JSON is heavily relied on in the field of data analytics, so it’s likely that you will learn about it if you decide to enroll in a data analytics class.
There are two structures that it is built upon: an ordered list with values, and a collection of name and value pairs. The first appears in the form of a sequence, list, vector or array. The latter is appears as a hash table, associative array, keyed list, record, dictionary or object.
Values must be one of several potential data types, including strings, Null, Boolean, arrays, objects or numbers. Keys must be strings with double-quotes while objects are placed inside curly braces and have value or key pairs. Booleans can only be true or false, making it easy to incorporate data with simple values, such as whether someone is married.
Advantages of JSON
JSON is fully independent of any platform, allowing for the tranfer massive amounts of data between computers. Naturally, this has made it a popular choice for data analytics.
Compared to XML
JSON in Python
JSON has native support in Python, and they share similar representation. If you’re using Python, you have several package options including json, ultrajson, Yajl-Py, simplejson, jyson and metamagic.json.
Converting JSON into Python objects — and vice-versa — is relatively straightforward. To convert into Python objects, use json.loads(). To convert from Python objects, use json.dumps().
Using these commands allows you to perform the role of a JSON parser without additional programs.
As you begin using JSON, you might start to notice some minor variations in these functions. For instance, if you are loading a file, you would use json.load as the function, but to load a string, then you would use json.loads. It is similar for the “dump” functions as well: use json.dump to dump the JSON into your file, and use json.dumps for cases when the data should be a string for printing or parsing, indicating a dump string.
Writing and Reading Files
The first thing you should learn to do with JSON is write a file, which will have the “.json” extension. The below instructions assume you are using Python, however the process is just as simple for most other programming languages.
To write the file, use the open() function in Python along with the parameter “w,” which indicates that you will write the file.
To read your file, just use json.load.
Data and Data Science
In data science and analytics, data is commonly loaded from a JSON format. The easiest way to do this is to in Pandas: use the function .read_json and let the data load. Then, convert it into a dataframe using pandas.DataFrame as your attribute for easier analysis.
Limits on Implementation
When you encode JSON, it is known as serialization, whereas when you decode it, it is deserialization. Deserializer implementations are able to set certain limits, which can be for the size of accepted texts, maximum nesting level of arrays and objects, precision and range of numbers or maximum length and content of strings.
Keep in mind that when these limitations are present, they are not universally relevant; they are only relevant to the Python interpreter and the Python data types.
Developers commonly use JSON to create APIs for web applications, as it allows them to use the language of their choice (provided it is supported by JSON).
Because of its versatility and lack of platform dependence, JSON is a popular choice in the field of data analytics. As with any othe programming skill, it’s helpful to enroll in a data analyst class in San Francisco or another tech hub for more opportunities for guided practice.