ENH: Improved error message (issue 13084) #13187
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As per issue 13084, made it clearer for the user to understand why a concat failed. No additional code was needed. This is because the _verify_integrity method inside the BlockManager class of pandas/core/internals.py already checked that the length of the concat data and index are the same. An alternative option was to pass the error in the e argument of the construction_error method of the SingleBlockManager. The alternative was not performed as there appeared to be no other reason why the shape of the dataframes would be different, as a result using the e argument would result in duplicating code. The method was also not extended to identify the dataframes with duplicate index values to avoid overcomplicating the code. The end user should be able to easily identify the dataframe/s causing the errors
| raise ValueError("Shape of passed values is {0}, indices imply {1}".format( | ||
| passed, implied)) | ||
| raise ValueError("Shape of passed values is {0}, indices imply {1}.\n\ | ||
| This means that at least one of the dataframes contains duplicate \ |
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this is not true at all, this error could be called for quite a few reasons. pls put the error more specifially (e.g. in merge.py) and test if there are duplicates.
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if you'd like to update, pls reopen the issue. |
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git diff upstream/master | flake8 --diffAs per issue 13084, made it clearer for the user to understand why a concat failed. No additional code was needed. This is because the _verify_integrity method inside the BlockManager class of pandas/core/internals.py already checked that the length of the concat data and index are the same. An alternative option was to pass the error in the e argument of the construction_error method of the SingleBlockManager. The alternative was not performed as there appeared to be no other reason why the shape of the dataframes would be different, as a result using the e argument would result in duplicating code. The method was also not extended to identify the dataframes with duplicate index values to avoid overcomplicating the code. The end user should be able to easily identify the dataframe/s causing the errors