Explore
For Enterprise
Join for Free
Log In

Parcourir
Rechercher
For Enterprise
Connexion
Inscrivez-vous gratuitement

Writing code using DB-API

Loading...

Databases and SQL for Data Science

4.7 (6,857 évaluations) | 69K étudiants inscrits

Cours 5 de 9 dans Science des données IBM Spécialisation

Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python. No prior knowledge of databases, SQL, Python, or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Visualiser le programme de cours

Compétences que vous apprendrez

Cloud Databases, Python Programming, Ipython, Relational Database Management System (RDBMS), SQL

Avis

4.7 (6,857 évaluations)

5 stars
5,163 ratings
4 stars
1,296 ratings
3 stars
274 ratings
2 stars
69 ratings
1 star
55 ratings

DT

Jun 17, 2019

This is a great review of an important topic. If I knew how much this mattered for the advanced capstone course I would have paid more attention. As it was, I came back here frequently.

MR

Nov 26, 2019

Really right to the point and efficient. Good examples and short direct videos. I had no idea about SQL before and now I can run good queries and have enough tools to learn even more.

À partir de la leçon

Week 3 - Accessing Databases using Python

After completing the lessons in this week, you will learn how to explain the basic concepts related to using Python to connect to databases and then create tables, load data, query data using SQL and analyze data using Python

How to access databases using Python?5:57

Writing code using DB-API4:46

Connecting to a database using ibm_db API2:05

Creating tables, loading data and querying data3:51

Analyzing data with Python9:29

Enseigné par

Rav Ahuja
AI and Data Science Program Director

Transcription

Hello and welcome to writing code using DB-APIs. After completing this video, you will be able to explain the basic concepts related to the Python DB-API and database cursors. And also write code using DB-APIs. As we saw in the beginning of this module, the user writes Python programs using a Jupyter notebook. There is a mechanism by which the Python code communicates with the DBMS. The Python code connects to the database using DB-API calls. DB-API is Python's standard API for accessing relational databases. It is a standard that allows you to write a single program that works with multiple kinds of relational databases instead of writing a separate program for each one. So, if you learn the DB-API functions, then you can apply that knowledge to use any database with Python. Here are some advantages of using the DB-API. It's easy to implement and understand. This API has been defined to encourage similarity between the Python modules that are used to access databases. It achieves consistency which leads to more easily understood modules. The code is generally more portable across databases and it has a broader reach of database connectivity from Python. As we know, each database system has its own library. As you can see, the table shows a list of a few databases and corresponding DB-APIs to connect to Python applications. The Ibm_db library is used to connect to a DB2 Warehouse on Cloud database. The MySQL Connector/Python library is used to connect to a Compose for MySQL database. The psycopg2 library is used to connect to a Compose from PostgreSQL database. And finally, the PyMongo library is used to connect to a Compose for MongoDB database. The two main concepts in the Python DB-API are connection objects and query objects. You use connection objects to connect to a database and manage your transactions. Cursor objects are used to run queries. You open a cursor object and then run queries. The cursor works similar to a cursor in a text processing system where you scroll down in your result set and get your data into the application. Cursors are used to scan through the results of a database. Here are the methods used with connection objects. The cursor() method returns a new cursor object using the connection. The commit() method is used to commit any pending transaction to the database. The rollback() method causes the database to roll back to the start of any pending transaction. The close() method is used to close a database connection. These objects represent a database cursor which is used to manage the content of a fetch operation. Cursors created from the same connection are not isolated i.e. any changes done to the database by a cursor are immediately visible by the other cursors. Cursors created from different connections can or cannot be isolated depending on how the transaction support is implemented. A database cursor is a control structure that enables traversal over the records in a database. It behaves like a file name or file handle in a programming language. Just as a program opens a filed accesses files contents, it opens a cursor to gain access to the query results. Similarly, the program closes a file to end its access and closes a cursor to end access to the query results. Another similarity is that just as file handle keeps track of the program's current position within an open file, a cursor keeps track of the program's current position within the query results. Let's walk through a Python application that uses the DB-API to query a database. First, you import your database module by using the connect API from that module. To open a connection to the database, you use the connection function and pass in the parameters that is the database name, username, and password. The connect function returns connection object. After this, you create a cursor object on the connection object. The cursor is used to run queries and fetch results. After running the queries using the cursor, we also use the cursor to fetch the results of the query. Finally, when the system is done running the queries, it frees all resources by closing the connection. Remember that it is always important to close connections to avoid unused connections taking up resources. Thanks for watching this video.

Explorer notre catalogue

Rejoignez-nous gratuitement et obtenez des recommendations, des mises à jour et des offres personnalisées.

Coursera

À propos
Direction
Carrières
Catalogue
Certificats
Certificats MasterTrack™
Diplômes
For Enterprise
For Government
Pour le campus

Communauté

Étudiants
Partenaires
Développeurs
Testeurs bêta
Traducteurs
Blog
Blog Tech

Plus

Conditions
Confidentialité
Aide
Accessibilité
Presse
Contact
Répertoire
Filiales

Apprendre partout

Suivez-nous

© 2019 Coursera Inc. Tous droits réservés.