title: Error while implementing ARIMA in Python on Quandl Data
body: <pre><code>df = quandl.get('NSE/TATAMOTORS', start_date='2000-01-01', end_date='2018-05-10')
  
  df=df.drop(['Last','Total Trade Quantity','Turnover (Lacs)'], axis=1)
  df.head(10)
  </code></pre>
  
  <p>OUTPUT - </p>
  
  <pre><code>  Open    High     Low   Close
  Date                                      
  2003-12-26  435.80  440.50  431.65  438.60
  2003-12-29  441.00  449.70  441.00  447.80
  2003-12-30  450.00  451.90  430.10  442.40
  2003-12-31  446.00  459.30  443.55  452.05
  2004-01-01  453.25  457.90  451.50  454.45
  2004-01-02  458.00  460.35  454.05  456.40
  2004-01-05  458.00  465.00  450.60  454.85
  2004-01-06  460.00  465.00  448.50  454.45
  2004-01-07  451.40  454.70  438.10  446.45
  2004-01-08  449.00  466.95  449.00  464.75
  </code></pre>
  
  <p>-</p>
  
  <pre><code>from statsmodels.tsa.arima_model import ARIMA
  model = ARIMA(df, order=(5,1,0))
  </code></pre>
  
  <p>OUTPUT - </p>
  
  <pre><code>Traceback (most recent call last):
  
    File "&lt;ipython-input-90-799de8e60d6f&gt;", line 1, in &lt;module&gt;
      model = ARIMA(df, order=(5,1,0))
  
    File "D:\A\lib\site-packages\statsmodels\tsa\arima_model.py", line 1000, in __new__
      mod.__init__(endog, order, exog, dates, freq, missing)
  
    File "D:\A\lib\site-packages\statsmodels\tsa\arima_model.py", line 1024, in __init__
      self.data.ynames = 'D.' + self.endog_names
  
  TypeError: must be str, not list
  </code></pre>
  
  <p>So i converted the index column containing dates to proper column 
  by -</p>
  
  <pre><code>df = df.reset_index()
  df.head(10)
  </code></pre>
  
  <p>Out[92]: </p>
  
  <pre><code>        Date    Open    High     Low   Close
  0 2003-12-26  435.80  440.50  431.65  438.60
  1 2003-12-29  441.00  449.70  441.00  447.80
  2 2003-12-30  450.00  451.90  430.10  442.40
  3 2003-12-31  446.00  459.30  443.55  452.05
  4 2004-01-01  453.25  457.90  451.50  454.45
  5 2004-01-02  458.00  460.35  454.05  456.40
  6 2004-01-05  458.00  465.00  450.60  454.85
  7 2004-01-06  460.00  465.00  448.50  454.45
  8 2004-01-07  451.40  454.70  438.10  446.45
  9 2004-01-08  449.00  466.95  449.00  464.75
  </code></pre>
  
  <p>then when i run this line -</p>
  
  <pre><code>from statsmodels.tsa.arima_model import ARIMA
  model = ARIMA(df, order=(5,1,0))
  </code></pre>
  
  <p>OUTPUT -</p>
  
  <pre><code>Traceback (most recent call last):
  
    File "&lt;ipython-input-94-799de8e60d6f&gt;", line 1, in &lt;module&gt;
      model = ARIMA(df, order=(5,1,0))
  
    File "D:\A\lib\site-packages\statsmodels\tsa\arima_model.py", line 1000, in __new__
      mod.__init__(endog, order, exog, dates, freq, missing)
  
    File "D:\A\lib\site-packages\statsmodels\tsa\arima_model.py", line 1015, in __init__
      super(ARIMA, self).__init__(endog, (p, q), exog, dates, freq, missing)
  
    File "D:\A\lib\site-packages\statsmodels\tsa\arima_model.py", line 452, in __init__
      super(ARMA, self).__init__(endog, exog, dates, freq, missing=missing)
  
    File "D:\A\lib\site-packages\statsmodels\tsa\base\tsa_model.py", line 43, in __init__
      super(TimeSeriesModel, self).__init__(endog, exog, missing=missing)
  
    File "D:\A\lib\site-packages\statsmodels\base\model.py", line 212, in __init__
      super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
  
    File "D:\A\lib\site-packages\statsmodels\base\model.py", line 63, in __init__
      **kwargs)
  
    File "D:\A\lib\site-packages\statsmodels\base\model.py", line 88, in _handle_data
      data = handle_data(endog, exog, missing, hasconst, **kwargs)
  
    File "D:\A\lib\site-packages\statsmodels\base\data.py", line 630, in handle_data
      **kwargs)
  
    File "D:\A\lib\site-packages\statsmodels\base\data.py", line 76, in __init__
      self.endog, self.exog = self._convert_endog_exog(endog, exog)
  
    File "D:\A\lib\site-packages\statsmodels\base\data.py", line 471, in _convert_endog_exog
      raise ValueError("Pandas data cast to numpy dtype of object. "
  
  ValueError: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).
  </code></pre>
  
  <p>HELP?</p>