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This is a continuation of the data analysis project using L.A. city's data 'Great Streets Pedestrian Survey 2015'. |
- sqlalchemy - Python SQL toolkit and Object Relational Mapper
- pyodbc - Module for accessing ODBC databases to be the bridge between SQL and Python
- pymssql - Module for creating Tables.
- MS SQL Server - localhost SQL server
- Azure SQL Server - Windows Cloud server
- MySQL - localhost SQL server
- pip install pymssql
- pip install pyodbc
- pip install SQLAlchemy
- download data https://data.lacity.org/Community-Economic-Development/Great-Streets-Pedestrian-Survey-2015/6yv9-t9gq/data
- Install the dependencies and setup retrieve all the database config before starting.
import pyodbc
server = 'db.database.windows.net'
database = ''
username = ''
password = ''
driver= '{ODBC Driver 17 for SQL Server}'
server = 'localhost:1433'
database = ''
username = ''
password = ''
driver= '{ODBC Driver 17 for SQL Server}'
from sqlalchemy import create_engine
import pymssql
engine = create_engine("mssql+pymssql://USERNAME:PASSWORD@SERVER:PORT/DB")
engine_azure = create_engine("mssql+pymssql://USERNAME:[email protected]:PORT/DB")
from sqlalchemy import create_engine
engine = create_engine("mysql+mysqldb://USERNAME:PASSWORD@localhost/DB")
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The original setup of this repository is by Yosep Kim.
For a full list of all authors and contributors, see the contributors page.
SQL_Great_Streets_Survey_ykimdeveloper follows good practices of security, but 100% security cannot be assured. SQL_Great_Streets_Survey_ykimdeveloper is provided "as is" without any warranty. Use at your own risk.
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This project is licensed under the MIT license.
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