{"id":39476,"date":"2020-03-25T11:42:06","date_gmt":"2020-03-25T15:42:06","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=39476"},"modified":"2022-11-21T09:45:14","modified_gmt":"2022-11-21T14:45:14","slug":"reading-from-databases-with-python","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/reading-from-databases-with-python\/","title":{"rendered":"Reading from databases with Python"},"content":{"rendered":"\n

Background \u2013 Reading from Databases with Python<\/strong><\/p>\n\n\n\n

This post will talk about several packages for working with databases using Python<\/a>. We\u2019ll start by covering pyodbc<\/strong>, which is one of the more standard packages used for working with databases, but we\u2019ll also cover a very useful module called turbodbc<\/strong>, which allows you to run SQL queries efficiently (and generally faster) within Python.<\/p>\n\n\n\n

pyodbc<\/strong><\/p>\n\n\n\n

pyodbc<\/strong> can be installed using pip:<\/p>\n\n\n\n

pip install pyodbc\n<\/p>\n\n\n\n

Let\u2019s start by writing a simple SQL query using pyodbc<\/strong>. To do that, we first need to connect to a specific database. In the examples laid out here, we will be using a SQLite database on my machine. However, you can do this with many other database systems, as well, such as SQL Server, MySQL, Oracle, etc. In the connection string, we specify the database driver (e.g. one of the database systems mentioned), the server, database name, and potentially username \/ password. If you have a trusted connection setup, then you can specify that (like in the first example below).<\/p>\n\n\n\n

\n\nimport pyodbc

\n \nchannel = pyodbc.connect(“DRIVER={SQLite3 ODBC Driver};SERVER=localhost;DATABASE=sample_database.db;Trusted_connection=yes”)

\n \n# or if using a password

\n \nchannel = pyodbc.connect(“DRIVER={SQLite3 ODBC Driver};SERVER=localhost;DATABASE=sample_database.db;Uid=YourUsername;Pwd=YourPassword;'”)

\n<\/p>\n\n\n\n

Reading SQL query with pandas<\/strong><\/p>\n\n\n\n

After we\u2019ve made the connection, we can write a SQL query to retrieve data from this database. One way of doing that is using the pandas<\/strong> package. Below, we wrap the SQL code inside quotes as the first parameter of pd.read_sql<\/em>. The second parameter contains our connection object.<\/p>\n\n\n\n

\nimport pandas as pd

\n \npd.read_sql(“select * from sample_table;”, channel)\n<\/p>\n\n\n\n

Reading SQL query with pyodbc<\/strong><\/p>\n\n\n\n

Besides using pandas<\/strong>, we can execute a SQL query with pyodbc<\/strong> alone. In this case, we need to create a cursor<\/em> object. A cursor<\/em> is an object used to process the results of a SQL query.<\/p>\n\n\n\n

\ncursor = channel.cursor()

\n \ncursor.execute(“select * from sample_table;”)\n \n\n<\/p>\n\n\n\n

Next, we can extract the results of the query by using the fetchall<\/em> method. fetchall<\/em> returns the rows of the query result into a list, rather than a data frame. Each element in the list corresponds to a row.<\/p>\n\n\n\n

\nrows = cursor.fetchall()\n \n\n<\/p>\n\n\n\n

Visit TheAutomatic.net to download ready-to-use code, and read the rest of the article: https:\/\/theautomatic.net\/2020\/03\/12\/reading-from-databases-with-python\/<\/a><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Join Andrew Treadway as he demonstrates how to use various Python packages to retrieve data from databases.<\/p>\n","protected":false},"author":388,"featured_media":99378,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,341,352,344],"tags":[6956,806,4922,6953,6954,1224,6950,6955,595,494,6952,6951],"contributors-categories":[13695],"class_list":{"0":"post-39476","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-development","12":"category-quant-north-america","13":"category-quant-regions","14":"tag-data-analysis","15":"tag-data-science","16":"tag-econometrics","17":"tag-mysql","18":"tag-oracle","19":"tag-pandas","20":"tag-pyodbc","21":"tag-pypyodbc","22":"tag-python","23":"tag-quant","24":"tag-sql-server","25":"tag-turbodbc","26":"contributors-categories-theautomatic-net"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nReading 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