<jupyter_start><jupyter_text>Cechy<jupyter_code>df.columns.values<jupyter_output><empty_output><jupyter_text>Rozkład ceny<jupyter_code>df.price_value.hist(bins=100);<jupyter_output><empty_output><jupyter_text>Najdroższy<jupyter_code>df.price_value.max()<jupyter_output><empty_output><jupyter_text>Najtańszy<jupyter_code>df.price_value.min()<jupyter_output><empty_output><jupyter_text>Mediana - jeżeli posortujemy cały dataset po 'price_value', to dokładnie 50% elementów będzie miało wartość 'price_value' poniżej mediany<jupyter_code>df.price_value.median()<jupyter_output><empty_output><jupyter_text>Kwartyle<jupyter_code>df.price_value.describe()<jupyter_output><empty_output><jupyter_text>Wykresy<jupyter_code>def filter_for_plot(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)
filter_for_plot('param_co2-emissions');
filter_for_plot('param_rok-produkcji');<jupyter_output><empty_output>
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{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '478896', 'license': 'MIT', 'n_tokens_mistral': 500, 'n_tokens_neox': 487, 'n_words': 39, 'path': 'CarPriceVisualisation.ipynb', 'repo_name': 'tetysionc/CarPricePrediction', 'size': '1165'}