join

Understanding Natural Join in SQL

由 折月煮酒 提交于 2021-01-20 09:04:55
问题 So I am fairly new to SQL and currently I am stuck with the concept of Natural Join. Currently I understand that the Natural Join operator joins tables by matching all columns with the same names and discarding duplicate columns and discarding rows which do not match. So recently I came across a question, pretty basic really, however i couldn't wrap my head around it. So there are 2 relations R(A,B,C) and S(A,B,D) A B C A B D 6 8 7 5 8 7 6 6 7 6 6 7 7 8 6 6 8 6 How many rows would the

超级复杂sql with as join on

由 Deadly 提交于 2021-01-11 03:03:46
---规则2---- /*风险返回码交易*/ /*同一卡号同终端近半小时内，交易笔数澹(含成功及失败)其中任一笔含有风险返回码(扩充后的风险返回码)*/ /*排除以下三种情况*/ /*a.同卡号连续返回码为51且时间跨度在5分钟之内的交易*/ /*b.同卡号连续返回码为55且时间跨度在5分钟之内的交易*/ /*c.同卡号连续返回码为61且时间跨度在5分钟之内的交易*/ select sa_slmt_mcht_id from ( select sa_slmt_mcht_id from tbl_txn_mon1 where tm_rec_crt_time between to_date('2016030300', 'yyyymmddhh24') - 30/24/60 and to_date('2016030400', 'yyyymmddhh24') group by sa_slmt_mcht_id having count(sa_slmt_mcht_id) > 3 ) a where exists( select sa_slmt_mcht_id from tbl_txn_mon1 b where a.sa_slmt_mcht_id=b.sa_slmt_mcht_id and b.sa_rsp_code1 in ('03','04','14','15','21','34','38',

Chained join not filtering as expected

由 生来就可爱ヽ(ⅴ＜●) 提交于 2021-01-07 01:53:08
问题 I have a related question here: Generate month data series with null months included? When I posted I omitted the second join thinking it was trivial and not impact my question - I was wrong there. SELECT TO_CHAR(activity_tasks.start_date, 'MON') AS month, EXTRACT(MONTH FROM activity_tasks.start_date) AS month_num, SUM(cost_planned) FILTER (WHERE activity_tasks.aasm_state IN ('open', 'planned' ) ) AS planned, SUM(cost_actual) FILTER (WHERE activity_tasks.aasm_state IN ('closed' ) ) AS actual

Chained join not filtering as expected

由 怎甘沉沦 提交于 2021-01-07 01:46:51
问题 I have a related question here: Generate month data series with null months included? When I posted I omitted the second join thinking it was trivial and not impact my question - I was wrong there. SELECT TO_CHAR(activity_tasks.start_date, 'MON') AS month, EXTRACT(MONTH FROM activity_tasks.start_date) AS month_num, SUM(cost_planned) FILTER (WHERE activity_tasks.aasm_state IN ('open', 'planned' ) ) AS planned, SUM(cost_actual) FILTER (WHERE activity_tasks.aasm_state IN ('closed' ) ) AS actual

Chained join not filtering as expected

由 有些话、适合烂在心里 提交于 2021-01-07 01:45:18
问题 I have a related question here: Generate month data series with null months included? When I posted I omitted the second join thinking it was trivial and not impact my question - I was wrong there. SELECT TO_CHAR(activity_tasks.start_date, 'MON') AS month, EXTRACT(MONTH FROM activity_tasks.start_date) AS month_num, SUM(cost_planned) FILTER (WHERE activity_tasks.aasm_state IN ('open', 'planned' ) ) AS planned, SUM(cost_actual) FILTER (WHERE activity_tasks.aasm_state IN ('closed' ) ) AS actual

Chained join not filtering as expected

由 强颜欢笑 提交于 2021-01-07 01:44:39
问题 I have a related question here: Generate month data series with null months included? When I posted I omitted the second join thinking it was trivial and not impact my question - I was wrong there. SELECT TO_CHAR(activity_tasks.start_date, 'MON') AS month, EXTRACT(MONTH FROM activity_tasks.start_date) AS month_num, SUM(cost_planned) FILTER (WHERE activity_tasks.aasm_state IN ('open', 'planned' ) ) AS planned, SUM(cost_actual) FILTER (WHERE activity_tasks.aasm_state IN ('closed' ) ) AS actual

Pandas: how to concatenate a MultiIndex DataFrame with a single index DataFrame, and custom ordering

由 允我心安 提交于 2021-01-05 10:46:44
问题 I have a MultiIndex pandas DataFrame df_multi like: import pandas as pd df_multi = pd.DataFrame([['A', 'A1', 0,234,2002],['A', 'A1', 1,324,2550], ['A', 'A1', 2,345,3207],['A', 'A1', 3,458,4560],['A', 'A2', 0,569,1980], ['A', 'A2', 1,657,2314],['A', 'A2', 2,768,4568],['A', 'A2', 3,823,5761]], columns=['Product','Scenario','Time','Quantity','Price']).set_index( ['Product', 'Scenario']) and a single index DataFrame df_single like: df_single = pd.DataFrame([['A', -3,100],['A', -2,100], ['A', -1

Column name labelling in data.table joins

由 元气小坏坏 提交于 2021-01-05 07:15:06
问题 I am trying to join data.table x to z using a non-equi join. Table x contains two columns X1 and X2 that are used as the range to use for joining with column Z1 in z. The current code successfully does the non-equi join however certain columns are removed or renamed. I would like to return the 'ideal' data.table supplied, instead of the one I currently have which I would have to rename columns or join data further to get the 'ideal' data supplied. > library(data.table) > > x <- data.table(Id

Python Pandas Concat “WHERE” a Condition is met

由 本秂侑毒 提交于 2021-01-03 06:23:32
问题 How can I "concat" a specific column from many Python Pandas dataframes, WHERE another column in each of the many dataframes meets a certain condition (colloquially termed condition "X" here). In SQL this would be simple using JOIN clause with WHERE df2.Col2 = "X" and df3.Col2 = "X" and df4.col2 = "X"... etc (which can be run dynamically). In my case, I want to create a big dataframe with all the "Col1"s from each of the many dataframes, but only include the Col1 row values WHERE the

Matching based on different independent tables using data.table in R

由 |▌冷眼眸甩不掉的悲伤 提交于 2021-01-01 09:09:06
问题 I would like to match multiple conditions from independent data tables onto my main data table. How can I do this using the data.table package? What would be the most efficient/fastest way? I have a mock example, with some mock conditions here to illustrate my question: main_data <- data.frame( pnum = c(1,2,3,4,5,6,7,8,9,10), age = c(24,35,43,34,55,24,36,43,34,54), gender = c("f","m","f","f","m","f","m","f","f","m")) data_1 <- data.frame( pnum = c(1,4,5,8,9), value_data_1 = c(1, 2, 1, 1, 1),
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