Duplicated column names!

In my case the problem was because I had duplicated column names.

You can mitigate this error without having to change your data or remove duplicates. Just create a new index with DataFrame.reset_index:

df = df.reset_index()

The old index is kept as a column in your dataframe, but if you don't need it you can do:

df = df.reset_index(drop=True)

Some prefer:

df.reset_index(inplace=True, drop=True)

This post comes up top when you search for the error but the answers are not complete, so let me add mine. There is another reason this error can happen: If you have duplicate columns in your data frames, you will not be able to concatenate and raise this. In fact, even in the original question there are two columns called timestamp. So it will be better to check if len(df.columns) == len(set(df.columns)) for all the data frames you are trying to concatenate.

As a complement of Nicholas Morley's answer, when you find even this not works:

df = df.reset_index(drop=True)

You should check whether the columns are unique. When they are not, even reseting index not works. Duplicated columns should be removed first to make it works.

This is because you have duplicated columns. Before concatenating drop duplicated columns in each DataFrame as follows:

df = df.loc[:,~df.columns.duplicated()].reset_index(drop=True)

This happens also when you have duplicates in the columns names.

The indexes of your two dataframes don't match.

When pandas is performing concat operation horizontally (axis=1) it tries to find rows with same indexes and join them horizontally. So row with index 1 from df1 will be matched with row with index 1. Therefore reset index in either one of them or both if both have troubled indexes.

pd.concat([df1.reset_index(drop=True), df2], axis=1)

or

pd.concat([df1.reset_index(drop=True), df2.reset_index(drop=True), axis=1])

Same Indices Between the Two DFs

Another reason for this issue might be that df1 and df2 might have the same indices, between each other. For example, both the dfs might have the same index idx1.

To check if this is the issue, you can see if the following outputs not an empty list:

print([org_name for org_name in cum_df.index if org_name in df_from_2002.index])

My suggested solution then would be to rename the indices (so df1 would keep having idx1 and you would change idx1 to idx2 in df2) and after concatenating (df1 = pd.concat([df1, df2])), combine the two indices (in case you need to get the sum of them) with this code:

df1.iloc[idx1] = df1.iloc[[idx1, idx2]].sum()

and then remove idx2:

df1.drop([idx2], inplace=True)

This happened to me when I was trying to concat two dataframes that have duplicated column names!

Let's say that I want to remove the first duplicated column:

duplicated_column = 'column'

df_tmp = df[duplicated_column].T
df_tmp = df_tmp.iloc[1: , :]

df = df.drop([duplicated_column], axis=1)
df = pd.concat([df, df_tmp.T], axis=1)

The problem for me was duplicate column labels, just as many others here mentioned it. To keep only the first column for duplicates I used below:

df=df.T[~df.T.index.duplicated(keep='first')].T

Answers here helped but concat worked fine for me in some cases even where duplicate columns were present. However, in some cases it didn't work and raised the InvalidIndexError.

It turned out that it works fine if order of duplicate columns is same but raises an error if order of duplicate columns is different.

Example where it works fine:

df = pd.DataFrame({'a': [1, 2, 3], 'b': [5, 6, 7], 'c': [9, 10, 11]})
df1 = pd.DataFrame({'a': [12], 'b': [13], 'c': [14]})
df.rename(columns={
    'c': 'b'
}, inplace=True)
df1.rename(columns={
    'c': 'b'
}, inplace=True)
print(pd.concat([df, df1]))

Output:
    a   b   b
0   1   5   9
1   2   6  10
2   3   7  11
0  12  13  14

Example where it doesn't work:

df = pd.DataFrame({'b': [1, 2, 3], 'a': [5, 6, 7], 'c': [9, 10, 11]})
df1 = pd.DataFrame({'a': [12], 'b': [13], 'c': [14]})
df.rename(columns={
    'c': 'b'
}, inplace=True)
df1.rename(columns={
    'c': 'b'
}, inplace=True)
print(pd.concat([df, df1]))

Output:
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely 
valued Index objects

below solution would work if you are concat is using axis=0, which means you want to append rows not columns

reason : one or both of your dataframes might have duplicate columns df1 columns could be A, B, C , C df2 columns could be B, D

in this case A has duplicate column C, and for this reason you might get this error. Drop one of the C column in df1 and hopefully issue will be resolved

df1['C'].is_unique

Happened the same with me, but then I noticed that my index (datetime) has different last dates. When I fixed the date using the same timeframe interval for both dataframes the pd.concat() worked fine.

df1 = pd.DataFrame.from_dict({'A':[1,2,3], 'B':[110,120,230]})
df2 = pd.DataFrame.from_dict({'B':[11,12,23], 'C':[10,11,12]})
df2.rename(columns={'C':'B'}, inplace=True)
pd.concat([df1,df2])

InvalidIndexError: Reindexing only valid with uniquely valued Index objects

The above code will replicate the error when concatenating rows of dataframe. The reason the error occurs is because, there are two columns in df2 with the same name 'B'

    A   B
0   1   110
1   2   120
2   3   230

    B   B
0   11  10
1   12  11
2   23  12

In my case, one of the pandas DataFrames had multiple columns with the same name

best solution from this page: https://pandas.pydata.org/pandas-docs/version/0.20/merging.html

df = pd.concat([df1, df2], axis=1, join_axes=[df1.index])