def create_dataset(n_rows=<NUM_LIT:1000>, extras=False, has_none=True):
random.seed(<NUM_LIT>)<EOL>ds = [[<EOL>random.random(),                                                                        <EOL>random.choice([float('<STR_LIT>'), float('<STR_LIT>'), float('<STR_LIT>'), -<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:1>, -<NUM_LIT:1>, math.pi]),      <EOL>row,                                                                                    <EOL>str(row),                                                                               <EOL>random.choice(['<STR_LIT:A>', '<STR_LIT:B>']) if extras else '<STR_LIT:A>',                                           <EOL>random.choice(['<STR_LIT:A>', '<STR_LIT>', '<STR_LIT>']),                                                   <EOL>random.choice(['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>', None]) if has_none else random.choice(['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>']),   <EOL>random.choice(['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>', '<STR_LIT:D>']) if extras else random.choice(['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>']),      <EOL>random.choice([<NUM_LIT:12>, <NUM_LIT>, -<NUM_LIT:32>]),                                                           <EOL>random.choice(['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>']),                                                         <EOL>random.choice(['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>', np.nan])                                                  <EOL>] for row in range(n_rows)]<EOL>df = pd.DataFrame(ds, columns=['<STR_LIT:float>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT>', '<STR_LIT:none>', '<STR_LIT>', <NUM_LIT>, '<STR_LIT>', '<STR_LIT>'])<EOL>df['<STR_LIT>'] = pd.Categorical(df['<STR_LIT>'], categories=['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>'])<EOL>df['<STR_LIT>'] = pd.Categorical(df['<STR_LIT>'], categories=['<STR_LIT:A>', '<STR_LIT:B>', '<STR_LIT:C>'])<EOL>return df<EOL>
Creates a dataset with some categorical variables.