title: pandas dataframe time series drop duplicates
body: <p>I am trying to update temperature time series by combining 2 CSV files that may have duplicate rows at times.</p>
  
  <p>I have tried to implement <code>drop_duplicates</code> but it's not working for me.</p>
  
  <p>Here is an example of what I'm trying to do:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  
  from pandas import DataFrame, Series
  
  
  dfA = DataFrame({'date' : Series(['1/1/10','1/2/10','1/3/10','1/4/10'], index=[0,1,2,3]),
      'a' : Series([60,57,56,50], index=[0,1,2,3]),
      'b' : Series([80,73,76,56], index=[0,1,2,3])})
  
  print("dfA")     
  print(dfA)
  
  dfB = DataFrame({'date' : Series(['1/3/10','1/4/10','1/5/10','1/6/10'], index=[0,1,2,3]),
      'a' : Series([56,50,59,75], index=[0,1,2,3]),
      'b' : Series([76,56,73,89], index=[0,1,2,3])})
  
  print("dfB")
  print(dfB)
  
  dfC = dfA.append(dfB)
  
  print(dfC.duplicated())
  
  dfC.drop_duplicates()
  print("dfC")
  print(dfC)
  </code></pre>
  
  <p>And this is the output:</p>
  
  <pre><code>dfA
      a   b    date
  0  60  80  1/1/10
  1  57  73  1/2/10
  2  56  76  1/3/10
  3  50  56  1/4/10
  dfB
      a   b    date
  0  56  76  1/3/10
  1  50  56  1/4/10
  2  59  73  1/5/10
  3  75  89  1/6/10
  0    False
  1    False
  2    False
  3    False
  0     True
  1     True
  2    False
  3    False
  dtype: bool
  dfC
      a   b    date
  0  60  80  1/1/10
  1  57  73  1/2/10
  2  56  76  1/3/10
  3  50  56  1/4/10
  0  56  76  1/3/10
  1  50  56  1/4/10
  2  59  73  1/5/10
  3  75  89  1/6/10
  </code></pre>
  
  <p>How do I update a time series with overlapping data and not have duplicates?</p>