Have you had a look at Enumerable.Union This method excludes duplicates from the return set. This is different behavior to the Concat method, which returns all the elements in the input sequences including duplicates. List<int> list1 = new List<int> { 1, 12, 12, 5}; List<int> list2 = new List<int> { 12, 5, 7, 9, 1 … Read more
For people who have come here to look for a general answer for duplicate row removal, use !duplicated(): a <- c(rep(“A”, 3), rep(“B”, 3), rep(“C”,2)) b <- c(1,1,2,4,1,1,2,2) df <-data.frame(a,b) duplicated(df) [1] FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE > df[duplicated(df), ] a b 2 A 1 6 B 1 8 C 2 > … Read more
This is much easier in pandas now with drop_duplicates and the keep parameter. import pandas as pd df = pd.DataFrame({“A”:[“foo”, “foo”, “foo”, “bar”], “B”:[0,1,1,1], “C”:[“A”,”A”,”B”,”A”]}) df.drop_duplicates(subset=[‘A’, ‘C’], keep=False)
You could possibly use a LINQ query to do this: int[] s = { 1, 2, 3, 3, 4}; int[] q = s.Distinct().ToArray();
Beginning with MySQL 8.0.19 you can use an alias for that row (see reference). INSERT INTO beautiful (name, age) VALUES (‘Helen’, 24), (‘Katrina’, 21), (‘Samia’, 22), (‘Hui Ling’, 25), (‘Yumie’, 29) AS new ON DUPLICATE KEY UPDATE age = new.age … For earlier versions use the keyword VALUES (see reference, deprecated with MySQL 8.0.20). INSERT … Read more
Use set() to remove duplicates if all values are hashable: >>> your_list = [‘one’, ‘two’, ‘one’] >>> len(your_list) != len(set(your_list)) True
Method #1: print all rows where the ID is one of the IDs in duplicated: >>> import pandas as pd >>> df = pd.read_csv(“dup.csv”) >>> ids = df[“ID”] >>> df[ids.isin(ids[ids.duplicated()])].sort_values(“ID”) ID ENROLLMENT_DATE TRAINER_MANAGING TRAINER_OPERATOR FIRST_VISIT_DATE 24 11795 27-Feb-12 0643D38-Hanover NH 0643D38-Hanover NH 19-Jun-12 6 11795 3-Jul-12 0649597-White River VT 0649597-White River VT 30-Mar-12 18 8096 … Read more
select o.orgName, oc.dupeCount, o.id from organizations o inner join ( SELECT orgName, COUNT(*) AS dupeCount FROM organizations GROUP BY orgName HAVING COUNT(*) > 1 ) oc on o.orgName = oc.orgName
This takes the last. Not the maximum though: In [10]: df.drop_duplicates(subset=”A”, keep=”last”) Out[10]: A B 1 1 20 3 2 40 4 3 10 You can do also something like: In [12]: df.groupby(‘A’, group_keys=False).apply(lambda x: x.loc[x.B.idxmax()]) Out[12]: A B A 1 1 20 2 2 40 3 3 10
The basic idea will be using a nested query with count aggregation: select * from yourTable ou where (select count(*) from yourTable inr where inr.sid = ou.sid) > 1 You can adjust the where clause in the inner query to narrow the search. There is another good solution for that mentioned in the comments, (but … Read more