title: Column names after transposing a dataframe
body: <p>I have a small dataframe - six rows (not counting the header) and 53 columns (a store name, and the rest weekly sales for the past year). Each row contains a particular store and each column the store's name and sales for each week. I need to transpose the data so that the weeks appear as rows, the stores appear as columns, and their sales appear as the rows.</p>
  
  <p>To generate the input data:</p>
  
  <pre><code>df_store = pd.read_excel(SourcePath+SourceFile, sheet_name='StoreSales', header=0, usecols=['StoreName'])
  # Number rows of all irrelevant stores.
  row_numbers = [x+1 for x in df_stores[(df_store['StoreName'] != 'Store1') &amp; (df_store['StoreName'] != 'Store2')
   &amp; (df_store['StoreName'] !='Store3')].index]
  
  # Read in entire Excel file, skipping the rows of irrelevant stores.
  df_store = pd.read_excel(SourcePath+SourceFile, sheet_name='StoreSales', header=0, usecols = "A:BE", 
      skiprows = row_numbers, converters = {'StoreName' : str})
  
  # Transpose dataframe
  df_store_t = df_store.transpose()
  </code></pre>
  
  <p>My output puts index numbers above each store name ( 0 to 5), and then each column starts out as StoreName (above the week), then each store name. Yet, I cannot manipulate them by their names.</p>
  
  <p>Is there a way to clear those index numbers so that I can work directly with the resulting column names (e.g., rename "StoreName" to "WeekEnding" and make reference to each store columns ("Store1", "Store2", etc.?)</p>