<jupyter_start><jupyter_text>Investigating Correlated Columns(Written by <NAME>, sent to <NAME> 11/4/2021)<jupyter_code>import numpy as np
import pandas as pd
import psycopg2
from psycopg2.extras import execute_values
import matplotlib.pyplot as plt

try:
        conn = psycopg2.connect(host='ec2-52-201-66-148.compute-1.amazonaws.com', database='d44ns4ruujn4nq', port=5432,
                                user='ub5debmb55aodh', password='<PASSWORD>')
        print("successfully connected to database")
except:
        print("I am unable to connect to the database")
# cursor
cur = conn.cursor()

get_cols_query = "SELECT * from rawparceltable;"
sample_data = [];
try: 
    cur.execute(get_cols_query)
    sample_data = cur.fetchall()
    
    print("query successful")
except:
    print("query failed")

    
print(sample_data[0:10])
df = pd.DataFrame(sample_data, columns=['nettaxablevalue','situszip', 
        'rollyear',
        'usecodedescchar1',  
        'sqftmain',
        'roll_landvalue',
        'roll_landbaseyear',  
        'istaxableparcel',
        'center_lat',
        'center_lon'])
df
df['nettaxablevalue'].mean()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12147134302822926, 'fraction_numerical': 0.03165098374679213, 'mean_word_length': 3.5173745173745172, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '650352', 'license': 'MIT', 'n_tokens_mistral': 418, 'n_tokens_neox': 392, 'n_words': 77, 'path': 'data/kNN/KNN 4 Mariesa.ipynb', 'repo_name': 'kripeshr22/MappingDemo', 'size': '1239'}