Fluxify
A Python package that eases the process of retrieving, organizing and altering data.
Required packages
- pandas
- imperium
- ijson
Installation
pip install fluxifyMain classes
fluxify.mapper.Mapper
This class is used read and processing fast files with small amounts of data that can be loaded into memory.
fluxify.lazy_mapper.LazyMapper
You've probable guessed it, this class is used to iterate on large files of data wether it is of format CSV, JSON or XML.
Usage
Retrieve data from a simple CSV file
id,brand,price,state,published_at
938,Xaomi,390.90,used,2020-01-03 12:32:29
04593,iPhone,1299.90,new,2020-01-02 09:48:12
Mapper implementation
from fluxify.mapper import Mapper
import yaml
# Could also be loaded from a file
yamlmapping = """
brand:
col: 1
price:
col: 2
state:
col: 3
publish_date:
col: 4
transformations:
- { transformer: 'date', in_format: '%Y-%m-%d %H:%M:%S', out_format: '%H:%M %d/%m/%Y' }
is_new:
conditions:
-
condition: "subject['state'] == 'new'"
returnOnSuccess: True
returnOnFail: False
"""
Map = yaml.load(yamlmapping, Loader=yaml.FullLoader)
mapper = Mapper(_type='csv')
data = mapper.map('path/to/csvfile.csv', Map)
print(data)Output
[
{
'brand': 'Xaomi',
'price': '390.90',
'state': 'used',
'published_date': '12:32 03/01/2020'
'is_new': False
},
{
'brand': 'iPhone',
'price': '1299.90',
'state': 'new',
'published_date': '09:48 02/01/2020'
'is_new': True
}
]LazyMapper implementation
The LazyMapper does not return all the mapped data at the end, instead it maps the data in small sizes that you can specify in order to not max out the memory.
from fluxify.lazy_mapper import LazyMapper
import yaml
# Could also be loaded from a file
yamlmapping = """
brand:
col: 1
price:
col: 2
state:
col: 3
publish_date:
col: 4
transformations:
- { transformer: 'date', in_format: '%Y-%m-%d %H:%M:%S', out_format: '%H:%M %d/%m/%Y' }
is_new:
conditions:
-
condition: "subject['state'] == 'new'"
returnOnSuccess: True
returnOnFail: False
"""
Map = yaml.load(yamlmapping, Loader=yaml.FullLoader)
mapper = LazyMapper(_type='csv', error_tolerance=True, bulksize=500)
mapper.map('path/to/csvfile.csv', Map)
def some_callback(results):
for item in results:
pass # Perform some action
mapper.set_callback(some_callback)
mapper.map('path/to/csvfile.csv', Map)As you can see, in this example the mapper will call the callback function every time it accumulates 500 mapped items.
Supported formats
| Format | CSV | JSON | XML | TXT |
|---|---|---|---|---|
| Supported | YES | YES | YES | NO |
Transformers
Fluxify has built-in transformers that can alter/modify the data.
| Function | Arguments | Description |
|---|---|---|
| number | stringvalue | Parses a string to an integer or float value |
| split | delimiter, index | Splits a string into parts with a delimiter and returns the splitted result if the index argument is not defined. |
| date | in_format, out_format | Let's you format a date string to the desired format. |
| replace | search, new | Replaces the search value with new value from string |
| boolean | No arguments | Parses a string to Boolean if the string contains [true |
| equipments_from_string | delimiter | Custom usage |
| options_from_string | delimiter | Custom usage |
Exceptions
Fluxify has different Exception classes for different reasons
They reside in the exceptions sub-package fluxify.exceptions
| Class | Arguments | Description |
|---|---|---|
| ArgumentNotFoundException | message | This exception is raised whenever a argument is not found. |
| InvalidArgumentException | message | This exception is raised when a passed parameter/argument is invalid. |
| ConditionNotFoundException | message | This exception is raised when the "condition" key is not defined in the mapping. |
| UnsupportedTransformerException | message | This exception is raised when a transformer other than the ones defined above, is used. |
