title: Merge dataframe with dask and convert it to pandas
body: <p>I have two dataframe </p>
  
  <p>dataframe1: </p>
  
  <pre><code>    &gt;df_case = dd.read_csv('s3://../.../df_case.csv')
      &gt;df_case.head(1)
  sacc_id$                   id$             creation_date
   0  001A000000hwvV0IAI  5001200000ZnfUgAAJ  2016-06-07 14:38:02
  </code></pre>
  
  <p>dataframe2:</p>
  
  <pre><code>&gt;df_limdata = dd.read_csv('s3://../.../df_limdata.csv')
  &gt;df_limdata.head(1)
       sacc_id$            opp_line_id$           oppline_creation_date
  0   001A000000hAUn8IAG  a0W1200000G0i3UEAR  2015-06-10
  </code></pre>
  
  <p>First, I did a merge of the 2 dataframes :</p>
  
  <pre><code>&gt; case = dd.merge(df_limdata, df_case, left_on='sacc_id$',right_on='sacc_id$')
  
  &gt;case
  
  Dask DataFrame Structure:
      Unnamed: 0_x    sacc_id$    opp_line_id$_x  oppline_creation_date_x     Unnamed: 0_y    opp_line_id$_y  oppline_creation_date_y
  npartitions=5                           
      int64   object  object  object  int64   object  object
      ...     ...     ...     ...     ...     ...     ...
  ...     ...     ...     ...     ...     ...     ...     ...
      ...     ...     ...     ...     ...     ...     ...
      ...     ...     ...     ...     ...     ...     ...
  Dask Name: hash-join, 78 tasks
  </code></pre>
  
  <p>Then I try to convert this dask case dataframe to pandas dataframe :</p>
  
  <pre><code>&gt; # conversion to pandas
  df = case.compute()
  </code></pre>
  
  <p>I get this error : </p>
  
  <pre><code>ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
  
  +------------+---------+----------+
  | Column     | Found   | Expected |
  +------------+---------+----------+
  | Unnamed: 0 | float64 | int64    |
  +------------+---------+----------+
  
  Usually this is due to dask's dtype inference failing, and
  *may* be fixed by specifying dtypes manually by adding:
  
  dtype={'Unnamed: 0': 'float64'}
  
  to the call to `read_csv`/`read_table`.
  
  Alternatively, provide `assume_missing=True` to interpret
  all unspecified integer columns as floats.
  </code></pre>
  
  <p>Can you help me to resolve this problem please?</p>
  
  <p>Thank you</p>