Remove duplicates from python dataframe list

Question

I have a pandas df where each row is a list of words. The list has duplicate words. I want to remove duplicate words.

I tried using dict.fromkeys(listname) in a for loop to iterate over each row in the df. But this splits the words into alphabets

filepath = "C:/abc5/Python/Clustering/output2.csv"
df = pd.read_csv(filepath,encoding='windows-1252')

df["newlist"] = df["text_lemmatized"]
for i in range(0,len(df)):
    l = df["text_lemmatized"][i]
    df["newlist"][i] = list(dict.fromkeys(l))

print(df)

Expected result is ==>

['clear', 'pending', 'order', 'pending', 'order']   ['clear', 'pending', 'order']
 ['pending', 'activation', 'clear', 'pending']   ['pending', 'activation', 'clear']

Actual result is

['clear', 'pending', 'order', 'pending', 'order']  ...   [[, ', c, l, e, a, r, ,,  , p, n, d, i, g, o, ]]
['pending', 'activation', 'clear', 'pending', ...  ...  [[, ', p, e, n, d, i, g, ,,  , a, c, t, v, o, ...

Answer 1

Use set to remove duplicates.

Also you don't need the for loop

  df["newlist"] = list(set( df["text_lemmatized"] ))

Answer 2

Just use series.map and np.unique

Your sample data:

Out[43]:
                           text_lemmatized
0  [clear, pending, order, pending, order]
1    [pending, activation, clear, pending]

df.text_lemmatized.map(np.unique)

Out[44]:
    0         [clear, order, pending]
    1    [activation, clear, pending]
    Name: val, dtype: object

If you prefer it isn't sorted, use pd.unique

df.text_lemmatized.map(pd.unique)

Out[51]:
0         [clear, pending, order]
1    [pending, activation, clear]
Name: text_lemmatized, dtype: object

Answer 3

df.drop_duplicates(subset ="text_lemmatized", 
                     keep = First, inplace = True) 

keep = First, means keep the first occurrence

Answer 4

Problem is there are not lists, but strings, so is necessary convert each value to list by ast.literal_eval , then is possible convert values to set s for remove duplicates:

import ast

df['newlist'] = df['text_lemmatized'].map(lambda x: list(set(ast.literal_eval(x))))
print(df)
                           text_lemmatized                       newlist
0  [clear, pending, order, pending, order]       [clear, pending, order]
1    [pending, activation, clear, pending]  [clear, activation, pending]

Or use dict.fromkeys :

f = lambda x: list(dict.fromkeys(ast.literal_eval(x)))
df['newlist'] = df['text_lemmatized'].map(f)

Another idea is convert column text_lemmatized to lists in one step and then remove duplicates in another step, advantage is lists in column text_lemmatized for next processing:

df['text_lemmatized'] = df['text_lemmatized'].map(ast.literal_eval)
df['newlist'] = df['text_lemmatized'].map(lambda x: list(set(x)))

EDIT:

After some discussion solution is:

df['newlist'] = df['text_lemmatized'].map(lambda x: list(set(x)))

Answer 5

Your code for removing duplicates seems fine. I tried following and it worked well. Guess the problem is the way you are appending the list in the dataframe column.

`list_from_df = [['clear', 'pending', 'order', 'pending', 'order'],
            ['pending', 'activation', 'clear', 'pending']] 

list_with_unique_words = []

for x in list_from_df:

    unique_words = list(dict.fromkeys(x))
    list_with_unique_words.append(unique_words)

print(list_with_unique_words)

output [['clear', 'pending', 'order'], ['pending', 'activation', 'clear']]

df["newlist"] = list_with_unique_words

df

`

Answer 6

Solution is ==>

import pandas as pd
filepath = "C:/abc5/Python/Clustering/output2.csv"
df = pd.read_csv(filepath,encoding='windows-1252')
df["newlist"] = df["text_lemmatized"]
df['newlist'] = df['text_lemmatized'].map(lambda x: list(set(x)))
print(df)

Thanks to jezrael and all others who helped narrow down to this solution