title: Pandas "read_csv" Function Returns NAN for All Blocks in My Table
body: <p>My original CSV file has float64 values in each block but after I use pd.csv() to read the file, it returns me a blank table. I tried to set the delimiter and the encoding of the function but it didn't help at all.
  The CSV file is automatically generated by a software and I have no way check the settings of the settings.
  Is there any way I can read my file to a dataframe with correct values?</p>
  
  <pre><code>&gt;&gt;&gt; pd.read_csv('./HISTORY_LOG_05-31-2018.CSV')
  D  Unnamed: 1  Unnamed: 2      ...       Unnamed: 108  Unnamed: 109  Unnamed: 110
  0 NaN         NaN         NaN      ...                NaN           NaN           NaN
  1 NaN         NaN         NaN      ...                NaN           NaN           NaN
  2 NaN         NaN         NaN      ...                NaN           NaN           NaN
  3 NaN         NaN         NaN      ...                NaN           NaN           NaN
  4 NaN         NaN         NaN      ...                NaN           NaN           NaN
  5 NaN         NaN         NaN      ...                NaN           NaN           NaN
  
  [6 rows x 111 columns]
  </code></pre>
  
  <p>I simplified the CSV file to</p>
  
  <pre><code>A,B
  0.000,0.000
  
  0.000,0.000
  </code></pre>
  
  <p>and I still got results like:</p>
  
  <pre><code>&gt;&gt;&gt; pd.read_table('./HISTORY_LOG_05-31-2018.CSV', encoding="cp1252")
      D
  0 NaN
  1 NaN
  2 NaN
  
  &gt;&gt;&gt; pd.read_table('./HISTORY_LOG_05-31-2018.CSV', encoding="cp1252", delimiter=",")
      D  Unnamed: 1
  0 NaN         NaN
  1 NaN         NaN
  2 NaN         NaN
  </code></pre>