docstr: Select data from the comments table and return it as a pandas DataFrame.
  Args:
      query (ast.Select): The SQL query to be executed.
  Returns:
      pandas.DataFrame: A pandas DataFrame containing the selected data.
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code: def select(self, query: ast.Select) -> pd.DataFrame:
      """Select data from the comments table and return it as a pandas DataFrame.
      Args:
          query (ast.Select): The SQL query to be executed.
      Returns:
          pandas.DataFrame: A pandas DataFrame containing the selected data.
      """
      hn_handler = self.handler
  
      # Get the limit value from the SQL query, if it exists
      limit = None
      if query.limit is not None:
          limit = query.limit.value
  
      # Get the item ID from the SQL query
      item_id = None
      conditions = extract_comparison_conditions(query.where)
      for condition in conditions:
          if condition[0] == "=" and condition[1] == "item_id":
              item_id = condition[2]
  
      if item_id is None:
          raise ValueError("Item ID is missing in the SQL query")
  
      # Call the Hacker News API to get the comments for the specified item
      comments_df = hn_handler.call_hackernews_api(
          "get_comments", params={"item_id": item_id}
      )
  
      # Fill NaN values with 'deleted'
      comments_df = comments_df.fillna("deleted")
      # Filter the columns to those specified in the SQL query
      self.filter_columns(comments_df, query)
  
      # Limit the number of results if necessary
      if limit is not None:
          comments_df = comments_df.head(limit)
  
      return comments_df
  
imports: import pandas
  from mindsdb.integrations.libs.api_handler import APITable
  from mindsdb_sql.parser import ast
  from mindsdb.integrations.utilities.sql_utils import extract_comparison_conditions
  from typing import List, Tuple
  import requests
label_desc: Use image node_id 1 for calling the CommentsTable obj's underlying member method code with example usage: obj.select(query) and returns: comments_df