text: Title: The DataFrame serialisation is slower than in v1
  Body: Using python pandas. Version 1 i used this:
  
  ```python
  def dbpop_influx(data, dbname, measurement, columns):
      ## constants:
      dbclient = DataFrameClient(host='localhost', port=8086, username='root', password='root', database=dbname)
      n_import_chunks = math.ceil(len(data) / 10000)
      data_chunks = np.array_split(data, n_import_chunks)
      for d in data_chunks:
          dbclient.write_points(d, measurement, tag_columns = columns, protocol = 'line')
  ```
  
  Takes 29 seconds (was looking to improve that speed with multiprocessing)
  
  Version 2 i used this:
  
  ```python
  _client = InfluxDBClient(url="http://localhost:9999", token=token, org="org")
  _write_client = _client.write_api(write_options=WriteOptions(batch_size=10000,
                                                               flush_interval=10_000,
                                                               jitter_interval=0,
                                                               retry_interval=5_000))
  
  
  start = time.time()
  _write_client.write('data', record=imp_dat[0], data_frame_measurement_name='coinmarketcap_ohlcv',
                      data_frame_tag_columns=['quote_asset','base_asset'])
  print(time.time() - start)
  ```
  
  this takes 118 seconds...
  
  data looks like:
  ![image](https://user-images.githubusercontent.com/32384270/81547780-3a30fd80-9374-11ea-9df4-f0d030fb08c9.png)
  
  
  @bednar 