Usage

To use deeppath in a project:

from deeppath import dget

with open("my_data.json") as json_data
    data = json.load(json_data)

nested_value = dget(data, "some/path/to/the/data")

With dget(), you can access data in a complex nested structure. The nested structure should be a json-like structure, so essentially consisting of dictionary- and list-like structures:

nested_data = {
    "users": [
        {"surname": "Doe", "name": "John"},
        {"name": "Jane", "surname": "Doe"},
    ],
    "location": {
        "city": "London",
        "country": "United Kingdom",
    },
}

dget(nested_data, "location/city")  # returns "London"
dget(nested_data, "users[0]/name")  # returns "John"

As can be seen in this example, the data can be heterogenous, dget will access whatever path matches!

Dealing with dict-like structure

The simplest use case is to deal with nested dictionaries: a dict of dict of dict… (it’s dictionaries all the way). If your data is stored in a structure supporting [key] access, you can simply list the keys of the successive dictionaries separated by “/” in a single string:

nested_data = {
    "first": {
        "second": {
            "third": "value"
        }
    }
}

dget(nested_data, "first/second/third")  # returns "value"

Dealing with list-like structures

If your data is contained in a list-like (a container in which you can access elements by index), you can simply enhance your string to use indexes. dget supports the same semantics as python lists so you can also use negative indices.

nested_data = {"names": ["John", "Jane"]}

dget(nested_data, "names[1]")  # returns "Jane"
dget(nested_data, "names[-1]")  # also returns "Jane"

Mixing data structure types

As far as dget is concerned, it does not matter what is the underlying data type. It merely requires that elements are accessible either through string keys (for dictionaries) or indices (for lists). You can have heterogenous data and use it in a very natural way.