input: Write a python function to preprocess a DataFrame by converting specified columns to categorical, one-hot encoding them, and converting an ID column to a string. Use the following assert statements as test cases:
  ```python
  import pandas as pd
  from io import StringIO
  
  def preprocess_df(df):
      df.dropna(inplace=True)
      df['Gender'] = df.Gender.astype("category")
      df['Age'] = df.Age.astype("category")
      df['State'] = df.State.astype("category")
      df['Country'] = df.Country.astype("category")
      df['Hobby'] = df.Hobby.astype("category")
      df = pd.get_dummies(df, columns=['Gender', 'Age', 'State', 'Country', 'Hobby'])
      df['ID'] = df.ID.astype('str')
      return df
  
  # Test cases
  data = StringIO('''
  ID,Gender,Age,State,Country,Hobby
  1,Male,25,California,USA,Reading
  2,Female,30,Texas,USA,Sports
  3,,35,California,USA,Music
  4,Male,40,Texas,USA,Reading
  ''')
  df = pd.read_csv(data)
  df_cleaned = preprocess_df(df)
  assert 'Gender_Male' in df_cleaned.columns
  assert 'Age_25' in df_cleaned.columns
  assert df_cleaned['ID'].dtype == 'object'
output: def preprocess_df(df):
      df.dropna(inplace=True)
      df['Gender'] = df.Gender.astype("category")
      df['Age'] = df.Age.astype("category")
      df['State'] = df.State.astype("category")
      df['Country'] = df.Country.astype("category")
      df['Hobby'] = df.Hobby.astype("category")
      df = pd.get_dummies(df, columns=['Gender', 'Age', 'State', 'Country', 'Hobby'])
      df['ID'] = df.ID.astype('str')
      return df
  
  import pandas as pd
  from io import StringIO
  
  # Test cases
  data = StringIO('''
  ID,Gender,Age,State,Country,Hobby
  1,Male,25,California,USA,Reading
  2,Female,30,Texas,USA,Sports
  3,,35,California,USA,Music
  4,Male,40,Texas,USA,Reading
  ''')
  df = pd.read_csv(data)
  df_cleaned = preprocess_df(df)
  assert 'Gender_Male' in df_cleaned.columns
  assert 'Age_25' in df_cleaned.columns
  assert df_cleaned['ID'].dtype == 'object'