Pandas/Python: How to concatenate two dataframes without duplicates?

The simplest way is to just do the concatenation, and then drop duplicates.

>>> df1   A  B0  1  21  3  1>>> df2   A  B0  5  61  3  1>>> pandas.concat([df1,df2]).drop_duplicates().reset_index(drop=True)   A  B0  1  21  3  12  5  6

The reset_index(drop=True) is to fix up the index after the concat() and drop_duplicates(). Without it you will have an index of [0,1,0] instead of [0,1,2]. This could cause problems for further operations on this dataframe down the road if it isn't reset right away.

In case you have a duplicate row already in DataFrame A, then concatenating and then dropping duplicate rows, will remove rows from DataFrame A that you might want to keep.

In this case, you will need to create a new column with a cumulative count, and then drop duplicates, it all depends on your use case, but this is common in time-series data

Here is an example:

df_1 = pd.DataFrame([{'date':'11/20/2015', 'id':4, 'value':24},{'date':'11/20/2015', 'id':4, 'value':24},{'date':'11/20/2015', 'id':6, 'value':34},])df_2 = pd.DataFrame([{'date':'11/20/2015', 'id':4, 'value':24},{'date':'11/20/2015', 'id':6, 'value':14},])df_1['count'] = df_1.groupby(['date','id','value']).cumcount()df_2['count'] = df_2.groupby(['date','id','value']).cumcount()df_tot = pd.concat([df_1,df_2], ignore_index=False)df_tot = df_tot.drop_duplicates()df_tot = df_tot.drop(['count'], axis=1)>>> df_totdate    id  value0   11/20/2015  4   241   11/20/2015  4   242   11/20/2015  6   341   11/20/2015  6   14

I'm surprised that pandas doesn't offer a native solution for this task.I don't think that it's efficient to just drop the duplicates if you work with large datasets (as Rian G suggested).

It is probably most efficient to use sets to find the non-overlapping indices. Then use list comprehension to translate from index to 'row location' (boolean), which you need to access rows using iloc[,]. Below you find a function that performs the task. If you don't choose a specific column (col) to check for duplicates, then indexes will be used, as you requested. If you chose a specific column, be aware that existing duplicate entries in 'a' will remain in the result.

import pandas as pddef append_non_duplicates(a, b, col=None):    if ((a is not None and type(a) is not pd.core.frame.DataFrame) or (b is not None and type(b) is not pd.core.frame.DataFrame)):        raise ValueError('a and b must be of type pandas.core.frame.DataFrame.')    if (a is None):        return(b)    if (b is None):        return(a)    if(col is not None):        aind = a.iloc[:,col].values        bind = b.iloc[:,col].values    else:        aind = a.index.values        bind = b.index.values    take_rows = list(set(bind)-set(aind))    take_rows = [i in take_rows for i in bind]    return(a.append( b.iloc[take_rows,:] ))# Usagea = pd.DataFrame([[1,2,3],[1,5,6],[1,12,13]], index=[1000,2000,5000])b = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]], index=[1000,2000,3000])append_non_duplicates(a,b)#        0   1   2# 1000   1   2   3    <- from a# 2000   1   5   6    <- from a# 5000   1  12  13    <- from a# 3000   7   8   9    <- from bappend_non_duplicates(a,b,0)#       0   1   2# 1000  1   2   3    <- from a# 2000  1   5   6    <- from a# 5000  1  12  13    <- from a# 2000  4   5   6    <- from b# 3000  7   8   9    <- from b