title: float() argument must be a string or a number, not 'Timestamp'
body: <p>I can't make scilearn work with a datetime series.</p>
  
  <p>found this post but did not help me =
  <a href="https://stackoverflow.com/questions/41256626/pandas-typeerror-float-argument-must-be-a-string-or-a-number">Pandas : TypeError: float() argument must be a string or a number</a></p>
  
  <p>the csv file has 2 date columns with a date, dates are in the following format:
  2017-07-21 06:19:53 (string)</p>
  
  <p>i converted the string to an datetime64[ns], so the date became a long value and i could do calculations on it. scilearn refuses this type and gives the error  float() argument must be a string or a number, not 'Timestamp'</p>
  
  <p>also tried with pandas.to_datetime() no luck.</p>
  
  <p>the model i use in scilearn is the KMeans clustering model.
  when printing the dtypes this is the result:</p>
  
  <pre><code>ip                      int64
  date           datetime64[ns]
  succesFlag              int64
  app                     int64
  enddate        datetime64[ns]
  user_userid             int64
  dtype: object
  </code></pre>
  
  <p>Here is my code:</p>
  
  <pre><code>def getDataframe():
      df = pd.read_csv(filename)
      df['date']=df['date'].astype('datetime64[ns]',inplace=True)
      df['enddate']=df['enddate'].astype('datetime64[ns]',inplace=True)
      df['app']=df['app'].replace({
              "Azure": 0 ,
              "Peoplesoft":1,
              "Office":2 ,
              "DevOps":3 ,
              "Optima":4 ,
              "Ada-Tech": 5 
           },inplace=True)    
      df['ip']=df['ip'].apply(lambda x: int(ip4.ip_address(x))).to_frame('ip')
      print(df.dtypes)
      return df
  </code></pre>
  
  <p>the expectation was that KMeans clustering model would work with numerical  values as i converted them but it did not.</p>
  
  <p>what did i wrong ?</p>