django-filterly 0.1.0

A clean, flexible Django queryset filtering library driven by query-string params.

django-filterly — Zero-Boilerplate Filtering for Django

A clean, flexible Django queryset filtering library driven by query-string params.
Drop it into any Django or DRF view and turn URL parameters into ORM queries — zero boilerplate.

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⚡ What You Get

GET /products?name__icontains=shirt&price__gte=100&is_active=true

from django_filterly import FilterSet

def product_list(request):
    qs = FilterSet(
        queryset=Product.objects.all(),
        params=request.GET,
    ).qs
    return JsonResponse(list(qs.values()), safe=False)

# Equivalent to:
# Product.objects.filter(name__icontains="shirt")
#                .filter(price__gte=100)
#                .filter(is_active=True)

That's it — no manual parsing, no type casting, no repetitive if "field" in request.GET blocks.

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🚀 Key Features

• Zero-Boilerplate Filtering from query params — no manual parsing
• Automatic Type Coercion — bool, int, float, date, datetime, list, range, UUID
• Deep Relation Filtering — user__profile__city__icontains=Cairo
• Built-In Validation with structured errors and machine-readable codes
• Works With Django & DRF out of the box — no extra setup
• No Config, No Magic Classes — just pass request.GET and go

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✨ Why django-filterly?

Without filterly	With filterly
Parse request.GET by hand	One FilterSet(queryset, request.GET).qs call
Write repetitive if "field" in request.GET blocks	Automatic type coercion, validation, and ORM mapping
Risk invalid values crashing your queries	Structured errors with machine-readable codes
Duplicate filter logic across views	Reusable FilterSet subclasses per model

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🤔 Why not django-filter?

• No serializers required — pass request.GET directly, no FilterSet class declaration needed
• No config-heavy classes — no Meta, no fields = [...], no per-field Filter() instances
• Works directly with query params — drop it into any view or script with a plain dict
• Zero setup — no INSTALLED_APPS entry, no migration, no wiring

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📦 Installation

pip install django-filterly

No extra settings needed — filterly has zero Django app registration. Just import and use.

Requirements: Python ≥ 3.10 · Django ≥ 3.2

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🔍 Supported Lookups

Text / String

Lookup	SQL equivalent	Example param
exact	= 'value'	name__exact=T-Shirt
iexact	ILIKE 'value'	name__iexact=t-shirt
contains	LIKE '%value%'	name__contains=shirt
icontains	ILIKE '%value%'	name__icontains=shirt
startswith	LIKE 'value%'	name__startswith=T
istartswith	ILIKE 'value%'	name__istartswith=t
endswith	LIKE '%value'	name__endswith=Shirt
iendswith	ILIKE '%value'	name__iendswith=shirt
regex	~ 'pattern'	name__regex=^T.*t$
iregex	~* 'pattern'	name__iregex=^t.*t$

Comparison

Lookup	SQL equivalent	Example param
gt	> value	price__gt=50
gte	>= value	price__gte=50
lt	< value	price__lt=200
lte	<= value	price__lte=200

Date & Time Parts

Lookup	Extracts	Example param
date	Date part of a datetime	created_at__date=2024-01-15
year	Year integer	created_at__year=2024
month	Month integer (1–12)	created_at__month=6
day	Day integer (1–31)	created_at__day=15
week	ISO week number	created_at__week=3
week_day	1=Sun … 7=Sat	created_at__week_day=2
hour	Hour (0–23)	updated_at__hour=14
minute	Minute (0–59)	updated_at__minute=30
second	Second (0–59)	updated_at__second=0

Sets & Ranges

Lookup	Behaviour	Example param
in	Field value is one of a list	status__in=active,pending,review
range	BETWEEN two values (inclusive)	price__range=10,500

Null / Existence

Lookup	Behaviour	Example param
isnull	IS NULL / IS NOT NULL	deleted_at__isnull=true

Exclusion (NOT)

Lookup	Behaviour	Example param
not	.exclude(field__exact=value)	status__not=draft

JSON Fields (PostgreSQL)

Lookup	Behaviour	Example param
contains	JSON subset match	meta__contains={"color":"red"}
has_key	Key exists	meta__has_key=color
has_keys	All keys present	meta__has_keys=color,size
has_any_keys	Any key present	meta__has_any_keys=color,size

Array Fields (PostgreSQL)

Lookup	Behaviour	Example param
contains	Array contains all values	tags__contains=sale,new
overlap	Array shares ≥1 value	tags__overlap=sale,promo
len	Array length equals N	tags__len=3

---

📖 Usage Examples

1. Plain dict — useful in tests or scripts

qs = FilterSet(
    queryset=Product.objects.all(),
    params={"price__gte": "50", "is_active": "true"},
).qs

2. Boolean values

filterly accepts any of these for True / False:

true  1  yes  on   → True
false 0  no   off  → False

3. Null checks

?deleted_at__isnull=true   → WHERE deleted_at IS NULL
?deleted_at__isnull=false  → WHERE deleted_at IS NOT NULL

4. Exclude / NOT

?status__not=draft  →  .exclude(status__exact="draft")

5. IN queries — two syntaxes

# Comma-separated string
?status__in=active,pending,review

# Multi-value QueryDict (?status=active&status=pending)
# auto-promoted to __in when the same key appears multiple times

6. Range queries

?price__range=10,500              → WHERE price BETWEEN 10 AND 500
?created_at__range=2024-01-01,2024-12-31

7. Date & datetime

?created_at__date=2024-01-15
?created_at__year=2024
?created_at__gte=2024-01-01T00:00:00
?created_at__lte=2024-12-31T23:59:59Z   ← Z suffix handled

8. Foreign key traversal — ORM style

?owner__email__icontains=gmail.com
?user__age__gte=18

9. Foreign key traversal — flat shorthand

Three or more underscore-separated segments where the last is a known lookup:

?owner_email_icontains=gmail.com   →  owner__email__icontains
?user_age_gte=18                   →  user__age__gte

Plain two-segment fields (is_active, created_at, user_id) are not treated as flat notation.

10. Deep / multi-hop relations

?owner__profile__city__icontains=Cairo

11. JSON field lookups

?meta__has_key=color
?meta__has_keys=color,size
?meta__has_any_keys=color,size
?meta__contains={"color": "red"}

12. PostgreSQL array field lookups

?tags__contains=sale,new
?tags__overlap=sale,promo
?tags__len=3

13. Custom transformers — per-field value conversion

def parse_price(value, *, field, lookup):
    cleaned = str(value).lstrip("$€£").strip()
    try:
        return float(cleaned)
    except ValueError:
        raise FilterValueError(
            f"Cannot parse '{value}' as a price.",
            field=field, lookup=lookup, value=value,
            expected_type="float", code="invalid_price",
        )

qs = FilterSet(
    queryset=Product.objects.all(),
    params={"price__gte": "$100.00"},
    custom_transformers={"price": parse_price},
).qs

14. DRF — inside a ListAPIView

from rest_framework.generics import ListAPIView
from django_filterly import FilterSet

class ProductListView(ListAPIView):
    serializer_class = ProductSerializer

    def get_queryset(self):
        qs = FilterSet(
            queryset=Product.objects.all(),
            params=self.request.GET,
            allowed_fields={"name", "price", "is_active", "created_at"},
            allowed_lookups={"exact", "icontains", "gte", "lte", "range"},
        ).qs
        return qs

15. Subclassing FilterSet — reusable defaults per model

class ProductFilterSet(FilterSet):
    _FIELDS = {
        "name", "price", "stock", "is_active",
        "created_at", "updated_at", "owner__email",
    }
    _LOOKUPS = {
        "exact", "icontains", "gt", "gte", "lt", "lte",
        "range", "in", "isnull", "not", "year", "month",
    }

    def __init__(self, params, *, queryset=None, **kwargs):
        super().__init__(
            queryset=queryset or Product.objects.all(),
            params=params,
            allowed_fields=kwargs.pop("allowed_fields", self._FIELDS),
            allowed_lookups=kwargs.pop("allowed_lookups", self._LOOKUPS),
            **kwargs,
        )

# In a view:
qs = ProductFilterSet(request.GET).qs

---

🔧 Advanced Usage (Validation, Whitelisting, Errors)

Whitelisting fields and lookups

allowed_fields and allowed_lookups are optional.

• Omit them → filterly accepts any field that exists on the model and any supported lookup. Good for internal tools or trusted clients.
• Set them → filterly rejects anything outside the whitelist with a FilterValidationError. Use this in public APIs to prevent callers from filtering on sensitive fields (e.g. password, secret_token) or using expensive lookups (e.g. regex).

# No whitelist — all model fields and all supported lookups accepted
qs = FilterSet(
    queryset=Product.objects.all(),
    params=request.GET,
).qs

# With whitelist — anything outside raises FilterValidationError
qs = FilterSet(
    queryset=Product.objects.all(),
    params=request.GET,
    allowed_fields={"price", "status", "created_at"},   # only these fields
    allowed_lookups={"exact", "gte", "lte", "in"},      # only these lookups
).qs

Full example with error handling

from django_filterly import FilterSet, FilterValidationError, FilterValueError

def product_list(request):
    """
    Examples:
    ?name__icontains=shirt
    ?price__range=10,500
    ?is_active=true
    ?created_at__year=2024
    ?status__in=active,pending
    ?deleted_at__isnull=true
    ?status__not=draft
    ?owner__email__icontains=gmail
    """
    try:
        qs = FilterSet(
            queryset=Product.objects.select_related("owner").all(),
            params=request.GET,
            allowed_fields={"name", "price", "is_active", "status",
                            "created_at", "deleted_at", "owner__email"},
            allowed_lookups={"exact", "icontains", "gte", "lte", "range",
                             "in", "isnull", "not", "year"},
        ).qs
        return JsonResponse(list(qs.values()), safe=False)

    except FilterValidationError as e:
        # e.field, e.lookup, e.code  →  "field_not_allowed" | "lookup_incompatible" | …
        return HttpResponseBadRequest(str(e))

    except FilterValueError as e:
        # e.field, e.value, e.expected_type, e.code  →  "invalid_int" | "invalid_date" | …
        return HttpResponseBadRequest(str(e))

Error codes reference

Code	Exception	When raised
field_not_allowed	FilterValidationError	Field not in allowed_fields
lookup_not_supported	FilterValidationError	Lookup unknown to filterly
lookup_not_allowed	FilterValidationError	Lookup not in allowed_lookups
lookup_incompatible	FilterValidationError	Lookup wrong for field's data type
field_does_not_exist	FilterValidationError	Field not on the model
not_a_relation	FilterValidationError	FK traversal on a non-FK field
invalid_bool	FilterValueError	Value not a recognised boolean
invalid_int	FilterValueError	Value not a valid integer
invalid_float	FilterValueError	Value not a valid float
invalid_number	FilterValueError	Value not a valid number
invalid_date	FilterValueError	Value not ISO 8601 date
invalid_datetime	FilterValueError	Value not ISO 8601 datetime
invalid_time	FilterValueError	Value not a valid time
invalid_uuid	FilterValueError	Value not a valid UUID
invalid_range	FilterValueError	Range does not have exactly 2 values
empty_list	FilterValueError	__in list is empty
null_value	FilterValueError	None passed for a non-nullable field

---

🛒 Real Example (E-commerce)

A shopper hits this URL:

GET /products?price__gte=100&price__lte=500&is_active=true&category__name__icontains=shoes&stock__gt=0&created_at__year=2024&tags__overlap=sale,new&ordering=price

filterly handles all 7 filter params in one call:

from django_filterly import FilterSet, FilterValidationError, FilterValueError
from django.http import JsonResponse, HttpResponseBadRequest

def product_list(request):
    try:
        qs = FilterSet(
            queryset=Product.objects.select_related("category").all(),
            params=request.GET,
            allowed_fields={
                "price", "is_active", "stock", "created_at",
                "category__name", "tags",
            },
            allowed_lookups={
                "gte", "lte", "gt", "exact",
                "icontains", "year", "overlap",
            },
        ).qs
        return JsonResponse(list(qs.values()), safe=False)

    except FilterValidationError as e:
        return HttpResponseBadRequest({"error": str(e), "field": e.field, "code": e.code})

    except FilterValueError as e:
        return HttpResponseBadRequest({"error": str(e), "field": e.field, "code": e.code})

What filterly does under the hood for that URL:

Param	Parsed as	ORM call
price__gte=100	field=price, lookup=gte, value=100.0	.filter(price__gte=100.0)
price__lte=500	field=price, lookup=lte, value=500.0	.filter(price__lte=500.0)
is_active=true	field=is_active, lookup=exact, value=True	.filter(is_active=True)
category__name__icontains=shoes	field=category__name, lookup=icontains	.filter(category__name__icontains="shoes")
stock__gt=0	field=stock, lookup=gt, value=0	.filter(stock__gt=0)
created_at__year=2024	field=created_at, lookup=year, value=2024	.filter(created_at__year=2024)
tags__overlap=sale,new	field=tags, lookup=overlap, value=["sale","new"]	.filter(tags__overlap=["sale","new"])

Note: ordering is not a filter param — filterly ignores any param that doesn't match a field + lookup pattern.
→ You can safely mix filtering with pagination, ordering, or any other custom query params in the same URL.

---

🛠 Low-Level API

You can use individual modules directly when you only need part of the pipeline.

Parser only

from django_filterly import parse_params

descriptors = parse_params({"price__gte": "100", "name__icontains": "shirt"})
for d in descriptors:
    print(d.field, d.lookup, d.value, d.exclude)
# price  gte        100    False
# name   icontains  shirt  False

Validator only

from django_filterly import FilterDescriptor, validate_filter

validate_filter(
    FilterDescriptor(field="price", lookup="gte", value="100"),
    model=Product,
    allowed_fields={"price", "name"},
    allowed_lookups={"gte", "lte", "icontains"},
)

Individual transformers

from django_filterly import to_bool, to_date, to_int

to_int("42",           field="age",        lookup="gte")   # → 42
to_bool("yes",         field="is_active",  lookup="exact") # → True
to_date("2024-01-15",  field="created_at", lookup="date")  # → date(2024, 1, 15)

---

🗂 Project Structure

django-filterly/
├── django_filterly/
│   ├── __init__.py
│   ├── constants.py      # lookup groups and type→lookup maps
│   ├── exceptions.py     # FilterValidationError, FilterValueError
│   ├── helpers.py        # field resolution and type detection
│   ├── parser.py         # FilterDescriptor + parse_params()
│   ├── transformers.py   # type coercion functions
│   ├── validators.py     # validate_filter() / validate_all()
│   └── filterset.py      # FilterSet — the main public API
├── tests/
│   └── test_filters.py
├── examples/
│   └── basic_usage.py
├── run_tests.py
└── pyproject.toml

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🧪 Running the Tests

# Clone and set up
git clone https://github.com/esraamashaal/django-filterly.git
cd django-filterly

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\activate          # Windows
source .venv/bin/activate       # macOS / Linux

# Install dev dependencies
pip install -e ".[dev]"

# Run all tests with coverage
python run_tests.py

# Or with pytest directly
pytest tests/ -v
pytest tests/ -v --tb=short --cov=django_filterly --cov-report=term-missing

# Run a specific test class
pytest tests/ -k TestParser -v
pytest tests/ -k TestFilterSet -v

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📄 License

MIT — see LICENSE.

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👩‍💻 Author

Esraa Raffik Mashaal
Senior Software Engineer