docstr: Sample a column.
  
  Args:
      col (str): Column name.
      n (int): Number of rows to sample.
  
  Returns:
      list: Sampled column.
code: def _sample_column(self, col: str, n: int) -> list:
      """Sample a column.
  
      Args:
          col (str): Column name.
          n (int): Number of rows to sample.
  
      Returns:
          list: Sampled column.
      """
  
      col_sample = []
      col_values = self.df[col].dropna().unique()
  
      # if there is a null value in the column, it MUST be included in the sample
      if self.df[col].isna().any():
          col_sample.append(np.nan)
          n -= 1
  
      # if the column has less than n unique values, then a random sample of the
      # unique values should be returned
      if len(col_values) <= n:
          col_sample.extend(col_values)
          n -= len(col_values)
      else:
          col_sample.extend(random.sample(list(col_values), n))
          n = 0
  
      # if there are still rows to sample, sample them randomly
      if n > 0 and len(col_values) > 0:
          col_sample.extend(random.choices(list(col_values), k=n))
      else:
          col_sample.extend([np.nan] * n)
  
      # shuffle the column sample before returning it
      random.shuffle(col_sample)
      return col_sample
  
file_path: pandas-ai/pandasai/helpers/data_sampler.py
imports: import random
  import pandas
  import numpy
  from .df_info import df_type, DataFrameType
  from .anonymizer import Anonymizer
label_desc: Use image node_id 3 for calling the DataSampler obj's underlying member method code with example usage: obj._sample_column(col, n) and returns: col_sample