Reindexing data frame Pandas

Question

I am trying to split a data set for training and testing using Pandas.

data = pd.read_csv("housingdata.csv", header=None)
train = testing.sample(frac=0.6)
train.reindex()
test  = testing.loc[~testing.index.isin(train.index)]
print train
print test

when I print the data, I get

         0     1     2  3      4
9  0.17004  12.5  7.87  0  0.524
1  0.02731   0.0  7.07  0  0.469
5  0.02985   0.0  2.18  0  0.458
3  0.03237   0.0  2.18  0  0.458
7  0.14455  12.5  7.87  0  0.524
6  0.08829  12.5  7.87  0  0.524

         0     1     2  3      4
0  0.00632  18.0  2.31  0  0.538
2  0.02729   0.0  7.07  0  0.469
4  0.06905   0.0  2.18  0  0.458
8  0.21124  12.5  7.87  0  0.524

As noticed, the row indices are re-shuffled. How to re-index the rows in both the data sets?

This however does not change global settings. Eg.,

train.iloc[0,4]

gives 0.524

Answer 1

As @EdChum's comments point out, it's not exactly clear what behavior you're looking for. But if all you want to do is to give both new dataframes indices going from 0, 1, 2 ... n then you can use reset_index() :

train.reset_index(inplace=True, drop=True)
test.reset_index(inplace=True, drop=True)