<jupyter_start><jupyter_text>Metered Loads: Data Visualization<jupyter_code>import os
import pandas as pd
%matplotlib inline
df = pd.read_csv(os.path.join(os.pardir, 'formatted_data', 'hourly_loads.csv'), parse_dates=['DATE_UTC'],
                index_col=['DATE_UTC'])
df.head()
df.tail()
df.plot();<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1510574018126888, 'fraction_numerical': 0.0, 'mean_word_length': 6.377777777777778, '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': '507607', 'license': 'Apache-2.0', 'n_tokens_mistral': 130, 'n_tokens_neox': 126, 'n_words': 20, 'path': 'data/metered_loads/notebooks/MeteredLoadsVisualization.ipynb', 'repo_name': 'priyald17/emissions-assumptions', 'size': '331'}