title: Combine two Pandas dataframes, resample on one time column by averaging
body: <p>My question is somewhat similar to this one with a few key differences:</p>
  
  <p><a href="https://stackoverflow.com/questions/26517125/combine-two-pandas-dataframes-resample-on-one-time-column-interpolate">Combine two Pandas dataframes, resample on one time column, interpolate</a></p>
  
  <p>I have two data sets acquired simultaneously by different acquisition systems with different sampling rates - one acquires data every one second (df2) and the second acquires data every 11 minutes (df1). I would like to create a single dataframe containing both data sets where the time index of the combined dataframe will be that from the 11-minute sampling frequency dataframe (df1). The data within this dataframe will be the original data from df1 with the data from the 1-second dataframe (df2) averaged over the relevant 11 minute period and appended to df1.</p>
  
  <p>Here is some example data:</p>
  
  <pre><code>from datetime import datetime, timedelta
  import pandas as pd
  import numpy as np
  
  todays_date = datetime.now().date()
  index1 = pd.date_range(todays_date-timedelta(10), periods=10, freq='11min')
  index2 = pd.date_range(todays_date-timedelta(10), periods=6000, freq='S')
  columns1 = [15, 17, 19, 21, 24, 27, 30, 34, 38, 43, 48, 54, 60, 67, 75, 84, 
  94, 105, 118, 132, 148, 166, 186, 208, 233, 261, 292, 327, 366, 410, 459, 
  514, 576, 645, 722, 809, 906]
  columns2 = [103.73, 111.469, 119.786, 128.723, 138.327, 148.647, 159.737, 
  171.655, 184.462, 198.224, 213.013, 228.905, 245.984, 264.336, 284.057, 
  305.25, 328.024, 352.497, 378.797, 407.058, 437.427, 470.063, 505.133,     
  542.82, 583.319, 626.839, 673.606, 723.862, 777.868, 835.903, 898.268, 
  965.286, 1037.304, 1114.695, 1197.86, 1287.23, 1383.267, 1486.47, 1597.372, 
  1716.548, 1844.616, 1982.239, 2130.13, 2289.054, 2459.835, 2643.358, 
  2840.573, 3052.502, 3280.243, 3524.975, 3787.966, 4070.578, 4374.274, 
  4700.629, 5051.333, 5428.202, 5833.189, 6268.39, 6736.061, 7238.624, 
  7778.682, 8359.033, 8982.682, 9652.861, 10373.039, 11146.949, 11978.599, 
  12872.296, 13832.67, 14864.696, 15973.718, 17165.483, 18446.161, 19822.39, 
  21301.296, 22890.539, 24598.352, 26433.582]
  data1 = np.random.rand(10, 37)*1000
  data2 = np.random.rand(6000, 78)*1000
  df1 = pd.DataFrame(data1, index=index1, columns=columns1)
  df2 = pd.DataFrame(data2, index=index2, columns=columns2) 
  </code></pre>
  
  <p>What is the simplest way to do this?</p>