Drop a row in pandas Dataframe base on integer index location

Question

I have time series data that has duplicate timestamp indexes but I would only like to drop a single row based on the integer location. For example if I have the following :

import numpy as np
import pandas as pd

dates       = pd.to_datetime(["2015-10-22 09:40:00","2015-10-22 09:40:00","2015-10-22 09:40:00","2015-10-22 09:50:00","2015-10-22 10:00:00"])
data_rand   = np.random.rand(len(dates),3)
col_head    = ['A','B','C']

df          = pd.DataFrame(data=data_rand, index=dates, columns=col_head)

print(df)
rowindex    = 1

df.drop(df.index[rowindex], inplace=True)
#df.drop(df.index.iloc[[rowindex]], inplace=True)

print(df)

The data outputs a dataframe that looks like:

                            A         B         C
2015-10-22 09:40:00  0.755642  0.797471  0.366410
2015-10-22 09:40:00  0.475411  0.629229  0.733368
2015-10-22 09:40:00  0.003278  0.461901  0.184833
2015-10-22 09:50:00  0.803465  0.218510  0.864337
2015-10-22 10:00:00  0.153356  0.950724  0.249950

Now if I wanted to remove the second row I would use the drop function but because there are two other labels with the exact same index all three would be dropped. Is there a way to only drop the middle of the three duplicate time stamps? I would prefer to do this without resetting the index.

What I would want the data to look like is this:

                            A         B         C
2015-10-22 09:40:00  0.755642  0.797471  0.366410
2015-10-22 09:40:00  0.003278  0.461901  0.184833
2015-10-22 09:50:00  0.803465  0.218510  0.864337
2015-10-22 10:00:00  0.153356  0.950724  0.249950

Answer 1

You could use iloc or loc like

In [5055]: idx = np.ones(len(df.index), dtype=bool)

In [5057]: idx[rowindex] = False

In [5058]: df.iloc[idx]     # or df.loc[idx]
Out[5058]:
                            A         B         C
2015-10-22 09:40:00  0.704959  0.995358  0.355915
2015-10-22 09:40:00  0.151127  0.398876  0.240856
2015-10-22 09:50:00  0.343456  0.513128  0.666625
2015-10-22 10:00:00  0.105908  0.130895  0.321981

Details

In [5059]: df
Out[5059]:
                            A         B         C
2015-10-22 09:40:00  0.704959  0.995358  0.355915
2015-10-22 09:40:00  0.762548  0.593177  0.691702
2015-10-22 09:40:00  0.151127  0.398876  0.240856
2015-10-22 09:50:00  0.343456  0.513128  0.666625
2015-10-22 10:00:00  0.105908  0.130895  0.321981

Answer 2

Using np.arange and iloc to select rows other than rowindex . Much similar to dropping rowindex i.e (I suggest @Zero's answer in case of thinking of dropping multiple row indices)

rowindex = 2
ndf = df.iloc[~(np.arange(df.shape[0]) == rowindex )]

Output:

                       A         B         C
2015-10-22 09:40:00  0.568431  0.302549  0.497309
2015-10-22 09:40:00  0.683263  0.916699  0.108929
2015-10-22 09:50:00  0.751543  0.480892  0.797728
2015-10-22 10:00:00  0.282703  0.433418  0.009757

df
                            A         B         C
2015-10-22 09:40:00  0.568431  0.302549  0.497309
2015-10-22 09:40:00  0.683263  0.916699  0.108929
2015-10-22 09:40:00  0.495492  0.232836  0.436861
2015-10-22 09:50:00  0.751543  0.480892  0.797728
2015-10-22 10:00:00  0.282703  0.433418  0.009757