title: sklearn-LinearRegression: could not convert string to float: '--'
body: <p>I am trying to use a LinearRegression from sklearn and I am getting a 'Could not convert a string to float'. All columns of the dataframe are float and the output y is also float. I have looked at other posts and the suggestions are to convert to float which I have done.</p>
  
  <pre><code>&lt;class 'pandas.core.frame.DataFrame'&gt;
  Int64Index: 789 entries, 158 to 684
  Data columns (total 8 columns):
  f1     789 non-null float64
  f2     789 non-null float64
  f3     789 non-null float64
  f4     789 non-null float64
  f5     789 non-null float64
  f6     789 non-null float64
  OFF    789 non-null uint8
  ON     789 non-null uint8
  dtypes: float64(6), uint8(2)
  memory usage: 44.7 KB
  
  type(y_train)
  pandas.core.series.Series
  type(y_train[0])
  float
  
  from sklearn.model_selection import train_test_split
  X_train,X_test,y_train,y_test=train_test_split(X,Y,random_state=0)
  X_train.head()
  from sklearn.linear_model import LinearRegression
  linreg = LinearRegression().fit(X_train, y_train)
  </code></pre>
  
  <p>The error I get is a</p>
  
  <pre><code>ValueError                                Traceback (most recent call last)
  &lt;ipython-input-282-c019320f8214&gt; in &lt;module&gt;()
        6 X_train.head()
        7 from sklearn.linear_model import LinearRegression
  ----&gt; 8 linreg = LinearRegression().fit(X_train, y_train)
  510         n_jobs_ = self.n_jobs
      511         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
  --&gt; 512                          y_numeric=True, multi_output=True)
      513 
      514         if sample_weight is not None and np.atleast_1d(sample_weight).ndim &gt; 1:
  
   527         _assert_all_finite(y)
      528     if y_numeric and y.dtype.kind == 'O':
  --&gt; 529         y = y.astype(np.float64)
      530 
      531     check_consistent_length(X, y)
  
  ValueError: could not convert string to float: '--'
  </code></pre>
  
  <p>Please help. </p>