<jupyter_start><jupyter_text>PostgreSQL with Python and psycopg2<jupyter_code>import psycopg2 as pg2
conn = pg2.connect(database='newdvddatabase', user='postgres', password=" ")
cur = conn.cursor()
cur.execute('SELECT * FROM payment')
cur.fetchone()
cur.fetchmany(5)
type(cur.fetchmany(5))
data = cur.fetchmany(10)
data[0]
conn.close()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1506849315068493, 'fraction_numerical': 0.024657534246575342, 'mean_word_length': 8.891891891891891, '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': '660529', 'license': 'MIT', 'n_tokens_mistral': 140, 'n_tokens_neox': 138, 'n_words': 24, 'path': 'Data_Analysis_With_SQL_and_PostGreSQL/10-PostGreSQL-with-Python/PostgreSQL with Python.ipynb', 'repo_name': 'ashishpatel26/Machine-Learning-and-Quantitative-analysis', 'size': '365'}