Remove rows with duplicate indices (Pandas DataFrame and TimeSeries)

Question

I\'m reading some automated weather data from the web. The observations occur every 5 minutes and are compiled into monthly files for each weather station. Once I\'m done pa

Answer 1

Oh my. This is actually so simple!

grouped = df3.groupby(level=0)
df4 = grouped.last()
df4
                      A   B  rownum

2001-01-01 00:00:00   0   0       6
2001-01-01 01:00:00   1   1       7
2001-01-01 02:00:00   2   2       8
2001-01-01 03:00:00   3   3       3
2001-01-01 04:00:00   4   4       4
2001-01-01 05:00:00   5   5       5

Follow up edit 2013-10-29 In the case where I have a fairly complex MultiIndex, I think I prefer the groupby approach. Here's simple example for posterity:

import numpy as np
import pandas

# fake index
idx = pandas.MultiIndex.from_tuples([('a', letter) for letter in list('abcde')])

# random data + naming the index levels
df1 = pandas.DataFrame(np.random.normal(size=(5,2)), index=idx, columns=['colA', 'colB'])
df1.index.names = ['iA', 'iB']

# artificially append some duplicate data
df1 = df1.append(df1.select(lambda idx: idx[1] in ['c', 'e']))
df1
#           colA      colB
#iA iB                    
#a  a  -1.297535  0.691787
#   b  -1.688411  0.404430
#   c   0.275806 -0.078871
#   d  -0.509815 -0.220326
#   e  -0.066680  0.607233
#   c   0.275806 -0.078871  # <--- dup 1
#   e  -0.066680  0.607233  # <--- dup 2

and here's the important part

# group the data, using df1.index.names tells pandas to look at the entire index
groups = df1.groupby(level=df1.index.names)  
groups.last() # or .first()
#           colA      colB
#iA iB                    
#a  a  -1.297535  0.691787
#   b  -1.688411  0.404430
#   c   0.275806 -0.078871
#   d  -0.509815 -0.220326
#   e  -0.066680  0.607233