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pd.duplicated() groups of duplicates


pd.duplicated() groups of duplicates

By : Anh Duc Nguyen
Date : November 22 2020, 04:01 AM
wish help you to fix your issue Use duplicated with keep='False' for filter all dupes by boolean indexing, then sort_values, for numbers per groups use ngroup, for count transform with size:
code :
cols = ['A','B']
df1 = df[df.duplicated(subset=cols,keep=False)].copy()
df1 = df1.sort_values(cols)
df1['group'] = 'g' + (df1.groupby(cols).ngroup() + 1).astype(str)
df1['duplicate_count'] = df1.groupby(cols)['origin'].transform('size')
print (df1)
   A  B origin group  duplicate_count
0  1  Q  file1    g1                2
1  1  Q  file2    g1                2
2  2  R  file3    g2                3
3  2  R  file4    g2                3
4  2  R  file5    g2                3
6  3  L  file7    g3                2
7  3  L  file8    g3                2


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SSRS row groups are duplicated

SSRS row groups are duplicated


By : user6297694
Date : March 29 2020, 07:55 AM
it should still fix some issue The problem was that some categories included trailing blanks, resulting in categories such as 'Hoses' and 'Hoses '. And easy fix by eliminating trailing blanks in the table.
Differentiate between groups of duplicated values

Differentiate between groups of duplicated values


By : Nim
Date : March 29 2020, 07:55 AM
like below fixes the issue Suppose I have the following table table: , Does this do what you want?
code :
select groupnum, column1, column2, seqnum
from (select t.*, count(*) over (partition by column1, column2) as cnt,
             dense_rank() over (order by column1, column2) as groupnum,
             row_number() over (partition by column1, column2 order by column1) as seqnum
      from table t
     ) t
where cnt > 1
order by groupnum;
merge data by groups and by common ID (IDs duplicated outside groups)

merge data by groups and by common ID (IDs duplicated outside groups)


By : Nick K
Date : March 29 2020, 07:55 AM
I hope this helps . This is not a duplicated question to How to join (merge) data frames. You can perform the left.merge inside the group but not to the whole data set. The ids are unique inside group, not acroos group. By not grouping and using a left.merge, you willl mess up the data. , You can just include group in the by argument for the join:
code :
a %>% left_join(b, by=c("id","group"))
  id v group v1
1  1 1     a NA
2  2 1     a 10
3  3 1     a NA
4  4 1     a 10
5  1 1     b NA
6  2 1     b 10
7  3 1     b NA
8  4 1     b 10
Find duplicated rows, multiply a certain column by number of duplicates, drop duplicated rows

Find duplicated rows, multiply a certain column by number of duplicates, drop duplicated rows


By : Again
Date : March 29 2020, 07:55 AM
it helps some times I think this question is nothing more of figuring out how to get a count of the occurrences of each unique row. If a row occurs only once, this number is one. If it occurs more often, it will be > 1. This count you can then use to multiply, filter, etc.
This nice one-liner (taken from How to count duplicate rows in pandas dataframe?) creates an extra column with the number of occurrences of each row:
R seems duplicated() to select the wrong duplicates

R seems duplicated() to select the wrong duplicates


By : najmadin
Date : March 29 2020, 07:55 AM
Does that help I've noticed a couple of times now that when I'm using R to identify duplicates, sometimes it seems to identify the wrong cases. , We need to apply duplicated on a data.frame or matrix or vector
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