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This error occurs when you try to join, merge, or concatenate two or more DataFrames or Series that have overlapping values in their indexes. For example, if you have two DataFrames with partially overlapping column names, and you try to join them using the join() method, you will get this error:

import pandas as pd

df1 = pd.DataFrame({'Col1': ['A0', 'A1', 'A2', 'A3', 'A4'],
                    'Col2': ['B0', 'B1', 'B2', None, None],
                    'Col3': ['C0', 'C1', None, None, None]})

df2 = pd.DataFrame({'Col1': [None, None, None, None, None],
                    'Col2': [None, None, None, 'B3', 'B4'],
                    'Col3': [None, None, 'C2', 'C3', 'C4'],
                    'Col4': ['D2', 'D3', 'D4', 'D5', 'D6']})

df1.join(df2)
        

The reason for this error is that pandas does not know how to handle the duplicate column names in the two DataFrames. It cannot simply overwrite or ignore the values in one DataFrame with the values in the other DataFrame because that would result in data loss or inconsistency. Therefore, pandas raises an exception to alert the user of this issue.

There are several ways to resolve this error, depending on your specific use case and desired output. Here are some common solutions:

Solution 1: Rename the overlapping columns

One simple way to avoid the error is to rename the columns that have overlapping names in one or both of the DataFrames. You can use the rename() method to change the column names by passing a dictionary that maps the old names to the new names. For example:

df2 = df2.rename(columns={'Col1': 'Col5',
                          'Col2': 'Col6',
                          'Col3': 'Col7'})

df1.join(df2)
        

Solution 2: Use suffixes to distinguish the overlapping columns

Another way to avoid the error is to use suffixes to distinguish the columns that have overlapping names in the two DataFrames. You can use the suffixes parameter of the join() method to specify a string or a tuple of strings that will be appended to the column names of each DataFrame. For example:

df1.join(df2, suffixes=('_x', '_y'))
        

Solution 3: Use combine_first() to fill missing values

Another way to avoid the error is to use the combine_first() method to fill the missing values in one DataFrame with the values from the other DataFrame. This method takes another DataFrame as an argument and returns a new DataFrame that has the combined values of both DataFrames. For example:

df1.combine_first(df2)
        

These solutions work well for different scenarios and help you avoid the error caused by overlapping indexes or column names in your DataFrames.