title: Can I change the default timestamp interpretation in pandas read_json
body: <p>I have a JSON data file with an unfortunately named "timestamp" field. The timestamp is formatted as <b><code>"%y%m%d%H%M%S"</code></b> but pandas.json_read() interprets this as a UNIX epoch timestamp so the field is interpreted as sometime in 1975.</p>
  
  <p>Is there a way to override the read_json behavior to provide a format specifier - similar to what pandas.to_json(...,date_format="...")) does?</p>
  
  <pre><code>import StringIO
  import pandas as pd
  my_json_data='''[
    {
      "itemId": "alpha:136:1",
      "testTime": 12.449,
      "workTime": 152.5,
      "project": "alpha",
      "user": "user100021-su7d",
      "timestamp": "170520161430",
      "accuracy": 1
    },
    {
      "itemId": "alpha:136:10",
      "testTime": 4.114,
      "workTime": 152.5,
      "project": "alpha",
      "user": "user100021-su7d",
      "timestamp": "170520161430",
      "accuracy": 0.8890000000000001
    },
    {
      "itemId": "alpha:136:100",
      "testTime": 5.114,
      "workTime": 43.4,
      "project": "alpha",
      "user": "user100021-su7d",
      "timestamp": "170522150338",
      "accuracy": 0.875
    }
  ]
  '''
  
  my_df = pd.read_json(StringIO.StringIO(my_json_data))
  my_df["timestamp"]
  </code></pre>
  
  <p>output is:</p>
  
  <pre><code>0   1975-05-28 14:42:41.430
  1   1975-05-28 14:42:41.430
  2   1975-05-28 15:15:50.338
  Name: timestamp, dtype: datetime64[ns]
  </code></pre>