<jupyter_start><jupyter_text>State Populations Table of Contents1&nbsp;&nbsp;Set-up2&nbsp;&nbsp;Capture  Set-up<jupyter_code>import numpy as np
import pandas as pd
files = {
    'NSW': '3101051.xls',
    'Victoria': '3101052.xls',
    'Queensland': '3101053.xls',
    'SA': '3101054.xls',
    'WA': '3101055.xls',
    'Tasmania': '3101056.xls',
    'NT': '3101057.xls',
    'ACT': '3101058.xls',
}<jupyter_output><empty_output><jupyter_text>Capture<jupyter_code>age_and_up = 0 # 0 or 16

totals = {}
for state, filename in files.items():
    xl = pd.ExcelFile(filename)
    data_sheets = [x for x in xl.sheet_names if 'Data' in x]
    frame = [xl.parse(name, index_col=0, header=0) for name in data_sheets]
    frame = pd.concat(frame, axis=1)
    frame = frame[frame.columns[frame.columns.str.contains('Persons')]]
    state_total = frame[frame.columns[age_and_up:]].iloc[-1].sum()
    totals[state] = state_total
totals<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1608832807570978, 'fraction_numerical': 0.0694006309148265, 'mean_word_length': 4.633136094674557, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '454236', 'license': 'MIT', 'n_tokens_mistral': 425, 'n_tokens_neox': 381, 'n_words': 71, 'path': 'abs/State Eligible Populations.ipynb', 'repo_name': 'bpalmer4/Covid-19', 'size': '951'}