<jupyter_start><jupyter_text>Dataset - US<jupyter_code>import pandas as pd
srcUS = "../time_series_covid19_confirmed_US.csv"
dest = "TexasData.csv"
tct = 'Texas_Counties.csv'

# Read data
usDf = pd.read_csv(srcUS)
tcounties = pd.read_csv(tct)
tcounties = tcounties['County']
tcounties = [c.split(' County')[0] for c in tcounties]

texasDf = usDf[usDf['Province_State'] == 'Texas']

outdf = {}
dates = texasDf.columns.values.tolist()[13:]
outdf['Date'] = dates

for county in tcounties:
    row = texasDf[texasDf['Admin2'] == county].values.tolist()[0]
    data = row[13:]
    outdf[county] = data

outdf = pd.DataFrame(data = outdf)
print(outdf.head(10))
# Save transformed dataset
outdf.to_csv(dest, index = False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15704697986577182, 'fraction_numerical': 0.01476510067114094, 'mean_word_length': 5.601769911504425, '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': '54015', 'license': 'MIT', 'n_tokens_mistral': 314, 'n_tokens_neox': 301, 'n_words': 55, 'path': 'data/Texas/time_series_covid19_confirmed_Texas_transformation.ipynb', 'repo_name': 'rmorse7/COMP554-HopeSimpson-Forecasting', 'size': '745'}