Pandas recalculate index after a concatenation

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.

If your index is autogenerated and you don't want to keep it, you can use the ignore_index option. `

train_df = pd.concat(train_class_df_list, ignore_index=True)

This will autogenerate a new index for you, and my guess is that this is exactly what you are after.

After vertical concatenation, if you get an index of [0, n) followed by [0, m), all you need to do is call reset_index:

train_df.reset_index(drop=True)

(you can do this in place using inplace=True).

---

import pandas as pd

>>> pd.concat([
    pd.DataFrame({'a': [1, 2]}), 
    pd.DataFrame({'a': [1, 2]})]).reset_index(drop=True)
    a
0	1
1	2
2	1
3	2

This should work:

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

Set drop to True to avoid an additional column in your dataframe.