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# !pip install pandas_profiling

import pandas_profiling as pp

# +
from sqlalchemy import create_engine

from IPython.display import display_html

import seaborn as sns
import matplotlib.pyplot as plt

# %matplotlib inline

# -

import warnings
import pandas as pd
import numpy as np


# +
postgres_user = 'dsbc_student'
postgres_pw = '<PASSWORD>'
postgres_host = '172.16.58.3'
postgres_port = '5432'
postgres_db = 'houseprices'

engine = create_engine('postgresql://{}:{}@{}:{}/{}'.format(
    postgres_user, postgres_pw, postgres_host, postgres_port, postgres_db))
df = pd.read_sql_query('select * from houseprices',con=engine)

# No need for an open connection, because only doing a single query
engine.dispose()


df.head(10)
# -

pp.ProfileReport(df)



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