text: import gzip
  import pandas as pd
  from pyarrow import csv
  import pyarrow.compute as pc
  
  vdata_pd = pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1")
  columns = list(vdata_pd.columns)
  vdata_pd.info(memory_usage="deep")
  
  vdata_arrow = csv.read_csv("2021VAERSDATA.csv.gz")
  tot_bytes = sum([
      vdata_arrow[name].nbytes
      for name in vdata_arrow.column_names])
  print(f"Total {tot_bytes // (1024 ** 2)} MB")
  
  for name in vdata_arrow.column_names:
      arr_bytes = vdata_arrow[name].nbytes
      arr_type = vdata_arrow[name].type
      pd_bytes = vdata_pd[name].memory_usage(index=False, deep=True)
      pd_type = vdata_pd[name].dtype
      print(
          name,
          arr_type, arr_bytes // (1024 ** 2),
          pd_type, pd_bytes // (1024 ** 2),)
  
  
  # %timeit pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1")
  # %timeit csv.read_csv("2021VAERSDATA.csv.gz")
  
  
  # REMOVE SYMPTOM_TEXT
  
  
  vdata_pd = pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1", usecols=lambda x: x != "SYMPTOM_TEXT")
  data_pd.info(memory_usage="deep")
  
  #columns.remove("SYMPTOM_TEXT")
  vdata_arrow = csv.read_csv(
      "2021VAERSDATA.csv.gz",
       convert_options=csv.ConvertOptions(include_columns=columns))
  vdata_arrow.nbytes
  
  # %timeit pd.read_csv("2021VAERSDATA.csv.gz", encoding="iso-8859-1", usecols=lambda x: x != "SYMPTOM_TEXT")
  # %timeit csv.read_csv("2021VAERSDATA.csv.gz", convert_options=csv.ConvertOptions(include_columns=columns))
  
  vdata = vdata_arrow.to_pandas()
  vdata.info(memory_usage="deep")
  
  
  
  # Theres more
  vdata = vdata_arrow.to_pandas(self_destruct=True)
  
  