Convert float Series into an integer Series in pandas

Try converting with astype:

new_re_df = [s.iloc[np.where(ts.astype(int) == int(i))] for i in ts]

Edit

On suggestion by @Rutger Kassies a nicer way would be to cast series and then groupby:

rise_p['ts'] = (rise_p.time / 100).astype('int')

ts_grouped = rise_p.groupby('ts')

...

Here's a different way to solve your problem

In [3]: df
Out[3]: 
         time    magnitude
0  1379945444   156.627598
1  1379945447  1474.648726
2  1379945448  1477.448999
3  1379945449  1474.886202
4  1379945699  1371.454224

In [4]: df.dtypes
Out[4]: 
time           int64
magnitude    float64
dtype: object

Convert your epoch timestamps to seconds

In [7]: df['time'] = pd.to_datetime(df['time'],unit='s')

Set the index

In [8]: df.set_index('time',inplace=True)

In [9]: df
Out[9]: 
                       magnitude
time                            
2013-09-23 14:10:44   156.627598
2013-09-23 14:10:47  1474.648726
2013-09-23 14:10:48  1477.448999
2013-09-23 14:10:49  1474.886202
2013-09-23 14:14:59  1371.454224

Groupby 1min and mean the results (how= can be an arbitrary function as well)

In [10]: df.resample('1Min',how=np.mean)
Out[10]: 
                       magnitude
time                            
2013-09-23 14:10:00  1145.902881
2013-09-23 14:11:00          NaN
2013-09-23 14:12:00          NaN
2013-09-23 14:13:00          NaN
2013-09-23 14:14:00  1371.454224