title: Python Pandas How to assign groupby operation results back to columns in parent dataframe?
body: <p>I have the following data frame in IPython, where each row is a single stock:</p>
  
  <pre><code>In [261]: bdata
  Out[261]:
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  Int64Index: 21210 entries, 0 to 21209
  Data columns:
  BloombergTicker      21206  non-null values
  Company              21210  non-null values
  Country              21210  non-null values
  MarketCap            21210  non-null values
  PriceReturn          21210  non-null values
  SEDOL                21210  non-null values
  yearmonth            21210  non-null values
  dtypes: float64(2), int64(1), object(4)
  </code></pre>
  
  <p>I want to apply a groupby operation that computes cap-weighted average return across everything, per each date in the "yearmonth" column.</p>
  
  <p>This works as expected:</p>
  
  <pre><code>In [262]: bdata.groupby("yearmonth").apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())
  Out[262]:
  yearmonth
  201204      -0.109444
  201205      -0.290546
  </code></pre>
  
  <p>But then I want to sort of "broadcast" these values back to the indices in the original data frame, and save them as constant columns where the dates match.</p>
  
  <pre><code>In [263]: dateGrps = bdata.groupby("yearmonth")
  
  In [264]: dateGrps["MarketReturn"] = dateGrps.apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())
  ---------------------------------------------------------------------------
  TypeError                                 Traceback (most recent call last)
  /mnt/bos-devrnd04/usr6/home/espears/ws/Research/Projects/python-util/src/util/&lt;ipython-input-264-4a68c8782426&gt; in &lt;module&gt;()
  ----&gt; 1 dateGrps["MarketReturn"] = dateGrps.apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())
  
  TypeError: 'DataFrameGroupBy' object does not support item assignment
  </code></pre>
  
  <p>I realize this naive assignment should not work. But what is the "right" Pandas idiom for assigning the result of a groupby operation into a new column on the parent dataframe?</p>
  
  <p>In the end, I want a column called "MarketReturn" than will be a repeated constant value for all indices that have matching date with the output of the groupby operation.</p>
  
  <p>One hack to achieve this would be the following:</p>
  
  <pre><code>marketRetsByDate  = dateGrps.apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())
  
  bdata["MarketReturn"] = np.repeat(np.NaN, len(bdata))
  
  for elem in marketRetsByDate.index.values:
      bdata["MarketReturn"][bdata["yearmonth"]==elem] = marketRetsByDate.ix[elem]
  </code></pre>
  
  <p>But this is slow, bad, and unPythonic.</p>