title: Pandas - possible to aggregate two columns using two different aggregations?
body: <p>I'm loading a csv file, which has the following columns:
  date, textA, textB, numberA, numberB</p>
  
  <p>I want to group by the columns: date, textA and textB - but want to apply "sum" to numberA, but "min" to numberB. </p>
  
  <pre><code>data = pd.read_table("file.csv", sep=",", thousands=',')
  grouped = data.groupby(["date", "textA", "textB"], as_index=False)
  </code></pre>
  
  <p>...but I cannot see how to then apply two different aggregate functions, to two different columns?
  I.e. <code>sum(numberA), min(numberB)</code></p>