How to get "aggregate" word count from pandas Series elements

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I'm currently using python3.7 in a Jupyter Notebook (v5.6.0) with pandas 0.23.4.

I've written code to tokenize some Japanese words and have successfully applied a word count function that returns the word counts from each row in a pandas Series like so:

0       [(かげ, 20), (モリア, 17), (たち, 15), (お前, 14), (おれ,...
1       [(お前, 11), (ゾロ, 10), (うっ, 10), (たち, 9), (サンジ, ...
2       [(おれ, 11), (男, 6), (てめえ, 6), (お前, 5), (首, 5), ...
3       [(おれ, 19), (たち, 14), (ヨホホホ, 12), (お前, 10), (みん...
4       [(ラブーン, 32), (たち, 14), (おれ, 12), (お前, 12), (船長...
5       [(ヨホホホ, 19), (おれ, 13), (ラブーン, 12), (船長, 11), (...
6       [(わたし, 20), (おれ, 16), (海賊, 9), (お前, 9), (もう, 9...
7       [(たち, 21), (あたし, 15), (宝石, 14), (おれ, 12), (ハッ,...
8       [(おれ, 13), (あれ, 9), (もう, 7), (ヨホホホ, 7), (見え, 7...
9       [(ケイミー, 23), (人魚, 20), (はっち, 14), (おれ, 13), (め...
10      [(ケイミー, 18), (おれ, 17), (め, 14), (たち, 12), (はっち...

From this previously asked question:

Creating a dictionary of word count of multiple text files in a directory

I thought I could use the answer to help with my objective.

I want to consolidate all the above pairs in each row into a dictionary where the key is the Japanese text, and the value is the sum of all the instances of the text appearing within the data set. I thought I could accomplish this with the collections.Counter module by turning each row in the series into a dictionary, like this:

vocab_list = []
for i in range(len(wordcount)):
    vocab_list.append(dict(wordcount[i]))

Which gives me the dictionary format that I want, where each row in the Series is now a dictionary, like so:

[{'かげ': 20,
 'モリア': 17,
 'たち': 15,
 'お前': 14,
 'おれ': 11,
 'もう': 9,
 '船長': 7,
 'っ': 7,
 '七武海': 7,
 '言っ': 6, ...

My problem comes when I try to use the sum() function and Counter() to aggregate the totals:

vocab_list = sum(vocab_list, Counter())
print(vocab_list)

Instead of getting the expected "aggregated dictionary", I receive the following error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-37-3c66e97f4559> in <module>()
      3     vocab_list.append(dict(wordcount[i]))
      4 
----> 5 vocab_list = sum(vocab_list, Counter())
      6 vocab_list

TypeError: unsupported operand type(s) for +: 'Counter' and 'dict'

Could you explain what exactly is wrong in the code and how to fix it?

typeerror data-science pandas word-count python
2 Answers

If the elements in your series are of type Counter you can simply aggregate by sum

df.agg(sum)

Example:

from collections import Counter

df = pd.Series([[('かげ', 20), ('男', 17), ('たち', 15), ('お前', 14)],[('お前', 11), ('ゾロ', 10), ('うっ', 10), ('たち', 9)],[('おれ', 11), ('男', 6), ('てめえ', 6), ('お前', 5), ('首', 5)]])   
df = df.apply(lambda x: Counter({y[0]:y[1] for y in x}))

df
# Out:
# 0          {'かげ': 20, '男': 17, 'たち': 15, 'お前': 14}
# 1          {'お前': 11, 'ゾロ': 10, 'うっ': 10, 'たち': 9}
# 2    {'おれ': 11, '男': 6, 'てめえ': 6, 'お前': 5, '首': 5}
# dtype: object

df.agg(sum)
# Out:
# Counter({'うっ': 10,
#          'おれ': 11,
#          'お前': 30,
#          'かげ': 20,
#          'たち': 24,
#          'てめえ': 6,
#          'ゾロ': 10,
#          '男': 23,
#          '首': 5})
user_23147370

I am unframiliar with Counter(), but I think it may have to do with the fact that your dictionary is inside a list.

Also, you could do this quite easliy without the use of additional tool (like counter). Here is something I threw together that 'works' but may not be performat in your use case: 

vocab_list = [{'かげ': 20,
            'モリア': 17,
            'たち': 15,
            'お前': 14,
            'おれ': 11,
            'もう': 9,
            '船長': 7}]

numberz = list(vocab_list[0].values())
totalz = 0
for x in numberz:
    totalz += x

print(totalz)

Out [29]: 93
user_84848430
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