<jupyter_start><jupyter_text>Lab timePick a data set to noodle around with. Come up with questions to ask your data and practice loading data, filtering, sorting, grouping, etc.<jupyter_code>import pandas as pd
# a table of country codes
country_codes = '../data/country-codes.csv'

# world population data
country_population = '../data/country-population.csv'

# Criterion films
criterion_films = '../data/criterion.csv'

# eel product imports
eels = '../data/eels.csv'

# FL gator hunt data
gators = '../data/gators.csv'

# fights during NHL games
hockey_fights = '../data/hockey-fights.xlsx'

# homicides in large cities, 2014
homicides = '../data/homicides2014.xlsx'

# 2017 MLB salary data
mlb = '../data/mlb.csv'

# media witnesses to TX executions
tx_death_row_media = '../data/tx-death-row-media-list.csv'

# CO weed sales reports by county, 2017 (data has some caveats)
'../data/colo-county-cannabis-sales-2017.csv'

# CO county FIPS, names, 2016 population estimates
'../data/colo-county-pop.csv'
# pick your data and create a data frame or frames
# ex.:
# df = pd.read_csv(mlb)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1261180679785331, 'fraction_numerical': 0.02146690518783542, 'mean_word_length': 4.086363636363636, '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': '395245', 'license': 'MIT', 'n_tokens_mistral': 411, 'n_tokens_neox': 372, 'n_words': 114, 'path': 'exercises/Lab time.ipynb', 'repo_name': 'cjwinchester/ire-2018-python-for-data-analysis', 'size': '1118'}