What does `ValueError: cannot reindex from a duplicate axis` mean?

This error usually rises when you join / assign to a column when the index has duplicate values. Since you are assigning to a row, I suspect that there is a duplicate value in affinity_matrix.columns, perhaps not shown in your question.

As others have said, you've probably got duplicate values in your original index. To find them do this:

df[df.index.duplicated()]

Indices with duplicate values often arise if you create a DataFrame by concatenating other DataFrames. IF you don't care about preserving the values of your index, and you want them to be unique values, when you concatenate the the data, set ignore_index=True.

Alternatively, to overwrite your current index with a new one, instead of using df.reindex(), set:

df.index = new_index

For people who are still struggling with this error, it can also happen if you accidentally create a duplicate column with the same name. Remove duplicate columns like so:

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

Simple Fix

Run this before grouping

df = df.reset_index()

Thanks to this github comment for the solution.

Simply skip the error using .values at the end.

affinity_matrix.loc['sums'] = affinity_matrix.sum(axis=0).values

I came across this error today when I wanted to add a new column like this

df_temp['REMARK_TYPE'] = df.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0)

I wanted to process the REMARK column of df_temp to return 1 or 0. However I typed wrong variable with df. And it returned error like this:

----> 1 df_temp['REMARK_TYPE'] = df.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0)

/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in __setitem__(self, key, value)
   2417         else:
   2418             # set column
-> 2419             self._set_item(key, value)
   2420 
   2421     def _setitem_slice(self, key, value):

/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in _set_item(self, key, value)
   2483 
   2484         self._ensure_valid_index(value)
-> 2485         value = self._sanitize_column(key, value)
   2486         NDFrame._set_item(self, key, value)
   2487 

/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in _sanitize_column(self, key, value, broadcast)
   2633 
   2634         if isinstance(value, Series):
-> 2635             value = reindexer(value)
   2636 
   2637         elif isinstance(value, DataFrame):

/usr/lib64/python2.7/site-packages/pandas/core/frame.pyc in reindexer(value)
   2625                     # duplicate axis
   2626                     if not value.index.is_unique:
-> 2627                         raise e
   2628 
   2629                     # other

ValueError: cannot reindex from a duplicate axis

As you can see it, the right code should be

df_temp['REMARK_TYPE'] = df_temp.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0)

Because df and df_temp have a different number of rows. So it returned ValueError: cannot reindex from a duplicate axis.

Hope you can understand it and my answer can help other people to debug their code.

In my case, this error popped up not because of duplicate values, but because I attempted to join a shorter Series to a Dataframe: both had the same index, but the Series had fewer rows (missing the top few). The following worked for my purposes:

df.head()
                          SensA
date                           
2018-04-03 13:54:47.274   -0.45
2018-04-03 13:55:46.484   -0.42
2018-04-03 13:56:56.235   -0.37
2018-04-03 13:57:57.207   -0.34
2018-04-03 13:59:34.636   -0.33

series.head()
date
2018-04-03 14:09:36.577    62.2
2018-04-03 14:10:28.138    63.5
2018-04-03 14:11:27.400    63.1
2018-04-03 14:12:39.623    62.6
2018-04-03 14:13:27.310    62.5
Name: SensA_rrT, dtype: float64

df = series.to_frame().combine_first(df)

df.head(10)
                          SensA  SensA_rrT
date                           
2018-04-03 13:54:47.274   -0.45        NaN
2018-04-03 13:55:46.484   -0.42        NaN
2018-04-03 13:56:56.235   -0.37        NaN
2018-04-03 13:57:57.207   -0.34        NaN
2018-04-03 13:59:34.636   -0.33        NaN
2018-04-03 14:00:34.565   -0.33        NaN
2018-04-03 14:01:19.994   -0.37        NaN
2018-04-03 14:02:29.636   -0.34        NaN
2018-04-03 14:03:31.599   -0.32        NaN
2018-04-03 14:04:30.779   -0.33        NaN
2018-04-03 14:05:31.733   -0.35        NaN
2018-04-03 14:06:33.290   -0.38        NaN
2018-04-03 14:07:37.459   -0.39        NaN
2018-04-03 14:08:36.361   -0.36        NaN
2018-04-03 14:09:36.577   -0.37       62.2

I wasted couple of hours on the same issue. In my case, I had to reset_index() of a dataframe before using apply function. Before merging, or looking up from another indexed dataset, you need to reset the index as 1 dataset can have only 1 Index.

I got this error when I tried adding a column from a different table. Indeed I got duplicate index values along the way. But it turned out I was just doing it wrong: I actually needed to df.join the other table.

This pointer might help someone in a similar situation.

This can also be a cause for this[:) I solved my problem like this]

It may happen even if you are trying to insert a dataframe type column inside dataframe

you can try this

df['my_new']=pd.Series(my_new.values)

if you get this error after merging two dataframe and remove suffix adnd try to write to excel Your problem is that there are columns you are not merging on that are common to both source DataFrames. Pandas needs a way to say which one came from where, so it adds the suffixes, the defaults being '_x' on the left and '_y' on the right.

If you have a preference on which source data frame to keep the columns from, then you can set the suffixes and filter accordingly, for example if you want to keep the clashing columns from the left:

# Label the two sides, with no suffix on the side you want to keep
df = pd.merge(
    df, 
    tempdf[what_i_care_about], 
    on=['myid', 'myorder'], 
    how='outer',
    suffixes=('', '_delete_suffix')  # Left gets no suffix, right gets something identifiable
)
# Discard the columns that acquired a suffix
df = df[[c for c in df.columns if not c.endswith('_delete_suffix')]]

Alternatively, you can drop one of each of the clashing columns prior to merging, then Pandas has no need to assign a suffix.

Just add .to_numpy() to the end of the series you want to concatenate.

In my case it was caused by mismatch in dimensions:

accidentally using a column from different df during the mul operation

It happened to me when I appended 2 dataframes into another (df3 = df1.append(df2)), so the output was:

df1
    A   B
0   1   a
1   2   b
2   3   c

df2
    A   B
0   4   d
1   5   e
2   6   f

df3
    A   B
0   1   a
1   2   b
2   3   c
0   4   d
1   5   e
2   6   f

The simplest way to fix the indexes is using the "df.reset_index(drop=bool, inplace=bool)" method, as Connor said... you can also set the 'drop' argument True to avoid the index list to be created as a columns, and 'inplace' to True to make the indexes reset permanent.

Here is the official refference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reset_index.html

In addition, you can also use the ".set_index(keys=list, inplace=bool)" method, like this:

new_index_list = list(range(0, len(df3)))
df3['new_index'] = new_index_list 
df3.set_index(keys='new_index', inplace=True)

official refference: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.set_index.html