While the examples you’ve worked with here are certainly contrived and overly simplistic, they illustrate a workflow you can apply to more general tasks: All done!Ĭongratulations, you can now wield the mighty power of JSON for any and all of your nefarious Python needs. You could also try subclassing JSONDecoder and overriding object_hook, but it’s better to stick with the lightweight solution whenever possible. loads ( data, object_hook = decode_complex ).
> with open ( "complex_data.json" ) as complex_data. So, for the sake of these examples, your complex object is going to be a complex object. You could use any custom class you like, but Python has a built-in type called complex for representing complex numbers, and it isn’t serializable by default. To get the hang of this, you’ll need a complex object to play with. It’s like the transitive property in mathematics: if A = B and B = C, then A = C. Essentially, you translate the more complex object into a simpler representation, which the json module then translates into JSON. Instead of going straight from the custom data type to JSON, you can throw in an intermediary step.Īll you need to do is represent your data in terms of the built-in types json already understands. Well, you could try to encode and decode the JSON by hand, but there’s a slightly more clever solution that’ll save you some work.
Python json query by date how to#
Now, the question is how to deal with more complex data structures. It’s like trying to fit a square peg in a round hole-you need a buzzsaw and parental supervision. dumps ( elf ) TypeError: Object of type 'Elf' is not JSON serializableĪlthough the json module can handle most built-in Python types, it doesn’t understand how to encode customized data types by default. I don’t know about you, but when I run the script interactively again, I get the following results: Yeah, yeah, your implementation is better, but the point is, you can now manipulate the JSON data as a normal Python object! append ( str ( user )) max_users = " and ". users = for user, num_complete in top_users : if num_complete < max_complete : break users. max_complete = top_users # Create a list of all users who have completed # the maximum number of TODOs. items (), key = lambda x : x, reverse = True ) # Get the maximum number of complete TODOs. todos_by_user ] = 1 # Create a sorted list of (userId, num_complete) pairs. todos_by_user ] += 1 except KeyError : # This user has not been seen. for todo in todos : if todo : try : # Increment the existing user's count. # Map of userId to number of complete TODOs for that user todos_by_user = # Increment complete TODOs count for each user. Simple Python objects are translated to JSON according to a fairly intuitive conversion. There is also a dumps() method (pronounced as “dump-s”) for writing to a Python string. Accordingly, the json library exposes the dump() method for writing data to files. What happens after a computer processes lots of information? It needs to take a data dump. Think of it like this: encoding is for writing data to disk, while decoding is for reading data into memory. But in reality, all we’re talking about here is reading and writing. Naturally, deserialization is the reciprocal process of decoding data that has been stored or delivered in the JSON standard.ĭefinitely. You may also hear the term marshaling, but that’s a whole other discussion.
Python json query by date series#
This term refers to the transformation of data into a series of bytes (hence serial) to be stored or transmitted across a network. The process of encoding JSON is usually called serialization. Keep in mind, JSON isn’t the only format available for this kind of work, but XML and YAML are probably the only other ones worth mentioning in the same breath. Yup, you got it! It’s nothing more than a standardized format the community uses to pass data around. So, we use JSON to store and exchange data? Have no fear, fellow Pythoneers and Pythonistas. Luckily, this is a pretty common task, and-as with most common tasks-Python makes it almost disgustingly easy. One way or another, you’re up to your neck in JSON, and you’ve got to Python your way out. Perhaps you’re gathering information through an API or storing your data in a document database. Chances are you’re here because you need to transport some data from here to there. Since its inception, JSON has quickly become the de facto standard for information exchange.
Watch it together with the written tutorial to deepen your understanding: Working With JSON Data in Python
Python json query by date code#
We can use the following code to find all documents where the “day” field is before a specific date: db.sales.Watch Now This tutorial has a related video course created by the Real Python team. You can use the following basic syntax to perform a query with a date range in MongoDB: db.collection.find( Example 3: Find Documents Before Specific Date
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