group-by

问题 I've got a table (col1, col2, ...) with an index on (col1, col2, ...). The table has got millions of rows in it, and I want to run a query: SELECT col1, COUNT(col2) WHERE col1 NOT IN (<couple of exclusions>) GROUP BY col1 Unfortunately, this is resulting in a full table scan of the table, which takes upwards of a minute. Is there any way of getting oracle to use the index on the columns to return the results much faster? EDIT: more specifically, I'm running the following query: SELECT owner,

问题 I want to get results from the database which is grouped by date. The resulting touples should be grouped by date which ranges from 5 am to 5 am. In other words the cutoff should be on 5 am and not midnight 12 am. I can do GROUP BY DAY(timestamp) to group by normal day but what if I have to vary the time and each records should be grouped from 5 am to 5 am ? How should I change the query ? Thanks ? 回答1: Simply subtract 5 hours from each datetime value you have, and then convert it to a date.

问题 I have this simple SQL query - SELECT pid, COUNT(*) AS docs FROM xml_table WHERE suid='2' GROUP BY pid; How do I get this using Django ORM (i.e. django models). Basically I am not getting how to do GROUP BY ? 回答1: XML_table.objects.filter(suid='2').values('pid').annotate(docs=Count('pid')).order_by() Docs 回答2: This works very nicely. from collections import defaultdict count = defaultdict( int ) for doc in XML_Table.objects.filter(suid='2'): count[doc.pid] += 1 It's not SQL. Often it's faster

问题 This question already has answers here : Closed 7 years ago . Possible Duplicate: php group by SUM using multi dimensional array I'm working on creating shopping-cart for a wholesale company. And i'll create also invoice later. It's same logic. Firstly, i multiply paket (package) * paket adeti (package quantity) * fiyatı (price) And i wrote it at the end of list. Now i have to calculate vat. The main problem is we don't know vat ratios exactly before. May be there exist 3 different vat ratios

问题 I'm using PostgreSQL 9.6. I have a table like this: mac sn loc time date vin1 vin2 vin3 1a34 4as11111111 aaaa 7:06:18 1/1/2018 447.42 472.32 682.59 1a34 4as11111111 aaaa 7:06:43 1/1/2018 455.97 476.25 682.59 1a34 4as11111111 aaaa 7:07:35 1/1/2018 470.88 484.2 682.5 I need to calculate the average of the vin1 , vin2 , vin3 within time intervals of 300 sec (5 min). For example, starting from the first time (7:06:18 - 7:11:18), for the dates in range. I can select the data I need with this query

问题 I have the following dataframe: userid | time 1 22.01.2001 13:00 1 22.01.2001 13:05 1 22.01.2001 13:07 2 22.01.2001 14:00 2 22.01.2001 14:04 2 22.01.2001 13:05 2 22.01.2001 13:06 3 22.01.2001 13:20 3 22.01.2001 13:22 4 22.01.2001 13:37 What I want to obtain is a new column per user that stores the average time difference among the consecutive activities: userid | avg_time_diff 1 3.5 #(5 + 2) / 2 2 2 #(4 + 1 + 1) / 3 3 2 4 0 To achieve this, do I need to loop trough each user and calculate the

问题 df.dtypes Close float64 eqId int64 date object IntDate int64 expiry int64 delta int64 ivMid float64 conf float64 Skew float64 psc float64 vol_B category dtype: object gb = df.groupby([df['vol_B'],df['expiry']]) gb.describe() I get a long error message with the final line being AttributeError: 'Categorical' object has no attribute 'flags' When I perform a groupby on each of them separately they each (independently) work great, I just can not perform multiple groupby with one of the variables

问题 I am hitting on a corner case in pandas. I am trying to use the agg fn but without doing a groupby. Say I want an aggregation on the entire dataframe , i.e. from pandas import * DF = DataFrame( randn(5,3), index = list( "ABCDE"), columns = list("abc") ) DF.groupby([]).agg({'a' : np.sum, 'b' : np.mean } ) # <--- does not work And DF.agg( {'a' ... } ) does not work either. My workaround is to do DF['Total'] = 'Total' then do a DF.groupby(['Total']) but this seems a bit artificial. Has anyone

问题 I have a dataframe with a datetime64 column called DT. Is it possible to use groupby to group by financial year from April 1 to March 31? For example, Date | PE_LOW 2010-04-01 | 15.44 ... 2011-03-31 | 16.8 2011-04-02 | 17. ... 2012-03-31 | 17.4 For the above data, I want to group by Fiscal Year 2010-2011 and Fiscal Year 2011-2012 without creating an extra column.* 回答1: With pandas.DatetimeIndex, that is very simple: DT.groupby(pd.DatetimeIndex(DT.Date).shift(-3,freq='m').year) Or if you use

问题 I have a simple table like this: user letter -------------- 1 A 1 A 1 B 1 B 1 B 1 C 2 A 2 B 2 B 2 C 2 C 2 C I want to get the top 2 occurrences of 'letter' per user, like so user letter rank(within user group) -------------------- 1 B 1 1 A 2 2 C 1 2 B 2 or even better: collapsed into columns user 1st-most-occurrence 2nd-most-occurrence 1 B A 2 C B How can I accomplish this in postgres? 回答1: with cte as ( select t.user_id, t.letter, row_number() over(partition by t.user_id order by count(*)