<jupyter_start><jupyter_text>Intro to Dataframes<jupyter_code>import pandas as pd
scores = {
    'name':['Ray', 'Richard', 'Vivian'],
    'city':['New York', 'Singapore', 'Yangon'],
    'score': [75, 99, 92],
}
df = pd.DataFrame(scores)
df.head()
df['name']
df['name_city'] = df['name'] + '_' + df['city']

df.head()
df[df['score'] > 90]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.23705722070844687, 'fraction_numerical': 0.021798365122615803, 'mean_word_length': 5.943396226415095, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, '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': '543132', 'license': 'MIT', 'n_tokens_mistral': 163, 'n_tokens_neox': 150, 'n_words': 28, 'path': 'Pandas - Advanced Pandas/01.Beginner to Advanced Pandas/01-02-dataframes.ipynb', 'repo_name': 'ptyadana/probability-and-statistics-for-business-and-data-science', 'size': '367'}