[ Python 常見問題 ] Pandas recalculate index after a concatenation

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Question
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:

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1. train_df = pd.concat(train_class_df_list)  
2. 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.

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

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1. train_df.reset_index(drop=True)  

you can do this in place using inplace=True:

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1. >>> import pandas as pd  
2. >>> cat_df = pd.concat([pd.DataFrame({'a':[1,2]}), pd.DataFrame({'a':[1,2]})])  
3. >>> cat_df.index  
4. Int64Index([0, 1, 0, 1], dtype='int64')  
5. >>> cat_df.reset_index(drop=True, inplace=True)  
6. >>> cat_df.index  
7. RangeIndex(start=0, stop=4, step=1)  
8. >>> cat_df  
9.    a  
10. 0  1  
11. 1  2  
12. 2  1  
13. 3  2