Outside of rare cases involving using generators as coroutines (where you're using. Is there a way to directly return a generator expression from df.to_dict(orient='records') instead of a list in order to reduce the memory footprint?Īs you can see, your return value does get "returned" in a sense (it's not completely discarded), but it's never seen by anything iterating normally, so it's largely useless. Furthermore, calling iter(df.to_dict(orient='records')) would return the desired generator, but would not reduce the required memory footprint as the list is created intermediately. I could certainly circumvent this issue by processing the dataframe chunk-wise and generate the list of dictionaries for each chunk which is then passed to the API. As my dataframe can get rather large, this might lead to memory issues especially as the code might be executed on lower spec target systems. When dealing with lists, the complete memory required to store the list items, is reserved/allocated. Resulting transformation depends on the orient parameter.įor my case, passing orient='records', a list of dictionaries is returned. Return a object representing the DataFrame. As stated in the docs, the returned value depends on the orient option: The required dictionaries can be generated by calling the. But, the difference would be that without the annotations all fields would be generated as optional fields and you will not be able to specify and enforce constraints, if any.I am working on a large Pandas DataFrame which needs to be converted into dictionaries before being processed by another API. You can pretty well do that without the annotations. Note: It is not absolutely necessary to annotate the Java class to be able to generate a JSON schema from it. Take a close look at the annotations on each class member of the Employee class shown below and relate them with the discussion here. gender field can only have MALE or FEMALE as its value.And each line cannot exceed 30 characters in length. address cannot be empty and can have up to and not exceeding 3 address lines.first name and last name fields cannot be empty.Using the annotation, we enforce the following constraints on the members of the Java class: In the example below, we have the Java class called Employee with fields for id, first name, last name, age, gender and multiple address lines. JJSchema provides an annotation to specify the above meta data for the members of the Java class. Optional constraints on number of occurrences and length of the value of the field.Indicator on whether the field is mandatory or optional.A partial list of meta data is given below. Like XML schemas, JSON schemas includes a list of meta data about the schema fields. This tutorial uses its 0.6 version.īelow is the maven dependency for JJSchema. The current release of JJSchema is 0.6 while version 1.0 is under development. Jackson, itself, lacks the capability to generate JSON Schema from a Java class and hence libraries like JJSchema come as a great help when it comes generating JSON schemas. It uses the popular Jackson JSON processor Java library, internally. JJSchema is an open source library hosted on Github that can generate the latest draft v4 compliant JSON schemas. JSON Schema specification is currently under draft and the latest version is v4. JSON Schemas are to JSON as XML Schemas are to XML. To be able to generate the JSON schema properly, the Java class should have getters and setters defined for its members.Ī JSON Schema is a JSON document that describes the structure of the JSON data. We will use an open source library called JJSchema to do the job. This tutorial shows you how to generate JSON schema from Java class.
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