duplicates

Remove duplicate rows in a table

由 旧城冷巷雨未停 提交于 2019-11-27 09:01:13
问题 I have a table contains order information like below: Order table : As we can see from that table, each order_no has several duplicates. So what I want is to keep only one row for each order_no (no matter which one it is) Is anyone knows how to do this? (FYI, I am using Oracle 10) 回答1: This should work, even in your ancient and outdated Oracle version: delete from order_table where rowid not in (select min(rowid) from order_table group by order_no); 回答2: If you don't care which row you get

Removing duplicate elements from a List

由 橙三吉。 提交于 2019-11-27 08:59:06
I have developed an array list. ArrayList<String> list = new ArrayList<String>(); list.add("1"); list.add("2"); list.add("3"); list.add("3"); list.add("5"); list.add("6"); list.add("7"); list.add("7"); list.add("1"); list.add("10"); list.add("2"); list.add("12"); But as seen above it contains many duplicate elements. I want to remove all duplicates from that list. For this I think first I need to convert the list into a set. Does Java provide the functionality of converting a list into a set? Are there other facilities to remove duplicates from a list? You can convert to a Set with: Set<String

Check for duplicates before inserting

由 感情迁移 提交于 2019-11-27 08:58:01
Before inserting into the database, I'm using the following code to check for duplicates. To me, a duplicate is only considered a duplicate when name , description , price , city , and enddate match. foreach($states_to_add as $item) { $dupesql = "SELECT COUNT(*) FROM table WHERE ( name = '$name' AND description = '$description' AND manufacturer = '$manufacturer' AND city ='$city' AND price = '$price' AND enddate = '$end_date' )"; $duperaw = mysql_query($dupesql); if($duperaw > 0) { echo nl2br("$name already exists in $city \n"); } else { $sql = "INSERT INTO table (..... (here go the values to

How to drop_duplicates

由 。_饼干妹妹 提交于 2019-11-27 08:41:30
问题 I have raw data as following example. At instant t1, a variable has a value x1, this variable should be recorded at instant t2 if and only if its value is not equal to x1. There is a way to compare a value in dataframes in python with the previous value and delete it if it's the same. I tried follow function, but it doesn't work.Please help. df time Variable Value 2014-07-11 19:50:20 Var1 10 2014-07-11 19:50:30 Var1 20 2014-07-11 19:50:40 Var1 20 2014-07-11 19:50:50 Var1 30 2014-07-11 19:50

Combine two tables into a new one so that select rows from the other one are ignored

由 时光毁灭记忆、已成空白 提交于 2019-11-27 08:33:30
问题 I have two tables that have identical columns. I would like to join these two tables together into a third one that contains all the rows from the first one and from the second one all the rows that have a date that doesn't exist in the first table for the same location. Example: transactions: date |location_code| product_code | quantity ------------+------------------+--------------+---------- 2013-01-20 | ABC | 123 | -20 2013-01-23 | ABC | 123 | -13.158 2013-02-04 | BCD | 234 | -4.063

How to avoid the “Duplicate status message” error in using Facebook SDK in iOS?

由 你说的曾经没有我的故事 提交于 2019-11-27 08:11:36
问题 I want to post the several same messages onto my feed/wall in an iOS application. From the 2nd try, I receive this error - (#506) Duplicate status message. How can I solve it? 回答1: You can't. That is Facebook's way to tell you to stop spamming. Sorry if it sounds slightly mean - but posting the same message over and over and over again is spamming, and its not good. The error message you are getting describes the problem - you are posting the same status message. It is a special error message

Merge items on dataframes with duplicate values

由 匆匆过客 提交于 2019-11-27 07:50:22
问题 So I have a dataframe (or series) where there are always 4 occurrences of each of column 'A', like this: df = pd.DataFrame([['foo'], ['foo'], ['foo'], ['foo'], ['bar'], ['bar'], ['bar'], ['bar']], columns=['A']) A 0 foo 1 foo 2 foo 3 foo 4 bar 5 bar 6 bar 7 bar I also have another dataframe, with values like the ones found in column A, but they don't always have 4 values. They also have more columns, like this: df_key = pd.DataFrame([['foo', 1, 2], ['foo', 3, 4], ['bar', 5, 9], ['bar', 2, 4],

MySQL ON DUPLICATE KEY UPDATE while inserting a result set from a query

由 你离开我真会死。 提交于 2019-11-27 07:39:05
问题 I am querying from tableONE and trying to insert the result set into tableTWO. This can cause a duplicate key error in tableTWO at times. So i want to ON DUPLICATE KEY UPDATE with the NEW determined value from the tableONE result set instead of ignoring it with ON DUPLICATE KEY UPDATE columnA = columnA . INSERT INTO `simple_crimecount` (`date` , `city` , `crimecount`)( SELECT `date`, `city`, count(`crime_id`) AS `determined_crimecount` FROM `big_log_of_crimes` GROUP BY `date`, `city` ) ON

Potential Duplicates Detection, with 3 Severity Level

由 微笑、不失礼 提交于 2019-11-27 07:26:25
问题 I wanna make a program that detect a potential duplicates with 3 severity level. let consider my data is only in two column, but with thousands row. data in second column delimited only with comma. data example : Number | Material 1 | helmet,valros,42 2 | helmet,iron,knight 3 | valros,helmet,42 4 | knight,helmet 5 | valros,helmet,42 6 | plain,helmet 7 | helmet, leather and my 3 levels is : very high : A,B,C vs A,B,C high : A,B,C vs B,C,A so so : A,B,C vs A,B so far i just can make the first

Detect duplicate MP3 files with different bitrates and/or different ID3 tags?

由 99封情书 提交于 2019-11-27 07:20:17
How could I detect (preferably with Python) duplicate MP3 files that can be encoded with different bitrates (but they are the same song) and ID3 tags that can be incorrect? I know I can do an MD5 checksum of the files content but that won't work for different bitrates. And I don't know if ID3 tags have influence in generating the MD5 checksum. Should I re-encode MP3 files that have a different bitrate and then I can do the checksum? What do you recommend? The exact same question that people at the old AudioScrobbler and currently at MusicBrainz have worked on since long ago. For the time being
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