text: import numpy as np
  from database_pg.queries import (
      get_count_apartments, get_prices_usd, get_total_squares, get_count_col, get_living_squares, get_kitchen_squares,
      get_floors, get_floor_counts, get_images_urls, get_heating_types, get_cities, get_regions, get_walls_material
  )
  
  
  def process_statistics():
      prices = np.array(get_prices_usd())
      squares = np.array(get_total_squares())
      living_squares = np.array(get_living_squares())
      kitchen_squares = np.array(get_kitchen_squares())
      floors = np.array(get_floors())
      floor_counts = np.array(get_floor_counts())
      images_counts = np.array([len(item) for item in get_images_urls()])
      heating_types = {item[0] if item[0] is not None else "missing": item[1] for item in get_heating_types()}
      walls_materials = {item[0] if item[0] is not None else "missing": item[1] for item in get_walls_material()}
      cities = {item[0] if item[0] is not None else "missing": item[1] for item in get_cities()}
      regions = {item[0] if item[0] is not None else "missing": item[1] for item in get_regions()}
      output = {
          "apartments count": get_count_apartments(),
          "params per apartment": get_count_col(),
          "USD prices": {
              "mean": int(np.mean(prices)),
              "median": int(np.median(prices)),
              "std": int(np.std(prices)),
          },
          "total squares": {
              "mean": np.mean(squares),
              "median": np.median(squares),
              "std": np.std(squares),
          },
          "living squares": {
              "mean": np.mean(living_squares),
              "median": np.median(living_squares),
              "std": np.std(living_squares),
          },
          "kitchen squares": {
              "mean": np.mean(kitchen_squares),
              "median": np.median(kitchen_squares),
              "std": np.std(kitchen_squares),
          },
          "floor of apartments": {
              "mean": np.mean(floors),
              "median": np.median(floors),
              "std": np.std(floors),
          },
          "floor counts": {
              "mean": np.mean(floor_counts),
              "median": np.median(floor_counts),
              "std": np.std(floor_counts),
          },
          "images per apartment": {
              "mean": np.mean(images_counts),
              "median": np.median(images_counts),
          },
          "heating_types:": heating_types,
          "walls_materials": walls_materials,
          "cities": cities,
          "regions": regions
      }
      return output
  