Pandas: Drop consecutive duplicates

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What’s the most efficient way to drop only consecutive duplicates in pandas?

drop_duplicates gives this:

In [3]: a = pandas.Series([1,2,2,3,2], index=[1,2,3,4,5])

In [4]: a.drop_duplicates()
Out[4]: 
1    1
2    2
4    3
dtype: int64

But I want this:

In [4]: a.something()
Out[4]: 
1    1
2    2
4    3
5    2
dtype: int64

Use shift:

a.loc[a.shift(-1) != a]

Out[3]:

1    1
3    2
4    3
5    2
dtype: int64

So the above uses boolean critieria, we compare the dataframe against the dataframe shifted by -1 rows to create the mask

Another method is to use diff:

In [82]:

a.loc[a.diff() != 0]
Out[82]:
1    1
2    2
4    3
5    2
dtype: int64

But this is slower than the original method if you have a large number of rows.

Update

Thanks to Bjarke Ebert for pointing out a subtle error, I should actually use shift(1) or just shift() as the default is a period of 1, this returns the first consecutive value:

In [87]:

a.loc[a.shift() != a]
Out[87]:
1    1
2    2
4    3
5    2
dtype: int64

Note the difference in index values, thanks @BjarkeEbert!

Here is an update that will make it work with multiple columns. Use “.any(axis=1)” to combine the results from each column:

cols = ["col1","col2","col3"]
de_dup = a[cols].loc[(a[cols].shift() != a[cols]).any(axis=1)]

Since we are going for most efficient way, i.e. performance, let’s use array data to leverage NumPy. We will slice one-off slices and compare, similar to shifting method discussed earlier in @EdChum's post. But with NumPy slicing we would end up with one-less array, so we need to concatenate with a True element at the start to select the first element and hence we would have an implementation like so –

def drop_consecutive_duplicates(a):
    ar = a.values
    return a[np.concatenate(([True],ar[:-1]!= ar[1:]))]

Sample run –

In [149]: a
Out[149]: 
1    1
2    2
3    2
4    3
5    2
dtype: int64

In [150]: drop_consecutive_duplicates(a)
Out[150]: 
1    1
2    2
4    3
5    2
dtype: int64

Timings on large arrays comparing @EdChum's solution

In [142]: a = pd.Series(np.random.randint(1,5,(1000000)))

In [143]: %timeit a.loc[a.shift() != a]
100 loops, best of 3: 12.1 ms per loop

In [144]: %timeit drop_consecutive_duplicates(a)
100 loops, best of 3: 11 ms per loop

In [145]: a = pd.Series(np.random.randint(1,5,(10000000)))

In [146]: %timeit a.loc[a.shift() != a]
10 loops, best of 3: 136 ms per loop

In [147]: %timeit drop_consecutive_duplicates(a)
10 loops, best of 3: 114 ms per loop

So, there’s some improvement!

Get major boost for values only!

If only the values are needed, we could get major boost by simply indexing into the array data, like so –

def drop_consecutive_duplicates(a):
    ar = a.values
    return ar[np.concatenate(([True],ar[:-1]!= ar[1:]))]

Sample run –

In [170]: a = pandas.Series([1,2,2,3,2], index=[1,2,3,4,5])

In [171]: drop_consecutive_duplicates(a)
Out[171]: array([1, 2, 3, 2])

Timings –

In [173]: a = pd.Series(np.random.randint(1,5,(10000000)))

In [174]: %timeit a.loc[a.shift() != a]
10 loops, best of 3: 137 ms per loop

In [175]: %timeit drop_consecutive_duplicates(a)
10 loops, best of 3: 61.3 ms per loop

Here is a function that handles both pd.Series and pd.Dataframes. You can mask/drop, choose the axis and finally choose to drop with ‘any’ or ‘all’ ‘NaN’. It is not optimized in term of computation time, but it has the advantage to be robust and pretty clear.

import numpy as np
import pandas as pd

# To mask/drop successive values in pandas
def Mask_Or_Drop_Successive_Identical_Values(df, drop=False, 
                                             keep_first=True,
                                             axis=0, how='all'):

    '''
    #Function built with the help of:
    # 1) https://stackoverflow.com/questions/48428173/how-to-change-consecutive-repeating-values-in-pandas-dataframe-series-to-nan-or
    # 2) https://stackoverflow.com/questions/19463985/pandas-drop-consecutive-duplicates
    
    Input:
    df should be a pandas.DataFrame of a a pandas.Series
    Output:
    df of ts with masked or dropped values
    '''
    
    # Mask keeping the first occurrence
    if keep_first:
        df = df.mask(df.shift(1) == df)
    # Mask including the first occurrence
    else:
        df = df.mask((df.shift(1) == df) | (df.shift(-1) == df))

    # Drop the values (e.g. rows are deleted)    
    if drop:
        return df.dropna(axis=axis, how=how)        
    # Only mask the values (e.g. become 'NaN')
    else:
        return df   

Here is a test code to include in the script:


if __name__ == "__main__":
    
    # With time series
    print("With time series:\n")
    ts = pd.Series([1,1,2,2,3,2,6,6,float('nan'), 6,6,float('nan'),float('nan')], 
                    index=[0,1,2,3,4,5,6,7,8,9,10,11,12])
    
    print("#Original ts:")    
    print(ts)

    print("\n## 1) Mask keeping the first occurrence:")    
    print(Mask_Or_Drop_Successive_Identical_Values(ts, drop=False, 
                                                   keep_first=True))

    print("\n## 2) Mask including the first occurrence:")    
    print(Mask_Or_Drop_Successive_Identical_Values(ts, drop=False, 
                                                   keep_first=False))
    
    print("\n## 3) Drop keeping the first occurrence:")    
    print(Mask_Or_Drop_Successive_Identical_Values(ts, drop=True, 
                                                   keep_first=True))
    
    print("\n## 4) Drop including the first occurrence:")        
    print(Mask_Or_Drop_Successive_Identical_Values(ts, drop=True, 
                                                   keep_first=False))
    
    
    # With dataframes
    print("With dataframe:\n")
    df = pd.DataFrame(np.random.randn(15, 3))
    df.iloc[4:9,0]=40
    df.iloc[8:15,1]=22
    df.iloc[8:12,2]=0.23
        
    print("#Original df:")
    print(df)

    print("\n## 5) Mask keeping the first occurrence:") 
    print(Mask_Or_Drop_Successive_Identical_Values(df, drop=False, 
                                                   keep_first=True))

    print("\n## 6) Mask including the first occurrence:")    
    print(Mask_Or_Drop_Successive_Identical_Values(df, drop=False, 
                                                   keep_first=False))
    
    print("\n## 7) Drop 'any' keeping the first occurrence:")    
    print(Mask_Or_Drop_Successive_Identical_Values(df, drop=True, 
                                                   keep_first=True,
                                                   how='any'))
    
    print("\n## 8) Drop 'all' keeping the first occurrence:")    
    print(Mask_Or_Drop_Successive_Identical_Values(df, drop=True, 
                                                   keep_first=True,
                                                   how='all'))
    
    print("\n## 9) Drop 'any' including the first occurrence:")        
    print(Mask_Or_Drop_Successive_Identical_Values(df, drop=True, 
                                                   keep_first=False,
                                                   how='any'))

    print("\n## 10) Drop 'all' including the first occurrence:")        
    print(Mask_Or_Drop_Successive_Identical_Values(df, drop=True, 
                                                   keep_first=False,
                                                   how='all'))

And here is the expected result:

With time series:

#Original ts:
0     1.0
1     1.0
2     2.0
3     2.0
4     3.0
5     2.0
6     6.0
7     6.0
8     NaN
9     6.0
10    6.0
11    NaN
12    NaN
dtype: float64

## 1) Mask keeping the first occurrence:
0     1.0
1     NaN
2     2.0
3     NaN
4     3.0
5     2.0
6     6.0
7     NaN
8     NaN
9     6.0
10    NaN
11    NaN
12    NaN
dtype: float64

## 2) Mask including the first occurrence:
0     NaN
1     NaN
2     NaN
3     NaN
4     3.0
5     2.0
6     NaN
7     NaN
8     NaN
9     NaN
10    NaN
11    NaN
12    NaN
dtype: float64

## 3) Drop keeping the first occurrence:
0    1.0
2    2.0
4    3.0
5    2.0
6    6.0
9    6.0
dtype: float64

## 4) Drop including the first occurrence:
4    3.0
5    2.0
dtype: float64
With dataframe:

#Original df:
            0          1         2
0   -1.890137  -3.125224 -1.029065
1   -0.224712  -0.194742  1.891365
2    1.009388   0.589445  0.927405
3    0.212746  -0.392314 -0.781851
4   40.000000   1.889781 -1.394573
5   40.000000  -0.470958 -0.339213
6   40.000000   1.613524  0.271641
7   40.000000  -1.810958 -1.568372
8   40.000000  22.000000  0.230000
9   -0.296557  22.000000  0.230000
10  -0.921238  22.000000  0.230000
11  -0.170195  22.000000  0.230000
12   1.460457  22.000000 -0.295418
13   0.307825  22.000000 -0.759131
14   0.287392  22.000000  0.378315

## 5) Mask keeping the first occurrence:
            0          1         2
0   -1.890137  -3.125224 -1.029065
1   -0.224712  -0.194742  1.891365
2    1.009388   0.589445  0.927405
3    0.212746  -0.392314 -0.781851
4   40.000000   1.889781 -1.394573
5         NaN  -0.470958 -0.339213
6         NaN   1.613524  0.271641
7         NaN  -1.810958 -1.568372
8         NaN  22.000000  0.230000
9   -0.296557        NaN       NaN
10  -0.921238        NaN       NaN
11  -0.170195        NaN       NaN
12   1.460457        NaN -0.295418
13   0.307825        NaN -0.759131
14   0.287392        NaN  0.378315

## 6) Mask including the first occurrence:
           0         1         2
0  -1.890137 -3.125224 -1.029065
1  -0.224712 -0.194742  1.891365
2   1.009388  0.589445  0.927405
3   0.212746 -0.392314 -0.781851
4        NaN  1.889781 -1.394573
5        NaN -0.470958 -0.339213
6        NaN  1.613524  0.271641
7        NaN -1.810958 -1.568372
8        NaN       NaN       NaN
9  -0.296557       NaN       NaN
10 -0.921238       NaN       NaN
11 -0.170195       NaN       NaN
12  1.460457       NaN -0.295418
13  0.307825       NaN -0.759131
14  0.287392       NaN  0.378315

## 7) Drop 'any' keeping the first occurrence:
           0         1         2
0  -1.890137 -3.125224 -1.029065
1  -0.224712 -0.194742  1.891365
2   1.009388  0.589445  0.927405
3   0.212746 -0.392314 -0.781851
4  40.000000  1.889781 -1.394573

## 8) Drop 'all' keeping the first occurrence:
            0          1         2
0   -1.890137  -3.125224 -1.029065
1   -0.224712  -0.194742  1.891365
2    1.009388   0.589445  0.927405
3    0.212746  -0.392314 -0.781851
4   40.000000   1.889781 -1.394573
5         NaN  -0.470958 -0.339213
6         NaN   1.613524  0.271641
7         NaN  -1.810958 -1.568372
8         NaN  22.000000  0.230000
9   -0.296557        NaN       NaN
10  -0.921238        NaN       NaN
11  -0.170195        NaN       NaN
12   1.460457        NaN -0.295418
13   0.307825        NaN -0.759131
14   0.287392        NaN  0.378315

## 9) Drop 'any' including the first occurrence:
          0         1         2
0 -1.890137 -3.125224 -1.029065
1 -0.224712 -0.194742  1.891365
2  1.009388  0.589445  0.927405
3  0.212746 -0.392314 -0.781851

## 10) Drop 'all' including the first occurrence:
           0         1         2
0  -1.890137 -3.125224 -1.029065
1  -0.224712 -0.194742  1.891365
2   1.009388  0.589445  0.927405
3   0.212746 -0.392314 -0.781851
4        NaN  1.889781 -1.394573
5        NaN -0.470958 -0.339213
6        NaN  1.613524  0.271641
7        NaN -1.810958 -1.568372
9  -0.296557       NaN       NaN
10 -0.921238       NaN       NaN
11 -0.170195       NaN       NaN
12  1.460457       NaN -0.295418
13  0.307825       NaN -0.759131
14  0.287392       NaN  0.378315

For other Stack explorers, building off johnml1135’s answer above. This will remove the next duplicate from multiple columns but not drop all of the columns. When the dataframe is sorted it will keep the first row but drop the second row if the “cols” match, even if there are more columns with non-matching information.

cols = ["col1","col2","col3"]
df = df.loc[(df[cols].shift() != df[cols]).any(axis=1)]

Just another way of doing it:

a.loc[a.ne(a.shift())]

The method pandas.Series.ne is the not equal operator, so a.ne(a.shift()) is equivalent to a != a.shift(). Documentation here.

Here’s a variant of EdChum’s answer that treats consecutive NaNs as duplicates, too:

def remove_consecutive_duplicates_and_nans(s):
    # By default, `shift` uses NaN as a fill value, which breaks our
    # removal of consecutive NaNs. Hence we use a different sentinel
    # object instead.
    shifted = s.astype(object).shift(-1, fill_value=object())
    return s.loc[
        (shifted != s)
        & ~(shifted.isna() & s.isna())
    ]

Create new column.

df['match'] = df.col1.eq(df.col1.shift())

Then:

df = df[df['match']==False]


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