Python Pandas Only Compare Identically Labeled DataFrame Objects

Question

I tried all the solutions here:
Pandas "Can only compare identically-labeled DataFrame objects" error

Didn’t work for me. Here’s what I’ve got. I have two data frames. One is a set of financial data that already exists in the system and another set that has some that may or may not exist in the system. I need to find the difference and add the stuff that doesn’t exist.

Here is the code:

import pandas as pd
import numpy as np
from azure.storage.blob import AppendBlobService, PublicAccess, ContentSettings
from io import StringIO

dataUrl = "http://ichart.finance.yahoo.com/table.csv?s=MSFT"
blobUrlBase = "https://pyjobs.blob.core.windows.net/"
data = pd.read_csv(dataUrl)

abs = AppendBlobService(account_name='pyjobs', account_key='***')
abs.create_container("stocks", public_access = PublicAccess.Container)
abs.append_blob_from_text('stocks', 'msft', data[:25].to_csv(index=False))
existing = pd.read_csv(StringIO(abs.get_blob_to_text('stocks', 'msft').content))

ne = (data != existing).any(1)

the failing code is the final line. I was going through an article on determining differences between data frames.

I checked the dtypes on all columns, they appear to be the same. I also did a side by side output, I sorted teh axis, the indices, dropped the indices etc. Still get that bloody error.

Here is the output of the first row of existing and data

>>> existing[:1]
         Date       Open   High    Low  Close    Volume  Adj Close
0  2016-05-27  51.919998  52.32  51.77  52.32  17653700      52.32
>>> data[:1]
         Date       Open   High    Low  Close    Volume  Adj Close
0  2016-05-27  51.919998  52.32  51.77  52.32  17653700      52.32

Here is the exact error I receive:

>>> ne = (data != existing).any(1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:Anaconda3libsite-packagespandascoreops.py", line 1169, in f
    return self._compare_frame(other, func, str_rep)
  File "C:Anaconda3libsite-packagespandascoreframe.py", line 3571, in _compare_frame
    raise ValueError('Can only compare identically-labeled '
ValueError: Can only compare identically-labeled DataFrame objects

Asked By: David Crook

||

Source

Answer 1

In order to get around this, you want to compare the underlying numpy arrays.

import pandas as pd

df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'], index=['One', 'Two'])
df2 = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], index=['one', 'two'])


df1.values == df2.values

array([[ True,  True],
       [ True,  True]], dtype=bool)

Answered By: piRSquared

Answer 2

Replicated with some fake data to achieve the end goal of removing duplicates. Note this is not the answer to the original question, but what the answer was to what I was attempting to do that caused the question.

b = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']},
                    index=[4, 5, 6, 7])


c = pd.DataFrame({'A': ['A7', 'A8', 'A9', 'A10', 'A11'],
                  'A': ['A7', 'A8', 'A9', 'A10', 'A11'],
                  'B': ['B7', 'B8', 'B9', 'B10', 'B11'],
                  'C': ['C7', 'C8', 'C9', 'C10', 'C11'],
                  'D': ['D7', 'D8', 'D9', 'D10', 'D11']},
                   index=[7, 8, 9, 10, 11])

result = pd.concat([b,c])
idx = np.unique(result["A"], return_index=True)[1]
result.iloc[idx].sort()

Answered By: David Crook

Answer 3

If you want to compare 2 Data Frames. Check-out flexible comparison in Pandas, using the methods like .eq(), .nq(), gt() and more… –> equal, not equal and greater then.

Example:

df['new_col'] = df.gt(df_1)

http://pandas.pydata.org/pandas-docs/stable/basics.html#flexible-comparisons

Answered By: Melroy van den Berg

Answer 4

I also faced the same issue and resolved it by sorting the index in both axis, before comparing two dataframes.

df1 = df1.sort_index(axis=1)
df2 = df2.sort_index(axis=1)
df1 = df1.sort_index()
df2 = df2.sort_index()

Answered By: SOWNDARIYA M

Python Pandas Only Compare Identically Labeled DataFrame Objects

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