Hi,
I'm trying this time to use a simple linear regression on my time series dataset to linearly predict data. But I got this error and I don't know how to handle it. Any ideas?
# Error
I'm trying this time to use a simple linear regression on my time series dataset to linearly predict data. But I got this error and I don't know how to handle it. Any ideas?
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# print df.head() eie Date_Time 2017 - 11 - 10 4470.76 2017 - 11 - 11 5465.72 2017 - 11 - 12 15465.72 2017 - 11 - 13 25465.72 2017 - 11 - 14 21480.59 y = np.array(df.values, dtype = float ) x = np.array(pd.to_datetime(df[ 'eie' ]).index.values, dtype = float ) slope, intercept, r_value, p_value, std_err = sp.linregress(x,y) xf = np.linspace( min (x), max (x), 100 ) xf1 = xf.copy() xf1 = pd.to_datetime(xf1) yf = (slope * xf) + intercept print ( 'r = ' , r_value, '\n' , 'p = ' , p_value, '\n' , 's = ' , std_err) |
Error:ValueError Traceback (most recent call last)
<ipython-input-13-5d30a02ce6af> in <module>
1 y = np.array(df.values, dtype=float)
----> 2 x = np.array(pd.to_datetime(df['eie']).index.values, dtype=float)
3
4 slope, intercept, r_value, p_value, std_err =sp.linregress(x,y)
5
ValueError: could not convert string to float: '2017-11-10'