data = pd.read_csv('DJIA.csv', header=0)

# prepare data
X = data.values
X = X.astype('float32')
train_size = int(len(X) * 0.50)
train, test = X[:train_size], X[train_size:]
# walk-forward validation
history = [x for x in train]
predictions = list()
for i in range(len(test)):
    # predict
    yhat = history[-1]
    predictions.append(yhat)
    # observation
    obs = test[i]
    history.append(obs)
    print('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))
# report performance
mse = mean_squared_error(test, predictions)
rmse = sqrt(mse)
print('RMSE: %.3f' % rmse)

ValueError                                Traceback (most recent call last)
<ipython-input-60-2ae5feb1d820> in <module>
      1 # prepare data
      2 X = data.values
----> 3 X = X.astype('float32')
      4 train_size = int(len(X) * 0.50)
      5 train, test = X[:train_size], X[train_size:]

ValueError: could not convert string to float: '2009-07-23'

TypeError                                 Traceback (most recent call last)
<ipython-input-61-dccb70d2484a> in <module>
     13         obs = test[i]
     14         history.append(obs)
---> 15         print('>Predicted=%.3f, Expected=%3.f' % (yhat, obs))
     16 # report performance
     17 mse = mean_squared_error(test, predictions)

TypeError: only size-1 arrays can be converted to Python scalars