duplicates

问题 I am trying to delete duplicates in Postgres. I am using this as the base of my query: DELETE FROM case_file as p WHERE EXISTS ( SELECT FROM case_file as p1 WHERE p1.serial_no = p.serial_no AND p1.cfh_status_dt < p.cfh_status_dt ); It works well, except that when the dates cfh_status_dt are equal then neither of the records are removed. For rows that have the same serial_no and the date is the same, I would like to keep the one that has a registration_no (if any do, this column also has NULLS

问题 I have a table of contacts. The table contains a mobile_phone column as well as a home_phone column. I'd like to fetch all pairs of duplicate contacts where a pair is two contacts sharing a phone number. Note that if contact A's mobile_phone matches contact B's home_phone, this is also a duplicate. Here is an example of three contacts that should match. contact_id|mobile_phone|home_phone|other columns such as email.......|... -------------------------------------------------------------------

问题 I have a table of contacts. The table contains a mobile_phone column as well as a home_phone column. I'd like to fetch all pairs of duplicate contacts where a pair is two contacts sharing a phone number. Note that if contact A's mobile_phone matches contact B's home_phone, this is also a duplicate. Here is an example of three contacts that should match. contact_id|mobile_phone|home_phone|other columns such as email.......|... -------------------------------------------------------------------

问题 I have a similar data set to the following: A B C 1 10 5 1 20 1 2 30 1 2 30 1 I'd like to add a column returning 1, until we hit a duplicate of A & B, when I need to return a 0, but only for the second instance, so: A B C D 1 10 5 1 1 20 1 1 2 30 1 1 2 30 1 0 Any help appreciated. 回答1: An option would be df$D <- as.integer(!duplicated(df[c("A", "B")])) df$D #[1] 1 1 1 0 回答2: Just a doodle with library(dplyr) : df %>% group_by(A,B) %>% mutate(D = +((1:n())==1)) Or if you want it to be zero

问题 In a Pandas df, I am trying to drop duplicates across multiple columns. Lots of data per row is NaN . This is only an example, the data is a mixed bag, so many different combinations exist. df.drop_duplicates() IDnum name formNumber 1 NaN AP GROUP 028-11964 2 1364615.0 AP GROUP NaN 3 NaN AP GROUP NaN Hopeful Output: IDnum name formNumber 1 1364615.0 AP GROUP 028-11964 EDIT: If the df.drop_duplicates() looks like this, would it change the solution? : df.drop_duplicates() IDnum name formNumber

问题 anyone know if there's a build in function in R that can return indices of duplicated elements corresponding to the unique elements? For instance I have a vector a <- ["A","B","B","C","C"] unique(a) will give ["A","B","C"] duplicated(a) will give [F,F,T,F,T] is there a build-in function to get a vector of indices for the same length as original vector a, that shows the location a's elements in the unique vecor (which is [1,2,2,3,3] in this example)? i.e., something like the output variable

问题 anyone know if there's a build in function in R that can return indices of duplicated elements corresponding to the unique elements? For instance I have a vector a <- ["A","B","B","C","C"] unique(a) will give ["A","B","C"] duplicated(a) will give [F,F,T,F,T] is there a build-in function to get a vector of indices for the same length as original vector a, that shows the location a's elements in the unique vecor (which is [1,2,2,3,3] in this example)? i.e., something like the output variable

问题 I'm looking for a way to find non-repeated elements in an array in bash. Simple example: joined_arrays=(CVE-2015-4840 CVE-2015-4840 CVE-2015-4860 CVE-2015-4860 CVE-2016-3598) <magic> non_repeated=(CVE-2016-3598) To give context, the goal here is to end up with an array of all package update CVEs that aren't generally available via 'yum update' on a host due to being excluded. The way I came up with doing such a thing is to populate 3 preliminary arrays: available_updates=() #just what 'yum

问题 This is the continuation of the OP1 and OP2. Specifically, the objective is to remove duplicates if more than one dict has the same content for the key paper_title . However, the line throw an error if there inconsistency in the way the list is imputed, such that if there is a combination of dict and str TypeError: string indices must be integers The complete code which generates the aforementioned error is as below: - from itertools import groupby def extract_secondary(): # test_list = [{

问题 This is the continuation of the OP1 and OP2. Specifically, the objective is to remove duplicates if more than one dict has the same content for the key paper_title . However, the line throw an error if there inconsistency in the way the list is imputed, such that if there is a combination of dict and str TypeError: string indices must be integers The complete code which generates the aforementioned error is as below: - from itertools import groupby def extract_secondary(): # test_list = [{