables, one which contains some static data about the resource, and another which can contain arbitrary/optional fields pertaining to that resource. First table is something like this (table names & fields changed): CREATE TABLE Resources ( ResID varbinary(28), ... extra

问题 Imagine a table with columns type, date, message. And some rows looking like this (type | date | message): 1 | 1310572318 | Hello 1 | 1310572317 | Hi 2 | 1310572315 | Wassup 3 | 1310572312 | Yo 3 | 1310572311 | Hey 3 | 1310572309 | Eyo 1 | 1310572305 | Hello 1 | 1310572303 | Good Day Is it possible to group them by type, and selecting the latest (ordered by date) so the result would be: 1 | 1310572318 | Hello 2 | 1310572315 | Wassup 3 | 1310572312 | Yo 1 | 1310572305 | Hello I'm pretty sure I

问题 In my elasticsearch index "people" there are the following documents: {"name": "John", "district": 1}, {"name": "Anne", "district": 1}, {"name": "Mary", "district": 2}, {"name": "Bobby", "district": 2}, {"name": "Nick", "district": 1}, {"name": "Bob", "district": 3}, {"name": "Kenny", "district": 1} I would like to get a result of documents that have a district of 2 or 1, but only 2 of them maximum. So if the above were my whole index, I would want it to return: {"name": "John", "district": 1

问题 The problem seems simple but I can't get my head around it, this is for sql server what I have in a table : What I need as a output . cksum id cksum id -2162514679 204 -2162514679 204 1 -2162514679 207 -2162514679 207 1 -2162514679 215 -2162514679 215 1 -2162514679 218 -2162514679 218 1 -2162514679 221 -2162514679 221 1 -2160286363 257 -2160286363 257 2 -2160286363 260 -2160286363 260 2 -2160286363 332 -2160286363 332 2 -2162514679 335 -2162514679 335 3 -2162514679 338 -2162514679 338 3

问题 I previously asked this question to take a oollection of datetime objects and group them by dayOfweek and time So just to recap: I take a collection of DateTime List<DateTime> collectionOfDateTime = GetDateColletion(); and then grouping by dayofWeek and time of day by doing this var byDayOfWeek = collectionOfDateTime.GroupBy(dt => dt.DayOfWeek + "-" + dt.Hour + "-" + dt.Minute); So at this point, I have these grouped by week (consistent time) working perfectly. I now have a new requirement to

问题 The below query is with lots of join table and DB::raw query, Which is working as expected. I would like to groupBy a "landing" which is a field in the table has URL stored. But I would like to groupBy URL without parameter. How can I achieve this? Query below trying to use SUBSTRING_INDEX but didn't worked. $adverts = DB::table('adverts') ->select(DB::raw('count(*) as total'),DB::raw('(SELECT DATE_FORMAT(max(instances.date), \'%d-%m-%Y\') FROM instances WHERE instances.ad_uid = adverts.ad

问题 I know that there are numerous questions about this, like Getting daily averages with pandas and How get monthly mean in pandas using groupby but I'm getting a weird error. Simple data set, with one index column (type timestamp) and one value column. Would like to get the monthly mean of the data. In [76]: df.head() Out[76]: A 2008-01-02 1 2008-01-03 2 2008-01-04 3 2008-01-07 4 2008-01-08 5 However, when I groupby, I get just the groups of the index and not of the value In [74]: df.head()

问题 newbie here - my first foray seemed ok, but this is my 2nd use of pandas. In using Pandas 0.12.0 on windows 7, I read 2 dataframes from SQL One works with groupby as expected, so I'm sure my problem isn't syntax. But on the other, where type(reddf) return pandas.core.frame.DataFrame, when try reddf.groupby( 'any column') I get - last few lines - c:\python27\lib\site-packages\pandas\core\groupby.pyc in __init__(self, index, grouper, name, level, sort) 1197 # no level passed 1198 if not

问题 I have some legacy code I'm maintaining and there's a SQL query in there that I don't understand. I'm hoping someone here can explain the purpose of this to me. The query looks like this: select * from product_performance where merchantid = 2151277 and clickday >= '2011-09-01' group by null; When I run this query without the group by null at the end, I get 44 rows. When I run it with the group by null , I get 1 row -- the first row in the previous set. What is happening here, and what is the