[{"_1":2,"_162":-5,"_163":-5},"loaderData",{"_3":4,"_18":19,"_25":26,"_141":142},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7,"_13":14,"_15":16,"_17":11},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","theme","system","contrast","normal","dockerRebrandTour","routes/_layout",{"_20":-5,"_21":22,"_23":11,"_24":22},"username","isAskGordonEnabled",true,"showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_27":28,"_29":-5,"_30":11,"_31":11,"_32":33,"_34":35,"_36":-5,"_37":11,"_38":39,"_88":-5,"_89":90,"_129":-5,"_130":11,"_131":22,"_132":11,"_133":11,"_134":-5,"_135":11,"_136":-5,"_137":138,"_139":91,"_140":-7},"canonicalUrl","https://hub.docker.com/r/tepmorysandbox/python-to-sql/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","tepmorysandbox","reponame","python-to-sql","cloudBuildSource","isAutobuildsAvailable","repo",{"_40":41,"_42":43,"_45":46,"_47":48,"_49":11,"_50":51,"_52":53,"_54":55,"_57":35,"_32":33,"_58":59,"_60":44,"_61":62,"_63":64,"_65":66,"_67":-5,"_68":62,"_69":70,"_71":11,"_72":33,"_73":74,"_79":11,"_80":81,"_85":33,"_86":87},"categories",[],"content_types",[44],"image","date_registered","2017-12-11T13:23:14.274891Z","description","Image for connecting MS SQL Azure database and a Python data environment (pandas, sqlalchemy). ","is_private","last_updated","2017-12-11T13:23:14.742884Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[56],"application/vnd.docker.container.image.v1+json","name","pull_count",373,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","\nThis docker image, built on top of microsoft/mssql-server-linux, allows to push and pull data from a Microsoft MS SQL database.\n\nThe key advantage of using microsoft/mssql-server-linux, as image source, is to start with an official driver (ODBC Driver 13 for SQL Server) already configured. Espeacially, driver installation and configuration can be bothersome (matching version of linux OS, MS SQL Driver and Python data manipulation libraries,...).\n\nDialoguing with an Azure MS SQL Database using :\n- sqlalchemy ORM : an entire pandas DataFrames can be pushed into a MS-SQL Database hosted in Azure: ```\ndf.sql_to(,)```.\n\n\n- pydobc driver : pyodbc allows to transmit queries from Python to MS SQL by : ```cursor.execute(\"SELECT * FROM ;\")```.\n\n\nNote: we assume that a MS SQL Azure database server is already set-up and configured.\n\n## General instruction\n\n#### 1. MS SQL setup\n\nAllow host's IP address to connect (write and read) on the existing MS SQL database\n\n#### 2. Daloguing via SQL queries\n```\nimport pyodbc\n\nserver = ''\ndatabase = ''\nusername = ''\npassword = ''\ndriver= '{ODBC Driver 13 for SQL Server}'\n\ncnxnpyodbc.connect('DRIVER='+driver+';PORT=1433;SERVER='+server+';PORT=1443;DATABASE='+database+';UID='+username+';PWD='+ password)\n\n\ncursor = cnxn.cursor()\ncursor.execute(\"CREATE TABLE PIVOT1 (id int,customer varchar(255))\")\ncursor.execute(\"INSERT INTO PIVOT1 VALUES (1,'Bob'),(2,'Alice'),(3,'Vitalik'),(4,'Satoshi')\")\ncnxn.commit()\n\ncursor.execute(\"SELECT * FROM PIVOT1;\")\nrow = cursor.fetchone()\nwhile row:\nprint (str(row[0]) + \" \" + str(row[1]))\nrow = cursor.fetchone()\n```\n#### 3. Pushing Pandas DataFrame\n```\nimport pyodbc\nimport pandas as pd;\nimport sqlalchemy;\nfrom sqlalchemy import create_engine\n\nengine = create_engine('mssql+pyodbc://:@:1433/?driver=ODBC+Driver+13+for+SQL+Server')\ndat=pd.DataFrame({'id':[1,2,3,4],'contract':[\"Bob\",\"Alice\",\"Vitalik\",\"Satoshi\"]})\ndat.to_sql(\"PIVOT2\",con=engine)\n```\n\n\n\n## QuickStart\n\nNote: we assume that python scripts will be located on the following directory : ./scripts\nThe container contains already a python boilerplate as example.\n\n### run from this image\n```\nmkdir /scripts\ndocker run -t -d --name mycontainer -v $(pwd)/scripts:/scripts python-to-sql bash\ndocker exec -it mycontainer /bin/bash\n```\n### Built from dockerfile\n```\nsudo docker image build -t pythonsql .\n```\nNote: DockerFile require to host a python file named code.py. 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tepmorysandbox/python-to-sql

Tags for tepmorysandbox/python-to-sql