Join Dan Sullivan for an in-depth discussion in this video Data frames in MongoDB, part of Advanced NoSQL for Data Science.
- [Instructor] In this video,…I'm going to create a new notebook…and show how to load data from MongoDB into DataFrames,…a commonly used data structure for data science.…The first thing I'm going to do is navigate to the New button,…and create a new Python notebook.…And because I'm working with MongoDB,…I'm going to import pymongo.…Now I also want to import a package called pandas.…Pandas is commonly used in data science.…I'm also going to use a Python convention…of referring to pandas simply as pd,…that'll save me some typing.…
You may have noticed that we did not execute a pip install…to make sure the pandas library was available.…pandas is one of the many libraries…that comes with the Anaconda distribution,…and that's one of the reasons why we chose that.…What I'd like to do is load data…from MongoDB database into Python.…Well, I'm going to start with a client,…and make a connection using pymongo.…And I'm still working with a database on my localhost,…and it's still at the same port, 27017.…Now, I want to connect to my database,…
The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.
- NoSQL compared to traditional relational databases
- Performing common data science tasks
- Preparing data with document databases
- Manipulating data in NoSQL
- Preparing, exploring, extracting, and model building
- Working with document, wide-column, and graph databases
- Reviewing case studies using MongoDB, Cassandra, and Neo4j
Skill Level Advanced
1. Why NoSQL?
Types of NoSQL databases2m 20s
2. Perform Common Data Science Tasks with NoSQL Databases
3. Document Databases for Data Science
4. Wide-Column Databases for Data Science
5. Graph Databases for Data Science
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