group-by

问题 ( Data sample and attempts at the end of the question ) With a dataframe such as this: Type Class Area Decision 0 A 1 North Yes 1 B 1 North Yes 2 C 2 South No 3 A 3 South No 4 B 3 South No 5 C 1 South No 6 A 2 North Yes 7 B 3 South Yes 8 B 1 North No 9 C 1 East No 10 C 2 West Yes How can I find what percentage of each type [A, B, C, D] that belongs to each area [North, South, East, West] ? Desired output: North South East West A 0.66 0.33 0 0 B 0.5 0.5 0 0 C 0 0.5 0.25 0.25 My best attempt so

问题 I have some data like this: row id 1 1 2 36 3 37 4 38 5 50 6 51 I would like to query it to look like this: row id group 1 1 1 2 36 2 3 37 2 4 38 2 5 50 3 6 51 3 ... so that I can GROUP BY where the numbers are consecutively sequential. Also, looping/cursoring is out of the question since I'm working with a pretty large set of data, thanks. 回答1: create table #temp ( IDUnique int Identity(1,1), ID int, grp int ) Insert into #temp(ID) Values(1) Insert into #temp(ID) Values(36) Insert into #temp

问题 I have some data like this: row id 1 1 2 36 3 37 4 38 5 50 6 51 I would like to query it to look like this: row id group 1 1 1 2 36 2 3 37 2 4 38 2 5 50 3 6 51 3 ... so that I can GROUP BY where the numbers are consecutively sequential. Also, looping/cursoring is out of the question since I'm working with a pretty large set of data, thanks. 回答1: create table #temp ( IDUnique int Identity(1,1), ID int, grp int ) Insert into #temp(ID) Values(1) Insert into #temp(ID) Values(36) Insert into #temp

问题 I have the folowwing SQL query SELECT CustomerID FROM sales WHERE `Date` <= '2012-01-01' GROUP BY CustomerID The query is executed over 11400000 rows and runs very slow. It takes over 3 minutes to execute. If I remove the group-by part, this runs below 1 second. Why is that? MySQL Server version is '5.0.21-community-nt' Here is the table schema: CREATE TABLE `sales` ( `ID` int(11) NOT NULL auto_increment, `DocNo` int(11) default '0', `CustomerID` int(11) default '0', `OperatorID` int(11)

问题 As I was advised by a good man and programmer I should simplify my table. So far I have made a new table (x-month,y-cities,value-Nettotal) it works, but still I didn't understand why it can't group the values (nettotal) by cities. It's OK with month, but the values just come starting from left to right without any 0 left behind. Anyway I hope you will understand everything from the source: here are the queries: <cfquery name="GET_SALES_TOTAL" datasource="#dsn#"> SELECT SUM(COALESCE(nettotal,0

问题 As I was advised by a good man and programmer I should simplify my table. So far I have made a new table (x-month,y-cities,value-Nettotal) it works, but still I didn't understand why it can't group the values (nettotal) by cities. It's OK with month, but the values just come starting from left to right without any 0 left behind. Anyway I hope you will understand everything from the source: here are the queries: <cfquery name="GET_SALES_TOTAL" datasource="#dsn#"> SELECT SUM(COALESCE(nettotal,0

问题 If we run the following code, from itertools import groupby s = '1223' r = groupby(s) x = list(r) a = [list(g) for k, g in r] print(a) b =[list(g) for k, g in groupby(s)] print(b) then surprisingly the two output lines are DIFFERENT: [] [['1'], ['2', '2'], ['3']] But if we remove the line "x=list(r)", then the two lines are the same, as expected. I don't understand why the list() function will change the groupby result. 回答1: The result of groupby , as with many objects in the itertools

问题 This question already has answers here : Recode dates to study day within subject (2 answers) Closed last month . I have data structured as below: ID Day Desired Output 1 1 1 1 1 1 1 1 1 1 2 2 1 2 2 1 3 3 2 4 1 2 4 1 2 5 2 3 6 1 3 6 1 Is it possible to create a sequence for the desired output without using a loop? The dataset is quite large so a loop won't work, is it possible to do this with the dplyr package or maybe a combination of cumsum/diff? 回答1: An option is to group by 'ID', and then

问题 So I have an array of tuples something like this query_results = [("foo", "bar"), ("foo", "qux"), ("baz", "foo")] I would like to achieve something like: { "foo": ["bar", "qux"], "baz": ["foo"] } So I have tried using this from itertools import groupby grouped_results = {} for key, y in groupby(query_results, lambda x: x[0]): grouped_results[key] = [y[1] for u in list(y)] The issue I have is although the number of keys are correct, the number of values in each array is dramatically lower than

问题 So I have an array of tuples something like this query_results = [("foo", "bar"), ("foo", "qux"), ("baz", "foo")] I would like to achieve something like: { "foo": ["bar", "qux"], "baz": ["foo"] } So I have tried using this from itertools import groupby grouped_results = {} for key, y in groupby(query_results, lambda x: x[0]): grouped_results[key] = [y[1] for u in list(y)] The issue I have is although the number of keys are correct, the number of values in each array is dramatically lower than