<jupyter_start><jupyter_text>Ideas for project weekI have put together 2 datasets on topics I have some knowledge and am passionate about.The 1st is about 9 years of team stats for Soccer teams in England taken from whoscored.com covering final standings, shots, goals, attacking and deffending actions, etc.This data set is very clean but a little on the small side and while whoscored.com does a good job of aggregating stats but might not be as accurate as the sources used in the industry.The 2nd data set is the result of some recent web scrapping I did, gathering the auction results for the artist <NAME>. There are almost 6,500 results but the data is very messy and will require lots of cleaning.I'm currently working on the art auction data set and the results can be used for wark and school outside of Lambda and am leaning toward this dataset.<jupyter_code>import pandas as pd
df1 = pd.read_csv('soccer data.csv')
df1.head()
df1.describe()
df1.shape
df1.dtypes
df2 = pd.read_csv('Damien_Hirst_Auction_Results.csv')
df2.head()
df2.describe()
df2.shape
df2.dtypes<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.051630434782608696, 'fraction_numerical': 0.016304347826086956, 'mean_word_length': 5.038251366120218, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, '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': '436671', 'license': 'MIT', 'n_tokens_mistral': 323, 'n_tokens_neox': 306, 'n_words': 161, 'path': 'project_ideas_and_data.ipynb', 'repo_name': 'AndrewMarksArt/AndrewMarksArt.github.io', 'size': '1110'}