<jupyter_start><jupyter_text>Video game dataset  <NAME> 3/11/2019<jupyter_code>import numpy as np
import pandas as pd
pd
vg_stats =pd.read_csv('vgsales.csv')
vg_stats

vg_stats['Publisher'].mode()
vg_stats['Platform'].mode()
vg_stats['Genre'].mode()
vg_stats[:20]
vg_stats['NA_Sales'].median()
vg_stats.loc[vg_stats['NA_Sales'] == 0.08][:10]
standard_dev = vg_stats['NA_Sales'].std()
standard_dev
high_NA = vg_stats['NA_Sales'].max()
high_NA
high_NA/ standard_dev
nintendo_stats = vg_stats.loc[vg_stats['Platform'] == 'Wii']
nintendo_stats
nintendo_mean= nintendo_stats['Global_Sales'].mean()
nintendo_mean
not_nintendo = vg_stats.loc[vg_stats['Platform'] != 'Wii']
not_nintendo
not_nintendo['Global_Sales'].mean()
nintendo_mean<jupyter_output><empty_output><jupyter_text>Worst NA sales<jupyter_code>vg_stats['NA_Sales'][:10:-1]<jupyter_output><empty_output><jupyter_text>highest JP sale<jupyter_code>vg_stats['JP_Sales'].max()<jupyter_output><empty_output><jupyter_text>mean JP sales<jupyter_code>vg_stats['JP_Sales'].mean()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15924170616113745, 'fraction_numerical': 0.016113744075829384, 'mean_word_length': 12.367088607594937, 'pattern_counts': {'":': 0, '<': 18, '<?xml version=': 0, '>': 18, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '139355', 'license': 'MIT', 'n_tokens_mistral': 462, 'n_tokens_neox': 418, 'n_words': 50, 'path': 'vg-stats.ipynb', 'repo_name': 'danhuyle508/data_analysis', 'size': '1055'}