![]() ![]() This has opened the doors I didn’t know even existed. I have found marrying SQL to Python immensely useful. Many of these operations were not possible in SQL. Once you brought it as DataFrame, then all the operations are usual Pandas operations. Now you can start using Python to work upon your data which rests in SQL Databases. This is not the end, but only the first step towards getting the “Best of Both Worlds”. In this article, you saw how to connect the two most powerful workhorses of the Data Science world, SQL and Python. One is to have a look at your SQL Server Management login window. You can find the server name using two ways. Let me help you locate your Server Name and Database You would know which one you are using, from your SQL Server Management Studio.Ĭonnection_string = ("Driver= " One when the connection is trusted one, and another where you need to enter your User_id and Password. There can be two types of connection Strings. Let’s have a look at the sample connection String. The connection string can be defined and declared separately. This function needs a connection string as a parameter. ![]() We need to establish the connection with the server first, and we will use nnect function for the same. Only the connection step will vary a little. However, the same codes can be used for any other ODBC compliant database. I am presently working on MS SQL Server, and that’s what I will be using for this article as well. These DBMS (Database management Systems) are compliant with ODBC. ODBC was developed by SQL Access Group in the early ’90s as an API (Application Programming Interface) to access databases. This makes access easy to ODBC (Open Database Connectivity) databases. Pyodbc is going to be the bridge between SQL and Python. pip install pyodbcĪnd then import the library in your Jupyter notebook import pyodbc Install pyodbc using pip or visit their webpage. These together can take your code to the pinnacle of automation and efficiency. These two languages together are a formidable force in our hands. This will give us the ability to use the dynamic nature of Python to build and run queries like SQL. We are going to use the library named pyodbc to connect python to SQL. So let us begin with our journey without any further ado. Watch out this space for more such articles and leave your demand for specific topics in comments for me to write about them. Just one more thing: This is my first attempt to marry SQL and Python. This will be of much use to those who have experience working with SQL but new to Python. You shall know how to do operations in both of these interchangeably. The purpose of this article is to introduce you to “Best of Both Worlds”. Through experience, I have noticed that for some operations, SQL is more efficient (and hence easy to use) and for others, Pandas has an upper hand (and hence more fun to use). ![]() ![]() Most of these operations can be done in Python using Pandas as well. It has retained its rightful grip on this field for decades.īy virtue of this monopolistic hold, the data being stored by an organization, especially the Relational Databases needs the use of SQL to access the database, as well as to create tables, and some operations on the tables as well. On the other hand, SQL is the guardian angel of Databases across the globe. Python has Pandas which makes data import, manipulation, and working with data, in general, easy and fun. Everyone dealing with data in any capacity has to be conversant in both SQL and Python. ![]()
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