docstr: Selects data from the Binance API and returns it as a pandas DataFrame.
  
  Returns dataframe representing the Binance API results.
  
  Args:
      query (ast.Select): Given SQL SELECT query
docstr_tok: ["Selects","data","from","the","Binance","API","and","returns","it","as","a","pandas","DataFrame",".","Returns","dataframe","representing","the","Binance","API","results",".","Args",":","query","(","ast.Select",")",":","Given","SQL","SELECT","query"]
code: def select(self, query: ast.Select) -> pd.DataFrame:
      """Selects data from the Binance API and returns it as a pandas DataFrame.
  
      Returns dataframe representing the Binance API results.
  
      Args:
          query (ast.Select): Given SQL SELECT query
      """
      conditions = extract_comparison_conditions(query.where)
      params = self._get_kline_params_from_conditions(conditions)
  
      total_results = params["limit"]
      if query.limit:
          total_results = query.limit.value
          params["limit"] = min(
              BinanceAggregatedTradesTable.DEFAULT_AGGREGATE_TRADE_LIMIT,
              query.limit.value,
          )
  
      if (
          total_results
          > BinanceAggregatedTradesTable.DEFAULT_AGGREGATE_TRADE_LIMIT
      ):
          # Max 1000 klines per API call so we need to combine multiple API calls.
          with ThreadPoolExecutor(
              max_workers=BinanceAggregatedTradesTable.MAX_THREAD_POOL_WORKERS
          ) as executor:
              aggregated_trades_data = self._get_batch_klines(
                  executor, total_results, params
              )
  
      else:
          aggregated_trades_data = self.handler.call_binance_api(
              method_name="klines", params=params
          )
  
      # Only return the columns we need to.
      columns = []
      for target in query.targets:
          if isinstance(target, ast.Star):
              columns = self.get_columns()
              break
          elif isinstance(target, ast.Identifier):
              columns.append(target.parts[-1])
          else:
              raise NotImplementedError
  
      if len(aggregated_trades_data) == 0:
          aggregated_trades_data = pd.DataFrame([], columns=columns)
      else:
          # Remove columns not part of select.
          aggregated_trades_data.columns = self.get_columns()
          for col in set(aggregated_trades_data.columns).difference(
              set(columns)
          ):
              aggregated_trades_data = aggregated_trades_data.drop(
                  col, axis=1
              )
  
      return aggregated_trades_data
  
file_path: mindsdb/mindsdb/integrations/handlers/binance_handler/binance_tables.py
imports: from mindsdb.integrations.libs.api_handler import APITable
  from mindsdb.integrations.libs.response import HandlerResponse
  from mindsdb.integrations.utilities.date_utils import interval_str_to_duration_ms, utc_date_str_to_timestamp_ms
  from mindsdb.integrations.utilities.sql_utils import extract_comparison_conditions
  from mindsdb_sql.parser import ast
  from concurrent.futures import ThreadPoolExecutor
  from typing import Dict, List
  import pandas
  import time
label_desc: Use image node_id 3 for calling the BinanceAggregatedTradesTable obj's underlying member method code with example usage: obj.select(query) and returns: aggregated_trades_data