title: Create DataFrame from Loop with Bloomberg function
body: <p>I have a list of company ticker's:</p>
  
  <pre><code>df = {'Ticker': ['AVON LN EQUITY', 'GFS LN EQUITY'], 'Value': [1., 2.]}
  df = pd.DataFrame(df)
  </code></pre>
  
  <p>I am using the BLPAPI wrapper, <a href="https://github.com/alex314159/blpapiwrapper" rel="nofollow">https://github.com/alex314159/blpapiwrapper</a></p>
  
  <p>Using this i would like to get a DataFrame, of all the prices over on a monthly basis. For this i am using the BLP class and BDH function. Settings below (the longer version is in the link):</p>
  
  <pre><code>def bdh(self, strSecurity='SPX Index', strData='PX_LAST', startdate=datetime.date(2013, 1, 1), enddate=datetime.date(2016, 9, 6),
          adjustmentSplit=False, periodicity='MONTHLY', strOverrideField='', strOverrideValue=''):
  </code></pre>
  
  <p>for simplicity i created a mini function:</p>
  
  <pre><code>def bloom_func(x, func):
      bloomberg = BLP()
      return bloomberg.bdh(x, func, strOverrideField='BEST_FPERIOD_OVERRIDE', strOverrideValue='1GY' )
      bloomberg.closeSession()
  </code></pre>
  
  <p>Using these i can get a DataFrame for one equity. </p>
  
  <pre><code>Price = bloom_func('VOD LN EQUITY', 'PX_LAST')
  print (c)
  </code></pre>
  
  <p>which works. </p>
  
  <p>However, when i try to run this in a across the 2 companies using:</p>
  
  <pre><code>df1 = pd.concat([df.apply(lambda x: bloom_func(x)) for p in df['Ticker']])
  </code></pre>
  
  <p>i get ValueError: Shape of passed values is (2, 0), indices imply (2, 2). </p>