code: def default_preprocessing(df):
      """
      1.Create a new column, RACE that is 'White' if RACEV2X = 1 and HISPANX = 2 i.e. non Hispanic White
        and 'non-White' otherwise
      2. Restrict to Panel 20
      3. RENAME all columns that are PANEL/ROUND SPECIFIC
      4. Drop rows based on certain values of individual features that correspond to missing/unknown - generally < -1
      5. Compute UTILIZATION, binarize it to 0 (< 10) and 1 (>= 10)
      """
  
      def race(row):
          if (row["HISPANX"] == 2) and (
              row["RACEV2X"] == 1
          ):  # non-Hispanic Whites are marked as WHITE; all others as NON-WHITE
              return "White"
          return "Non-White"
  
      df["RACEV2X"] = df.apply(lambda row: race(row), axis=1)
      df = df.rename(columns={"RACEV2X": "RACE"})
  
      df = df[df["PANEL"] == 20]
  
      # RENAME COLUMNS
      df = df.rename(
          columns={
              "FTSTU53X": "FTSTU",
              "ACTDTY53": "ACTDTY",
              "HONRDC53": "HONRDC",
              "RTHLTH53": "RTHLTH",
              "MNHLTH53": "MNHLTH",
              "CHBRON53": "CHBRON",
              "JTPAIN53": "JTPAIN",
              "PREGNT53": "PREGNT",
              "WLKLIM53": "WLKLIM",
              "ACTLIM53": "ACTLIM",
              "SOCLIM53": "SOCLIM",
              "COGLIM53": "COGLIM",
              "EMPST53": "EMPST",
              "REGION53": "REGION",
              "MARRY53X": "MARRY",
              "AGE53X": "AGE",
              "POVCAT15": "POVCAT",
              "INSCOV15": "INSCOV",
          }
      )
  
      df = df[df["REGION"] >= 0]  # remove values -1
      df = df[df["AGE"] >= 0]  # remove values -1
  
      df = df[df["MARRY"] >= 0]  # remove values -1, -7, -8, -9
  
      df = df[df["ASTHDX"] >= 0]  # remove values -1, -7, -8, -9
  
      df = df[
          (
              df[
                  [
                      "FTSTU",
                      "ACTDTY",
                      "HONRDC",
                      "RTHLTH",
                      "MNHLTH",
                      "HIBPDX",
                      "CHDDX",
                      "ANGIDX",
                      "EDUCYR",
                      "HIDEG",
                      "MIDX",
                      "OHRTDX",
                      "STRKDX",
                      "EMPHDX",
                      "CHBRON",
                      "CHOLDX",
                      "CANCERDX",
                      "DIABDX",
                      "JTPAIN",
                      "ARTHDX",
                      "ARTHTYPE",
                      "ASTHDX",
                      "ADHDADDX",
                      "PREGNT",
                      "WLKLIM",
                      "ACTLIM",
                      "SOCLIM",
                      "COGLIM",
                      "DFHEAR42",
                      "DFSEE42",
                      "ADSMOK42",
                      "PHQ242",
                      "EMPST",
                      "POVCAT",
                      "INSCOV",
                  ]
              ]
              >= -1
          ).all(1)
      ]  # for all other categorical features, remove values < -1
  
      def utilization(row):
          return (
              row["OBTOTV15"]
              + row["OPTOTV15"]
              + row["ERTOT15"]
              + row["IPNGTD15"]
              + row["HHTOTD15"]
          )
  
      df["TOTEXP15"] = df.apply(lambda row: utilization(row), axis=1)
      lessE = df["TOTEXP15"] < 10.0
      df.loc[lessE, "TOTEXP15"] = 0.0
      moreE = df["TOTEXP15"] >= 10.0
      df.loc[moreE, "TOTEXP15"] = 1.0
  
      df = df.rename(columns={"TOTEXP15": "UTILIZATION"})
      return df
  
imports: import os
  import pandas
  from aif360.datasets import StandardDataset
label_desc: Use image node_id 1 for calling a global function with example usage: default_preprocessing(df) and returns: df, str, unknown, str