<jupyter_start><jupyter_text>df['price_value'].describe() df['param_marka-pojazdu'].unique()<jupyter_code><jupyter_output><empty_output><jupyter_text><jupyter_code>def group_and_barplot(feat_groupby, feat_agg='price_value',agg_funcs=[np.mean, np.median, np.size], feat_sort='mean', top=50, subplots=True):
  return(
      df
      .groupby(feat_groupby)[feat_agg]
      .agg(agg_funcs)
      .sort_values(by=feat_sort, ascending=False)
      .head(top)
  ).plot(kind='bar', figsize=(15,5), subplots=subplots)
group_and_barplot('param_marka-pojazdu');
group_and_barplot('param_kraj-pochodzenia',feat_sort='size');
group_and_barplot('param_kolor',feat_sort='mean');<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1645021645021645, 'fraction_numerical': 0.007215007215007215, 'mean_word_length': 9.057971014492754, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '200574', 'license': 'MIT', 'n_tokens_mistral': 282, 'n_tokens_neox': 275, 'n_words': 22, 'path': 'day2_visualisation.ipynb', 'repo_name': 'mazi1444/dw_matrix_car', 'size': '693'}