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A library that enables programmatic interaction with daft.ie.

Project description

# Daftlistings

Python library that enables Programmatic interaction with [Daft.ie](https://daft.ie)

## Installation

daftlistings is available on the Python Package Index (PyPI). You can install daftlistings using pip.

``` bash
virtualenv env
source env/bin/activate
pip install daftlistings
```

To install the development version, run:

``` bash
pip install https://github.com/AnthonyBloomer/daftlistings/archive/dev.zip
```

## Examples

Get apartments to let in Dublin City that are between €1000 and €1500 and contact the advertiser of each listing.

``` python
from daftlistings import Daft, RentType

daft = Daft()

daft.set_county("Dublin City")
daft.set_listing_type(RentType.APARTMENTS)
daft.set_min_price(1000)
daft.set_max_price(1500)

listings = daft.search()

if len(listings) > 0:
first = listings[0]

contact = first.contact_advertiser(
name="Jane Doe",
contact_number="019202222",
email="jane@example.com",
message="Hi, I seen your listing on daft.ie and I would like to schedule a viewing."
)

if contact:
print("Advertiser contacted")
```

You can sort the listings by price, distance, upcoming viewing or date using the SortType object. The SortOrder object allows you to sort the listings descending or ascending.

``` python

from daftlistings import Daft, SortOrder, SortType, RentType

daft = Daft()

daft.set_county("Dublin City")
daft.set_listing_type(RentType.ANY)
daft.set_sort_order(SortOrder.ASCENDING)
daft.set_sort_by(SortType.PRICE)
daft.set_max_price(2500)

listings = daft.search()

for listing in listings:
print(listing.formalised_address)
print(listing.daft_link)
print(listing.price)
features = listing.features
if features is not None:
print('Features: ')
for feature in features:
print(feature)
print("")

```

Parse listing data from a given search result url.

``` python

from daftlistings import Daft

offset = 0

while 1:
daft = Daft()
daft.set_result_url("https://www.daft.ie/dublin-city/new-homes-for-sale/?ad_type=new_development")
daft.set_offset(offset)
listings = daft.search()
if not listings:
break
for listing in listings:
print(listing.formalised_address)
print(listing.price)
print(' ')
offset += 10

```

Find student accommodation near UCD that is between 850 and 1000 per month

``` python
from daftlistings import Daft, SortOrder, SortType, RentType, University, StudentAccommodationType

daft = Daft()
daft.set_listing_type(RentType.STUDENT_ACCOMMODATION)
daft.set_university(University.UCD)
daft.set_student_accommodation_type(StudentAccommodationType.ROOMS_TO_SHARE)
daft.set_min_price(850)
daft.set_max_price(1000)
daft.set_sort_by(SortType.PRICE)
daft.set_sort_order(SortOrder.ASCENDING)
daft.set_offset(offset)
listings = daft.search()

for listing in listings:
print(listing.price)
print(listing.formalised_address)
print(listing.daft_link)

```

## Documentation

The documentation has been created using [mkdocs](http://www.mkdocs.org/) and the [mkdocs material theme](https://squidfunk.github.io/mkdocs-material/). To update the documentation, clone the repository and edit the markdown files in the docs/ directory.

To view your changes, run:

``` bash
mkdocs serve
```


To build and publish the documentation, run:

``` bash
sh deploy_docs.sh "Updating documentation"
```

## Tests

The Python unittest module contains its own test discovery function, which you can run from the command line:

```
python -m unittest discover tests/
```

## Contributing

- Fork the project and clone locally.
- Create a new branch for what you're going to work on.
- Push to your origin repository.
- Create a new pull request in GitHub.


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