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 ##C

Solution 1:

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
 0120177713201762,    ID  Year  Score
 0120188012201870]

Solution 2:

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] forx, _inenumerate(all_df_list)ifall_df_list[x].equals(all_df_list[x-1])isFalse]

print(desired_list)
[   IDYearScore012018     80122018     70,    IDYearScore012017     77132017     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]

Solution 3:

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

Solution 4:

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

from pyoccur import pyoccur
pyoccur.remove_dup(all_df_list)

Output:

012018     80122018     70,IDYearScore012017     77132017     62]

Solution 5:

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
 0120188012201870,    ID  Year  Score
 0120177713201762]

The final desired_list hold 2 dataframe equal to df1, df2

In [231]: desired_list[0] == df1
Out[231]:
     ID  Year  Score
0TrueTrueTrue1TrueTrueTrueIn [232]: desired_list[1] == df2
Out[232]:
     ID  Year  Score
0TrueTrueTrue1TrueTrueTrue