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I have a column in my dataframe that is a list with values from rows. Is there any way to get the same columns with unique values in these lists sorted also.

This is my dataframe column.

ListProds
['YIZ12FF-A', 'YIZ12FF-A', 'YIIE2FF-A', 'YIR72FF-A', 'YIR72FF-A', 'YIR72FF-A']
['HYY32ZY-A', 'HYY32ZY-A']
['YI742FF-A', 'YI742FF-A', 'YI742FF-A', 'YI742FF-A']
['YI762FF-A', 'YI762FF-A', 'YI762FF-A', 'YI762FF-A', 'YI762FF-A', 'YI762FF-A', 'YI6E2FF-A', 'YI6E2FF-A', 'YI6E2FF-A']
['YI762FF-A', 'YI762FF-A', 'YI762FF-A', 'YI742FF-A', 'YI742FF-A', 'YI742FF-A', 'YI772FF-A', 'YI772FF-A', 'YI772FF-A']
['YIIE2FF-A']
['YIZ12FF-A', 'YIZ12FF-A', 'YIZ12FF-A', 'YIZ12FF-A', 'YIRE2FF-A', 'YIRE2FF-A', 'YIRE2FF-A', 'YIRE2FF-A', 'YIIK2FF-A', 'YIIK2FF-A', 'YIZ32FF-A', 'YZ3F2FF-A']
['YIY32FF-A']
['YNF82FF-A']
['YTFL2FF-A', 'YTFL2FF-A', 'YTHR2FF-A', 'YU0Y2FF-A']
['YI6A2FF-A', 'YI6A2FF-A', 'YI6A2FF-A']
['YI772FF-A']
['YTZR2FF-A']
['YIRF2FF-A', 'YIRF2FF-A', 'YIRF2FF-A', 'YIRF2FF-A']
['YI752FF-A', 'YI752FF-A', 'YI752FF-A']

I tried a few methods used in Python without success.

What is the pandas method for removing duplicated values from list into a dataframe?

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  • ListProds.apply(np.unique) Commented Nov 5, 2019 at 12:51

1 Answer 1

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3

Convert values to sets and then sort them:

df['ListProds'] = df['ListProds'].apply(lambda x: sorted(set(x)))

Or like mentioned @Chris A in comments use np.unique:

df['ListProds'] = df['ListProds'].apply(lambda x: np.sort(np.unique(x)))
#if lists are sorted
#df['ListProds'] = df['ListProds'].apply(np.unique)

print (df)
                                            ListProds
0                   [YIIE2FF-A, YIR72FF-A, YIZ12FF-A]
1                                         [HYY32ZY-A]
2                                         [YI742FF-A]
3                              [YI6E2FF-A, YI762FF-A]
4                   [YI742FF-A, YI762FF-A, YI772FF-A]
5                                         [YIIE2FF-A]
6   [YIIK2FF-A, YIRE2FF-A, YIZ12FF-A, YIZ32FF-A, Y...
7                                         [YIY32FF-A]
8                                         [YNF82FF-A]
9                   [YTFL2FF-A, YTHR2FF-A, YU0Y2FF-A]
10                                        [YI6A2FF-A]
11                                        [YI772FF-A]
12                                        [YTZR2FF-A]
13                                        [YIRF2FF-A]
14                                        [YI752FF-A]
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