Python error "TypeError: Cannot compare types 'ndarray(dtype=int64)' and 'str'"

0 votes

I'm trying to execute the following python code:

import pandas as pd                                                                        

csv_file = 'sample.csv'                                                                    
count = 1                                                                                  
my_filtered_csv = pd.read_csv(csv_file, usecols=['subDirectory_filePath', 'expression'])   

emotion_map = { '0':'6', '1':'3', '2':'4', '3':'5', '4':'2', '5':'1', '6':'0'}             

my_filtered_csv['expression'] = my_filtered_csv['expression'].replace(emotion_map)         


I end up with the following error log:

Traceback (most recent call last):
  File "/Users/mona/CS585/project/affnet/", line 11, in <module>
    my_filtered_csv['expression'] = my_filtered_csv['expression'].replace(emotion_map)
  File "/Users/mona/anaconda/lib/python3.6/site-packages/pandas/core/", line 3836, in replace
    limit=limit, regex=regex)
  File "/Users/mona/anaconda/lib/python3.6/site-packages/pandas/core/", line 3885, in replace
  File "/Users/mona/anaconda/lib/python3.6/site-packages/pandas/core/", line 3259, in replace_list
    masks = [comp(s) for i, s in enumerate(src_list)]
  File "/Users/mona/anaconda/lib/python3.6/site-packages/pandas/core/", line 3259, in <listcomp>
    masks = [comp(s) for i, s in enumerate(src_list)]
  File "/Users/mona/anaconda/lib/python3.6/site-packages/pandas/core/", line 3247, in comp
    return _maybe_compare(values, getattr(s, 'asm8', s), operator.eq)
  File "/Users/mona/anaconda/lib/python3.6/site-packages/pandas/core/", line 4619, in _maybe_compare
    raise TypeError("Cannot compare types %r and %r" % tuple(type_names))
TypeError: Cannot compare types 'ndarray(dtype=int64)' and 'str'

Process finished with exit code 1

May 30 in Python by Ashish

1 answer to this question.

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0 votes

Hey @Ashish, change the emotion_map to the following:

​emotion_map = { 0:6, 1:3, 2:4, 3:5, 4:2, 5:1, 6:0}

It helped me

answered May 30 by Mir

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