[Python] Pandas + sklearn Linear regression fails

I am trying to implement some linear regression model in Python. See the code below, which I've used to make a linear regression.

import pandas
salesPandas = pandas.DataFrame.from_csv('home_data.csv')

# check the shape of the DataFrame (rows, columns)
salesPandas.shape
(21613, 20)

from sklearn.cross_validation import train_test_split

train_dataPandas, test_dataPandas = train_test_split(salesPandas, train_size=0.8, random_state=1)

from sklearn.linear_model import LinearRegression

reg_model_Pandas = LinearRegression()

print type(train_dataPandas)
print train_dataPandas.shape
<class 'pandas.core.frame.DataFrame'>
(17290, 20)

print type(train_dataPandas['price'])
print train_dataPandas['price'].shape
<class 'pandas.core.series.Series'>
(17290L,)

X = train_dataPandas
y = train_dataPandas['price']
reg_model_Pandas.fit(X, y)


After I've executed the python code above, the following error appears:

---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-dc363e199032> in <module>()
3 X = train_dataPandas
4 y = train_dataPandas['price']
----> 5 reg_model_Pandas.fit(X, y)

C:\Users\...\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\linear_model\base.py in fit(self, X, y, n_jobs)
374 n_jobs_ = self.n_jobs
375 X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 376 y_numeric=True, multi_output=True)
377
378 X, y, X_mean, y_mean, X_std = self._center_data(

C:\Users\...\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric)
442 X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
443 ensure_2d, allow_nd, ensure_min_samples,
--> 444 ensure_min_features)
445 if multi_output:
446 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

C:\Users\...\AppData\Local\Continuum\Anaconda2\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features)
342 else:
343 dtype = None
--> 344 array = np.array(array, dtype=dtype, order=order, copy=copy)
345 # make sure we actually converted to numeric:
346 if dtype_numeric and array.dtype.kind == "O":

ValueError: invalid literal for float(): 20140610T000000


Output from train_dataPandas.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 17290 entries, 4058200630 to 1762600320
Data columns (total 20 columns):
date 17290 non-null object
price 17290 non-null int64
bedrooms 17290 non-null int64
bathrooms 17290 non-null float64
sqft_living 17290 non-null int64
sqft_lot 17290 non-null int64
floors 17290 non-null float64
waterfront 17290 non-null int64
view 17290 non-null int64
condition 17290 non-null int64
grade 17290 non-null int64
sqft_above 17290 non-null int64
sqft_basement 17290 non-null int64
yr_built 17290 non-null int64
yr_renovated 17290 non-null int64
zipcode 17290 non-null int64
lat 17290 non-null float64
long 17290 non-null float64
sqft_living15 17290 non-null int64
sqft_lot15 17290 non-null int64
dtypes: float64(4), int64(15), object(1)
memory usage: 2.8+ MB

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