docs: def run(verbosity=1):
      """Load all_tweets.csv and run normalize, dropna, encode before dumping to cleaned_tweets.csv.gz
  
      Many columns have "mixed type" to `read_csv` should set `low_memory` to False to ensure they're loaded accurately
      >>> df2 = pd.read_csv(os.path.join(DATA_PATH, 'cleaned_tweets.csv.gz'), index_col='id', compression='gzip', quotechar='"', quoting=pd.io.common.csv.QUOTE_NONNUMERIC, low_memory=False)
      /home/hobs/.virtualenvs/twip/local/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723:
      DtypeWarning: Columns (74,82,84,105,114,115,116,117,118,119,120,121,122,123,125,126,127,128,129,130,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,149,150,151,152,199,200,202,203,210,211,214,215,223,231,232,238) have mixed types. Specify dtype option on import or set low_memory=False.
         interactivity=interactivity, compiler=compiler, result=result)
      """
      filepath = os.path.join(DATA_PATH, 'all_tweets.csv')
      # this should load 100k tweets in about a minute
      # check the file size and estimate load time from that (see scritps/cat_tweets.py)
      print('Loading tweets from {} (could take a minute or so)...'.format(filepath))
      df = pd.read_csv(filepath, encoding='utf-8', engine='python')
      if 'id' in df.columns:
          df = df.set_index('id')
      df = normalize(df)
      df = dropna(df)
      df = encode(df, verbosity=verbosity)
      df = clean_labels(df)
      df.to_csv(os.path.join(DATA_PATH, 'cleaned_tweets.csv.gz'), compression='gzip',
                quotechar='"', quoting=pd.io.common.csv.QUOTE_NONNUMERIC)
      # the round-trip to disk cleans up encoding issues so encoding no longer needs to be specified on load
      df = pd.read_csv(os.path.join(DATA_PATH, 'cleaned_tweets.csv.gz'), index_col='id', compression='gzip',
                       quotechar='"', quoting=pd.io.common.csv.QUOTE_NONNUMERIC, low_memory=False)
      df.to_csv(os.path.join(DATA_PATH, 'cleaned_tweets.csv.gz'), compression='gzip',
                quotechar='"', quoting=pd.io.common.csv.QUOTE_NONNUMERIC)
      return df