How do I get a list of all the duplicate items using pandas in python?

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I have a list of items that likely has some export issues. I would like to get a list of the duplicate items so I can manually compare them. When I try to use pandas duplicated method, it only returns the first duplicate. Is there a a way to get all of the duplicates and not just the first one?

A small subsection of my dataset looks like this:

ID,ENROLLMENT_DATE,TRAINER_MANAGING,TRAINER_OPERATOR,FIRST_VISIT_DATE
1536D,12-Feb-12,"06DA1B3-Lebanon NH",,15-Feb-12
F15D,18-May-12,"06405B2-Lebanon NH",,25-Jul-12
8096,8-Aug-12,"0643D38-Hanover NH","0643D38-Hanover NH",25-Jun-12
A036,1-Apr-12,"06CB8CF-Hanover NH","06CB8CF-Hanover NH",9-Aug-12
8944,19-Feb-12,"06D26AD-Hanover NH",,4-Feb-12
1004E,8-Jun-12,"06388B2-Lebanon NH",,24-Dec-11
11795,3-Jul-12,"0649597-White River VT","0649597-White River VT",30-Mar-12
30D7,11-Nov-12,"06D95A3-Hanover NH","06D95A3-Hanover NH",30-Nov-11
3AE2,21-Feb-12,"06405B2-Lebanon NH",,26-Oct-12
B0FE,17-Feb-12,"06D1B9D-Hartland VT",,16-Feb-12
127A1,11-Dec-11,"064456E-Hanover NH","064456E-Hanover NH",11-Nov-12
161FF,20-Feb-12,"0643D38-Hanover NH","0643D38-Hanover NH",3-Jul-12
A036,30-Nov-11,"063B208-Randolph VT","063B208-Randolph VT",
475B,25-Sep-12,"06D26AD-Hanover NH",,5-Nov-12
151A3,7-Mar-12,"06388B2-Lebanon NH",,16-Nov-12
CA62,3-Jan-12,,,
D31B,18-Dec-11,"06405B2-Lebanon NH",,9-Jan-12
20F5,8-Jul-12,"0669C50-Randolph VT",,3-Feb-12
8096,19-Dec-11,"0649597-White River VT","0649597-White River VT",9-Apr-12
14E48,1-Aug-12,"06D3206-Hanover NH",,
177F8,20-Aug-12,"063B208-Randolph VT","063B208-Randolph VT",5-May-12
553E,11-Oct-12,"06D95A3-Hanover NH","06D95A3-Hanover NH",8-Mar-12
12D5F,18-Jul-12,"0649597-White River VT","0649597-White River VT",2-Nov-12
C6DC,13-Apr-12,"06388B2-Lebanon NH",,
11795,27-Feb-12,"0643D38-Hanover NH","0643D38-Hanover NH",19-Jun-12
17B43,11-Aug-12,,,22-Oct-12
A036,11-Aug-12,"06D3206-Hanover NH",,19-Jun-12

My code looks like this currently:

df_bigdata_duplicates = df_bigdata[df_bigdata.duplicated(cols='ID')]

There area a couple duplicate items. But, when I use the above code, I only get the first item. In the API reference, I see how I can get the last item, but I would like to have all of them so I can visually inspect them to see why I am getting the discrepancy. So, in this example I would like to get all three A036 entries and both 11795 entries and any other duplicated entries, instead of the just first one. Any help is most appreciated.

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("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       19-Dec-11  0649597-White River VT  0649597-White River VT         9-Apr-12
2    8096        8-Aug-12      0643D38-Hanover NH      0643D38-Hanover NH        25-Jun-12
12   A036       30-Nov-11     063B208-Randolph VT     063B208-Randolph VT              NaN
3    A036        1-Apr-12      06CB8CF-Hanover NH      06CB8CF-Hanover NH         9-Aug-12
26   A036       11-Aug-12      06D3206-Hanover NH                     NaN        19-Jun-12

but I couldn't think of a nice way to prevent repeating ids so many times. I prefer method #2: groupby on the ID.

>>> pd.concat(g for _, g in df.groupby("ID") if len(g) > 1)
       ID ENROLLMENT_DATE        TRAINER_MANAGING        TRAINER_OPERATOR FIRST_VISIT_DATE
6   11795        3-Jul-12  0649597-White River VT  0649597-White River VT        30-Mar-12
24  11795       27-Feb-12      0643D38-Hanover NH      0643D38-Hanover NH        19-Jun-12
2    8096        8-Aug-12      0643D38-Hanover NH      0643D38-Hanover NH        25-Jun-12
18   8096       19-Dec-11  0649597-White River VT  0649597-White River VT         9-Apr-12
3    A036        1-Apr-12      06CB8CF-Hanover NH      06CB8CF-Hanover NH         9-Aug-12
12   A036       30-Nov-11     063B208-Randolph VT     063B208-Randolph VT              NaN
26   A036       11-Aug-12      06D3206-Hanover NH                     NaN        19-Jun-12

Pandas : Find duplicate rows in a Dataframe based on all or , In Python's Pandas library, Dataframe class provides a member function to find duplicate rows based Let's create a Dataframe with some duplicate rows i.e. Select all duplicate rows based on multiple column names in list. Find Duplicate Rows based on all columns. To find & select the duplicate all rows based on all columns call the Daraframe.duplicate () without any subset argument. It will return a Boolean series with True at the place of each duplicated rows except their first occurrence (default value of keep argument is ‘first’ ).

With Pandas version 0.17, you can set 'keep = False' in the duplicated function to get all the duplicate items.

In [1]: import pandas as pd

In [2]: df = pd.DataFrame(['a','b','c','d','a','b'])

In [3]: df
Out[3]: 
       0
    0  a
    1  b
    2  c
    3  d
    4  a
    5  b

In [4]: df[df.duplicated(keep=False)]
Out[4]: 
       0
    0  a
    1  b
    4  a
    5  b

pandas.DataFrame.duplicated, Return boolean Series denoting duplicate rows. Considering certain Only consider certain columns for identifying duplicates, by default use all of the columns. Check for duplicates in list using Set & looking for first duplicate Instead of adding all list elements into set and then looking for duplicates. We can add elements one by one to list and while adding check if it is duplicated or not i.e.

Python Pandas: Find Duplicate Rows In DataFrame Based On All Or , Python Pandas: Find Duplicate Rows In DataFrame Based On All Or on all or selected columns, then use the pandas.dataframe.duplicated() function. list of column names in subset argument of the Dataframe.duplicate()  By Using Set. This is the most popular way of removing duplicates from list. A set is an unordered collection of data type that is mutable. set () automatically removes duplicates. list1 = [9,9,5,6,3,4,1,9,2,4,2] list2 = list (set (list1)) print ("New List : ", list2) Now let’s check the output.

df[df['ID'].duplicated() == True]

This worked for me

Python, Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one Pandas duplicated() method helps in analyzing duplicate values only. subset: Takes a column or list of column label. If False, it consider all of the same values as duplicates. If you like to have a function where you can send your lists, and get them back without duplicates, you can create a function and insert the code from the example above.

As I am unable to comment, hence posting as a separate answer

To find duplicates on the basis of more than one column, mention every column name as below, and it will return you all the duplicated rows set:

df[df[['product_uid', 'product_title', 'user']].duplicated() == True]

Find the duplicate rows of the dataframe in python pandas , In this tutorial we will learn how to find the duplicate rows of the dataframe in python pandas with duplicated() Function. lets see with an example pandas. To demonstrate how this allows us to make new sub-lists: >>> import copy >>> l [ ['foo'], [], []] >>> l_deep_copy = copy.deepcopy(l) >>> l_deep_copy[0].pop() 'foo' >>> l_deep_copy [ [], [], []] >>> l [ ['foo'], [], []] And so we see that the deep copied list is an entirely different list from the original.

How to extract all duplicate rows from a Pandas DataFrame in Python, Kite is a free autocomplete for Python developers. Code faster with the Kite plugin for your code editor, featuring Line-of-Code Completions and cloudless  or you can just use. >>> df['a'].tolist() [1, 3, 5, 7, 4, 5, 6, 4, 7, 8, 9] To drop duplicates you can do one of the following: >>> df['a'].drop_duplicates().values.tolist() [1, 3, 5, 7, 4, 6, 8, 9] >>> list(set(df['a'])) # as pointed out by EdChum [1, 3, 4, 5, 6, 7, 8, 9] share. Share a link to this answer. Copy link.

How to Count Duplicates in Pandas DataFrame, You can capture this data in Python using pandas DataFrame: import pandas You may observe the duplicate values under both the Color and Shape columns. You can If that's the case, you can simply add all the columns needed like this: This is the final part of the Excel Unique Values series that shows how to get a list of distinct / unique values in column using a formula, and how to tweak that formula for different datasets. You will also learn how to quickly get a distinct list using Excel's Advanced Filter, and how to extract unique rows with Duplicate Remover.

How to remove duplicate data from python dataframe, Create Dataframe with Duplicate data. import pandas as pd df = pd. Now we will remove all the duplicate rows from the dataframe using You can drop duplicates from multiple columns as well. just add them as list in  Return boolean Series denoting duplicate rows. Considering certain columns is optional. Parameters subset column label or sequence of labels, optional. Only consider certain columns for identifying duplicates, by default use all of the columns. keep {‘first’, ‘last’, False}, default ‘first’ Determines which duplicates (if any) to mark.

Comments
  • Method #2 is just perfect! Thank you so much.
  • Method #2 fails ("No objects to concatenate") if there are no dups
  • what does g for _ do?
  • @user77005 you might've figured out already, but for everyone's benefit, it reads like this: g for (placeholder, g) in df.groupby('bla') if 'bla'; the underscore is a typical symbol for placeholder of an inevitable argument where we don't want to use it for anything in a lambda-like expression.
  • Method #1 needs to be updated: sort was deprecated for DataFrames in favor of either sort_values or sort_index Related SO Q&A
  • Bingo, there's the answer. So: str or str or boolean... odd API choice. 'all' would be more logical and intuitive IMO.
  • @dreme this isn't syntatically correct, nor does it work. Mismatching ']' and it also doesn't return what they need. Its shorter, but wrong.
  • Oops, you're right @FinancialRadDeveloper, on both counts. I'll delete my comment. Thanks for picking up the error.
  • df[df['ID'].duplicated() == True]This will return all the duplicates
  • Please, can you extend your answer with more detailed explanation? This will be very useful for understanding. Thank you!
  • Welcome to Stack Overflow and thanks for your contribution! It would be kind if you could extend you answer by an explanation. Here you find a guide How to give a good answer. Thanks!