<jupyter_start><jupyter_text>This notebook uses [the world population data from World Bank](https://data.worldbank.org/indicator/SP.POP.TOTL)The code below loads the data from the CSV file<jupyter_code>import pandas as pd
data = pd.read_csv('data/WorldBank_world_population.csv', skiprows=3).set_index('Country Name')
data.head()
pop2018 = data['2018']<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12041884816753927, 'fraction_numerical': 0.02356020942408377, 'mean_word_length': 7.704545454545454, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '180183', 'license': 'MIT', 'n_tokens_mistral': 137, 'n_tokens_neox': 127, 'n_words': 29, 'path': '15-warmup-blank_dataframe_and_series.ipynb', 'repo_name': 'hanisaf/advanced-data-management-and-analytics', 'size': '382'}