<jupyter_start><jupyter_text>3.<jupyter_code>df['unit_price'] = df['price']/df['area']
df[:5]<jupyter_output><empty_output><jupyter_text>4.1<jupyter_code>avg_price_per_year = df.groupby('built_in').mean()['unit_price']
avg_price_per_year[:]
avg_price_per_year.plot()<jupyter_output><empty_output><jupyter_text>The unit prices rose and fell steeply for a number of years (looking to be aroud the time of the Great Depression) but remanined fairly stable since the 19040s except for a bried period in the '70s.  4.2<jupyter_code>avg_price_per_year.plot.hist()<jupyter_output><empty_output><jupyter_text>Most unit prices are below $200/sqft  4.3<jupyter_code>avg_price_per_type = df.groupby('house_type').mean()['unit_price']
avg_price_per_type[:]
avg_price_per_type.plot.bar()<jupyter_output><empty_output><jupyter_text>The most expensive average unit price is the single family homes.  4.4<jupyter_code>count_per_house_type = df['house_type'].value_counts()
count_per_house_type[:]
count_per_house_type.plot.pie()<jupyter_output><empty_output><jupyter_text>Single family homes make up the majority of the different types in the data.  4.5<jupyter_code>df.plot.scatter(x = 'area', y = 'price')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12285012285012285, 'fraction_numerical': 0.018018018018018018, 'mean_word_length': 9.35593220338983, 'pattern_counts': {'":': 0, '<': 25, '<?xml version=': 0, '>': 25, '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': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '267776', 'license': 'MIT', 'n_tokens_mistral': 468, 'n_tokens_neox': 459, 'n_words': 83, 'path': 'lab13.ipynb', 'repo_name': 'uni-student234/ISAT252', 'size': '1221'}