I have a problem where I produce a pandas dataframe by concatenating along the row axis (stacking vertically). 

Each of the constituent dataframes has an autogenerated index (ascending numbers). 

After concatenation, my index is screwed up: it counts up to n (where n is the shape[0] of the corresponding dataframe), and restarts at zero at the next dataframe. 

I am trying to "re-calculate the index, given the current order", or "re-index" (or so I thought). Turns out that isn't exactly what `DataFrame.reindex` seems to be doing. 

---

Here is what I tried to do:

```
train_df = pd.concat(train_class_df_list)
train_df = train_df.reindex(index=[i for i in range(train_df.shape[0])])

```

It failed with "cannot reindex from a duplicate axis." I don't want to change the order of my data... just need to delete the old index and set up a new one, with the order of rows preserved.