title: Integer in python/pandas becomes BLOB (binary) in sqlite
body: <p>Storing an integer in sqlite results in BLOBs (binary values) instead of INTEGER in sqlite. The problem is the INT in the "Baujahr" column. The table is created. </p>
  
  <pre><code>CREATE TABLE "Objekt" ( `No` INTEGER NOT NULL UNIQUE, `Objekt_id` INTEGER, 
  `Strecke` TEXT, `Baujahr` INTEGER, `Eigentümer` TEXT, PRIMARY KEY(`No`) )
  </code></pre>
  
  <p>dataframe and dtypes: </p>
  
  <pre><code>id  Strecke  Baujahr Eigentümer Objekt_id
  5     A23     1938    Fronz      0327
  
  Objekt.dtypes
  Strecke       object
  Baujahr        int64
  Eigentümer    object
  Objekt_id     object
  dtype: object
  </code></pre>
  
  <p>The DataFrame ist written to sqlite</p>
  
  <pre><code>stmt ="INSERT INTO Objekt (Objekt_id, Strecke, Baujahr, Eigentümer) VALUES (?, ?, ?, ?)"
  c.execute(stmt, (Objekt.Objekt_id.values[0], Objekt.Strecke.values[0], 
  Objekt.Baujahr.values[0], Objekt.Eigentümer.values[0] ))
  conn.commit()
  </code></pre>
  
  <p>Sqlite works only up to  INT 8 and works not with INT 32 or INT64 (The definition BIGINT in the CREATE TABLE .... does not help either). So I tried to convert using these conversions</p>
  
  <pre><code>Objekt.Baujahr.astype(int)
  Objekt.Baujahr.astype(np.int8)
  Objekt.Baujahr = int(Objekt.Baujahr)
  </code></pre>
  
  <p>The dtypes command shows that Baujahr remained int64!!</p>
  
  <p>I cannot edit the values in the database and by querying these values i get a binary back. Any idea?</p>
  
  <p>Python 3.6.4, sqlite3 2.6.0, pandas 0.22.0</p>