# and insert the data into the collection. # which is created dynamically like the database, # We define the collection we will store this data in, # Now, we make the API call and prices the results to the terminal. # It does not have to exist first, like with relational databases. # Connect to the database with the connection string we got from Atlas, replacing user and password.Ĭlient = Next we define the database we are using. After that, you can create the script like below: # Import the libraries we need The pymongo package is a library that makes it easy to interact with a MongoDB database in Python. The requests package will enable you to make the API call. Run the command below in a terminal to install those packages. First, be sure to install all the Python packages you need for the script using Python version 3.11 or later. Now, let's create a Python script that will connect to the CoinGecko API-an open API for cryptocurrency prices-and store that data in our database. You will be adding this database connection string to your Python scripts later. You will have to replace in this string with your actual password and with the name you will be giving your database. Since you are going to be connecting with Python, choose Connect your application.Ĭhoose Python as a language and your version of Python, and you will be presented with a connection string. The next screen will give you the option to choose how you will connect to your new database. After that, click the Choose a connection method button, which will now be active. Then add a Username and Password and click Create Database User. Add a description if you want and click Add IP Address. The IP address will auto-populate with your local IP address. Here you will have to add your local IP address and create a user for your database. Once complete, you will see a screen like below:Ĭlick on the Connect button to start setting up your connection. It will take a few minutes for the cluster to provision. Unless you want to modify the cluster, you can choose the default and click Create Cluster. It’s worth noting that the free tier here remains free, as opposed to other products which might offer a free trial period only. I chose the free option for this example. Next, choose the type of account you need. Once you have created your account, you will be prompted to name your organization, name your project, and choose the language for code samples and help. To get started on using MongoDB, you’ll first need to sign up for a free MongoDB Atlas account here. How to Create a Database in Your Python App Using MongoDB Atlas To see how easy it is to get started with Python, let’s first create a database on MongoDB Atlas. Although MongoDB supports modeling and querying relationships between documents, it really excels when you wish to embed related data within a document itself.įor our tutorial, let’s take a look at how you might import data from a cryptocurrency API into a MongoDB collection using Python. It’s easily scalable, flexible, and relatively simple for beginners. Non-relational databases like MongoDB can store large amounts of data with varying structures. With their document-oriented (as opposed to table-oriented) structure, document databases support a varying data structure easily some records might have certain attributes, while other records don’t have those attributes. When your data structure might be more fluid, with a schema that is less fixed, a document database like MongoDB might be more appropriate. For smaller-scale applications, or for local testing, SQLite is also a good option to use. This type of data structure lends itself well to management by a relational database system (like MySQL or PostgreSQL). Most likely, though, these sets of attributes for each model will not change very much for the purposes of your application, and it’s pretty likely that each record will have all of their attributes. Courses might have titles, descriptions, start and end dates, and so on. Students will have a similar set of attributes. Teachers will have IDs, names, basic contact information, and maybe a role (“homeroom,” “substitute”). A relational database, as you might expect, is also often the best choice for highly interrelated data.įor example, consider a school database to manage teachers, students, and courses. In practice, this is very unusual while an application is under active development. Relational databases like MySQL and PostgreSQL can be a good choice if you’re dealing with data models that have a fixed structure (think: “tables”)-that is, the set of attributes for any given model changes rarely. With so many options for databases, which solution is best for your application? Which you choose depends on your use case.
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