title: pandas.to_csv outputs a column of floats instead of integers
body: <p>I'm working on project involving querying data from a dataframe, performing a few operations on it and then storing it in a csv.  Here is the stripped-down code.  </p>
  
  <p>get_value() is a function which returns the mean of five values gotten from a query, forced into int. </p>
  
  <pre><code>import pandas as pd
  d = pd.DataFrame(columns=['"Column1"','"Column2"'])
  test = pd.read_csv("./test.csv", header = None, low_memory=False)
  for line in range(1, 15):
      if test.values[line][5] == '1':
          value = str(get_value(line, 1))
      else:
          value = str(get_value(line, 0))
      d.loc[line-1]=[line,value]
  d.to_csv('output.csv', index = False)
  </code></pre>
  
  <p>Unfortunately, whenever I do so I get the first column (line, obviously an integer here) as a series of floats.  Sample output:</p>
  
  <pre><code>1.0,4859
  2.0,7882
  3.0,10248
  4.0,8098
  5.0,8048
  6.0,6087
  7.0,7349
  8.0,8246
  9.0,5863
  10.0,5962
  11.0,7641
  12.0,8127
  13.0,7808
  14.0,9886
  </code></pre>
  
  <p>Replacing the to_csv with a print statement gives me a dataframe full of beautiful ints:</p>
  
  <pre><code>0      1    4859
  1      2    7882
  2      3   10248
  3      4    8098
  4      5    8048
  5      6    6087
  6      7    7349
  7      8    8246
  8      9    5863
  9     10    5962
  10    11    7641
  11    12    8127
  12    13    7808
  13    14    9886
  </code></pre>
  
  <p>As a result I suspect it's got something to do with to_csv, but I'm a novice and far from certain about that.  What's going on, and is there any workaround?  Thanks for reading.</p>
  
  <p>Edit:  DSM has helpfully suggested I run d.info().  It looks like he's right, and that they're int-looking floats.</p>
  
  <pre><code>Int64Index: 14 entries, 0 to 13
  Data columns (total 2 columns):
  "Id"       14 non-null float64
  "Sales"    14 non-null object
  </code></pre>