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Given a dataframe, I want to get the duplicated indexes, which do not have duplicate values in the columns, and see which values are different.
Specifically, I have this dataframe:
import pandas as pd wget https://www.dropbox.com/s/vmimze2g4lt4ud3/alt_exon_repeatmasker_intersect.bed alt_exon_repeatmasker = pd.read_table('alt_exon_repeatmasker_intersect.bed', header=None, index_col=3) In : alt_exon_repeatmasker.index.is_unique Out: False
And some of the indexes have duplicate values in the 9th column (the type of DNA repetitive element in this location), and I want to know what are the different types of repetitive elements for individual locations (each index = a genome location).
I’m guessing this will require some kind of
groupby and hopefully some
groupby ninja can help me out.
To simplify even further, if we only have the index and the repeat type,
genome_location1 MIR3 genome_location1 AluJb genome_location2 Tigger1 genome_location3 AT_rich
So the output I’d like to see all duplicate indexes and their repeat types, as such:
genome_location1 MIR3 genome_location1 AluJb
EDIT: added toy example
Also useful and very succinct:
Note that this only returns one of the duplicated rows, so to see all the duplicated rows you’ll want this:
df.groupby(level=0).filter(lambda x: len(x) > 1)['type']
filter method for this kind of operation. You can also use masking and transform for equivalent results, but this is faster, and a little more readable too.
filter method was introduced in version 0.12, but it failed to work on DataFrames/Series with nonunique indexes. The issue — and a related issue with
transform on Series — was fixed for version 0.13, which should be released any day now.
Clearly, nonunique indexes are the heart of this question, so I should point out that this approach will not help until you have pandas 0.13. In the meantime, the
transform workaround is the way to go. Be ware that if you try that on a Series with a nonunique index, it too will fail.
There is no good reason why
transform should not be applied to nonunique indexes; it was just poorly implemented at first.
Even faster and better:
As of 9/21/18 Pandas indicates
FutureWarning: 'get_duplicates' is deprecated and will be removed in a future release, instead suggesting the following:
>>> df[df.groupby(level=0).transform(len)['type'] > 1] type genome_location1 MIR3 genome_location1 AluJb
df[df.groupby(level=0).type.count() > 1]
FYI a multi-index:
df[df.groupby(level=[0,1]).type.count() > 1]
This gives you index values along with a preview of duplicated rows
def dup_rows_index(df): dup = df[df.duplicated()] print('Duplicated index loc:',dup[dup == True ].index.tolist()) return dup