<jupyter_start><jupyter_text><jupyter_code>import sqlite3
import pandas as pd
import io
from google.colab import files
uploaded = files.upload()
top10 = pd.read_csv(io.BytesIO(uploaded['Top 10 Products.csv']))
top10
from google.colab import files
uploaded = files.upload()
Avg_Age = pd.read_csv(io.BytesIO(uploaded['Avg_Age.csv']))
Largest_Category = pd.read_csv(io.BytesIO(uploaded['Largest_Category.csv']))
Top10_and_Suppliers = pd.read_csv(io.BytesIO(uploaded['Top10_and_Suppliers.csv']))
print(Avg_Age,'\n')
print('\n', Largest_Category,'\n')
print('\n',Top10_and_Suppliers)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14802631578947367, 'fraction_numerical': 0.02138157894736842, 'mean_word_length': 8.98360655737705, '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': '649401', 'license': 'MIT', 'n_tokens_mistral': 244, 'n_tokens_neox': 232, 'n_words': 35, 'path': 'Sprint Challenge/Query_Outputs.ipynb', 'repo_name': 'hughjafro/DS-Unit-3-Sprint-2-SQL-and-Databases', 'size': '608'}