title: Pandas : ValueError ( any way to convert Sparse[float64, 0.0] dtypes to float64 datatype )
body: <p>I have a dataframe  X_train to which i am concatenating a couple of another dataframe. This second &amp; third dataframe is obtained from sparse matrix which has been been generated by a TF-IDF VEctorizer</p>
  
  <pre><code>q1_train_df = pd.DataFrame.sparse.from_spmatrix(q1_tdidf_train,index=X_train.index,columns=q1_features)
  q2_train_df = pd.DataFrame.sparse.from_spmatrix(q2_tdidf_train,index=X_train.index,columns=q2_features)
  X_train_final  = pd.concat([X_train,q1_train_df,q2_train_df],axis=1)
  </code></pre>
  
  <p>X_train_final dtypes is looking as below</p>
  
  <pre><code>
  X_train_final.dtypes
  
  cwc_min                       float64
  cwc_max                       float64
  csc_min                       float64
  csc_max                       float64
  ctc_min                       float64
                           ...         
  q2_zealand       Sparse[float64, 0.0]
  q2_zero          Sparse[float64, 0.0]
  q2_zinc          Sparse[float64, 0.0]
  q2_zone          Sparse[float64, 0.0]
  q2_zuckerberg    Sparse[float64, 0.0]
  Length: 10015, dtype: object
  </code></pre>
  
  <p>I am using XGBoost to train on this final dataframe and this is throwing error while trying to fit the data</p>
  
  <pre><code>model.fit( X_train_final,y_train)
  
  
  ValueError: DataFrame.dtypes for data must be int, float or bool.
                  Did not expect the data types in fields q1_04, q1_10, q1_100, q
  </code></pre>
  
  <p>I think the error is due to Sparse[float64,0.0] dtypes present in it . Can you please help me out, not able to figure out how to get out of this error ??</p>