CODEWITHSUNDEEP
python-3.xpandaslistdictionary
user13973446
user13973446

Reputation:

Pandas Series values contains list, how to count unique values and return it as a dictionary

So I have a Pandas Series that looks as such

df['LABEL']

0 [0.0]
1 [0.0]
2 [1.0]
3 [3.0]
4 [3.0]
5 [3.0]

I want to count the number of unique values of this Pandas Series, and then return it as a dictionary

However when I use value_count() it returns an error

Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'
Traceback (most recent call last):
  File "pandas\_libs\hashtable_class_helper.pxi", line 1652, in pandas._libs.hashtable.PyObjectHashTable.map_locations
TypeError: unhashable type: 'list'

However after finding the unique values I wish to see it as a dictionary

Expected output would be

{ 0.0 : 2, 1.0: 1, 3.0 : 3}

Upvotes: 2

Views: 468

Answers (2)

DYZ
DYZ

Reputation: 57105

Assuming all your lists are single-element lists, the following solves your problem:

df['LABEL'].str[0].value_counts().to_dict()
#{3: 3, 0: 2, 1: 1}

If a list has more than one element and you want to count all of them, let the Counter help:

from collections import Counter
#Replaced the last [3] with [3, 3, 3]
dict(Counter(df['LABEL'].sum()))
#{0: 2, 1: 1, 3: 5}

The latter approach is about 10 times faster than the first one and works for the lists of any length.

Upvotes: 2

bigbounty
bigbounty

Reputation: 17408

In [20]: df
Out[20]:
   LABLE
0  [0.0]
1  [0.0]
2  [1.0]
3  [3.0]
4  [3.0]
5  [3.0]

In [19]: df["LABLE"].apply(lambda x: x[0]).value_counts().to_dict()
Out[19]: {3.0: 3, 0.0: 2, 1.0: 1}

Upvotes: 0

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