data-struct 1.2.0

A class to help you handling complex JSON-like objects

data-struct

structLib is essentially a module which contains the Struct class. This class specifically aims at making your life easier when working with JSON-like objects (meaning nested dict / list objects)

Installation

To install structLib on your python setup, run the following:

pip install data-struct

On linux or mac you will probably need to run this command instead:

python3 -m pip install data-struct

Please note that version 3.8 (or higher) of python is required to run this library!

Usage

Let's have closer look at what we can do with this library and how... If you take a look at the code or the help module you will notice that the Struct class isn't the only element in there. There is indeed a bunch of other functions defined. The only reason these functions are not part of the class is because they work using recursive algorithms, therefore it was way easier to implement them outside and then simply call them from the class methods when needed. Note that you should not be using those functions, the Struct class handles all of that for you.

Example

Now let's look at some code shall we? The following code can be found in the examples folder.

# This code aims to illustrate how we can use structLib
from structLib import Struct

# You will notice I only import the Struct class, the reason in because it's the only thing actually useful for
# us

with open("actors.json") as f_in:
    data = Struct.load(f_in)  # First we load the data into a Struct object.

print(data, '\n')

"""Let's say I want to see if "Tom Cruise" is present in the data structure
I could use the Struct.isValueIn() method but if I want to know where the value is, there is a better option
"""

path = data.pathToValue("Tom Cruise")  # If the value doesn't exist in the Struct, the result will be None
print(path)

# Now I want to get his age:

age = data["Actors", 0, 'age']
print(age, '\n')

# The value is wrong let's fix that
data["Actors", 0, "age"] = 58

# Let's add Jonny Depp in there
jonny = {
    "name": "Jonny Depp",
    "age": 57,
    "Born At": "Owensboro, Kentucky",
    "Birthdate": "June 9, 1963",
    "photo": "https://upload.wikimedia.org/wikipedia/commons/thumb/7/79/Johnny_Depp_Deauville_2019.jpg/390px"
             "-Johnny_Depp_Deauville_2019.jpg",
}

data['Actors'].append(jonny)

print(data, '\n')

# Let's say I want to print the ages of everyone:
for a in data.getAll("age"):
    print(a)

# Now let's sort them by age
data['Actors'] = Struct(data['Actors']).sorted(path="age").data
# Here I have to do this complicated mess because the list isn't at the top level of the structure but rather 
# under the "Actors" key
print(data)

# Finally let's write that back into the file
with open("sorted_actors.json", 'w') as f_out:
    data.dump(f_out, indent=4)

The constructor

Creating a Struct instance couldn't be easier:

from structLib import Struct

s = Struct(data)

In most cases, data would be a list or a dictionary. But even if it's not the case, the object will automatically be deserialized by putting all its attributes into a dict object. That being said, keep in mind that any non-builtin type will be converted to a string so make sure that all the data contained in your object is indeed convertible into a string, otherwise some information will be lost.

Iterating through a Struct

The for syntax works perfectly with Struct objects but the behavior depends on what type of data we have. If self.data is a list, for element in struct_object will work exact way as for a regular list object, no mystery there.
However if self.data is a dictionary, then element will be a tuple containing the key, and the corresponding value.

The sorted/sort methods

Those are interesting. The only difference between the two is that the sort method modifies the object directly and returns nothing, while the sorted method returns the sorted version of the object but leaves the original as is. Aside from that the behavior is exactly the same. With that out of the way, let's look at the interesting in part

Usage:

struct_object.sort(path=None, function=lambda x:x)

The path argument specifies which value should be prioritized to run the sort.
The function is a function that will be executed on the value. The default is lambda x:x, which is a function that does explicitly nothing to the value it receives.

The behavior of the sort will depend on what type of data you have. Indeed, the self.data attribute can either be a list or a dict object.
We then have four possible situations:

• If it's a list and path is None, the list will be sorted directly based on the values it contains.
• If it's a list and path is specified, the list will be sorted based on the value which corresponds to path in each element on the list.
• If it's a dict and path is None, the dict will be sorted based on the keys.
• If it's a dict and path is specified, the dict will be sorted based on the value corresponding to the path in each element of the dict.

Let's give an example:

from structLib import Struct
from datetime import datetime

def parseDate(string):
    content = [int(x) for x in string.split('-')]
    return datetime(year=content[2], month=content[1], day=content[0])

data = Struct({
    "10-08-2020": {'id': 2},
    "7-08-2020": {'id': 1},
    "1-07-2020": {'id': 3}
})

def test1():
    # Sort by date
    data.sort(function=parseDate)
    assert data.getAll("id") == [3, 1, 2]

def test2():
    # Sort by id
    data.sort('id')
    assert data.getAll("id") == [1, 2, 3]

# now if it's a list
data2 = Struct([1, 8, 3])

def test3():
    # Note that using Struct for a simple list like this is pointless
    assert [x for x in data2.sorted()] == [1, 3, 8]

data3 = Struct(
    [
        {"date": "10-08-2020"},
        {"date": "7-08-2020"},
        {"date": "1-07-2020"}
    ]
)

def test4():
    data3.sort("date", parseDate)
    assert [x["date"] for x in data3] == ["1-07-2020", "7-08-2020", "10-08-2020"]

Requirements

None! That's also what is cool about it!

Authors

EddieBreeg