<jupyter_start><jupyter_text>Include scatter plots without selective axes ranges<jupyter_code>#Recommendations by Metacritic (Y,X)
c = plt.scatter(DF['Metacritic'],DF['RecommendationCount'], color=sea.color_palette()[0])
plt.xlabel('Metacritic Score')
plt.ylabel('Recommendation Count')
plt.savefig("../report/metacritic-recommendations-scatter-all.png", bbox_inches='tight');
#Recommendations by Price (Y,X)
c = plt.scatter(DF['PriceInitial'],DF['RecommendationCount'], color=sea.color_palette()[0])
plt.xlabel("Initial Price in USD")
plt.ylabel("Number of Recommendations")
plt.savefig("../report/price-recommendations-scatter-all.png", bbox_inches='tight');
#Metacritic score by Price (Y,X)
c = plt.scatter(DF['PriceInitial'],DF['Metacritic'], color=sea.color_palette()[0])
plt.xlabel("Initial Price in USD")
plt.ylabel("Mean Metacritic Score")
plt.savefig("../report/price-metacritic-scatter-all.png", bbox_inches='tight');<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17345872518286312, 'fraction_numerical': 0.003134796238244514, 'mean_word_length': 9.191489361702128, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '426668', 'license': 'MIT', 'n_tokens_mistral': 366, 'n_tokens_neox': 335, 'n_words': 52, 'path': 'analysis/analysis-elh.ipynb', 'repo_name': 'CraigKelly/steam-data', 'size': '957'}