CODEWITHSUNDEEP
pythonpandasdataframeduplicates
Jake
Jake

Reputation: 474

Removing duplicate dataframes in a list

I have a list in python that contains duplicate dataframes. The goal is to remove these duplicate dataframes in whole. Here is some code:

import pandas as pd
import numpy as np
##Creating Dataframes
data1_1 =[[1,2018,80], [2,2018,70]]

data1_2 =  [[1,2017,77], [3,2017,62]]


df1 = pd.DataFrame(data1_1, columns = ['ID', 'Year', 'Score'])
df2 = pd.DataFrame(data1_2, columns = ['ID', 'Year', 'Score'])


###Creating list with duplicates
all_df_list = [df1,df1,df1,df2,df2,df2]

The desired result is this:

###Desired results
desired_list = [df1,df2]

Is there a way to remove any duplicated dataframes within a python list?

Thank you

Upvotes: 3

Views: 1934

Answers (6)

Eric O.
Eric O.

Reputation: 583

I find this easier to read and understand and debug.

DISCLAIMER: If you are planning to work with large lists you need to consider a different solution.

def remove_duplicate_dataframes(dfs: list) -> list:
    if len(dfs) < 2:
        return dfs

    unique_dfs = []
    for idx, df in enumerate(dfs):
        if len(unique_dfs) == 0:
            unique_dfs.append(df)
            continue

        dfs_copy = deepcopy(dfs)
        dfs_copy.pop(idx)
        if any([df_.equals(df) for df_ in dfs_copy]):
            continue
        else:
            unique_dfs.append(df)

    return unique_dfs

Upvotes: 0

amrrs
amrrs

Reputation: 6325

There's a new Python library pyoccur to do this easily.

from pyoccur import pyoccur
pyoccur.remove_dup(all_df_list)

Output:

 0   1  2018     80
 1   2  2018     70,    ID  Year  Score
 0   1  2017     77
 1   3  2017     62]

Upvotes: 1

Andy L.
Andy L.

Reputation: 25239

You just need to pass the list of duplicate df's to pd.Series and drop duplicate and convert it back to list

In [229]: desired_list = pd.Series(all_df_list).drop_duplicates().tolist()

In [230]: desired_list
Out[230]:
[   ID  Year  Score
 0   1  2018     80
 1   2  2018     70,    ID  Year  Score
 0   1  2017     77
 1   3  2017     62]

The final desired_list hold 2 dataframe equal to df1, df2

In [231]: desired_list[0] == df1
Out[231]:
     ID  Year  Score
0  True  True   True
1  True  True   True

In [232]: desired_list[1] == df2
Out[232]:
     ID  Year  Score
0  True  True   True
1  True  True   True

Upvotes: 0

Erfan
Erfan

Reputation: 42916

We can use pandas DataFrame.equals with list comprehension in combination with enumerate to compare the items in the list between each other:

desired_list = [all_df_list[x] for x, _ in enumerate(all_df_list) if all_df_list[x].equals(all_df_list[x-1]) is False]

print(desired_list)
[   ID  Year  Score
0   1  2018     80
1   2  2018     70,    ID  Year  Score
0   1  2017     77
1   3  2017     62]

DataFrame.equals returns True if the compared dataframes are equal:

df1.equals(df1)
True

df1.equals(df2)
False

Note As Wen-Ben noted in the comments. Your list should be sorted like [df1, df1, df1, df2, df2, df2]. Or with more df's: [df1, df1, df2, df2, df3, df3]

Upvotes: 1

BENY
BENY

Reputation: 323236

I am doing with numpy.unique 

_,idx=np.unique(np.array([x.values for x in all_df_list]),axis=0,return_index=True)
desired_list=[all_df_list[x] for  x in idx ]
desired_list
Out[829]: 
[   ID  Year  Score
 0   1  2017     77
 1   3  2017     62,    ID  Year  Score
 0   1  2018     80
 1   2  2018     70]

Upvotes: 3

Engineero
Engineero

Reputation: 12918

My first thought was to use a set, but dataframes are mutable and thus not hashable. Do you still need individual dataframes in your list, or is it useful to merge all of these into a single dataframe with all unique values?

You can pd.merge() them all into a single dataframe with unique values using reduce from functools:

from functools import reduce
reduced_df = reduce(lambda left, right: pd.merge(left, right, on=None, how='outer'),
                    all_df_list)
print(reduced_df)
#    ID  Year  Score
# 0   1  2018     80
# 1   2  2018     70
# 2   1  2017     77
# 3   3  2017     62

Upvotes: 1

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