parseval 1.0.0

parseval is a data validation tool for python. It provides numerous API to parse and validate data for all native data-types. it handles data on atomic level and focuses on validation part primarily.

parseval: A pythonic data validator

parseval is a data validation tool for python. It provides numerous API to parse and validate data for all native data-types. it handles data on atomic level and focuses on validation part primarily.

Currently parseval supports following data types:

• String
• Integer /Long
• Numeric/Float
• Boolean
• Date
• DateTime

The library will be updated in future to support more native data types and some complex types. Users can also create their own parser class just by inheriting the FieldParser class, but they have to follow build design pattern, like it is done in the existing parsers.

How to get it?

The simplest of them all, run following pip install command inside your virtual environment or wherever you like:

pip install parseval

Voila!!

API reference: https://parseval.readthedocs.io/en/latest/

Who will be benefited?

Any user who handle raw source data and wants to be absolutely sure about the data format.

Here are some use cases:

• ETL process (keep in mind data read is not part of the library)
• Data scraping and machine learning data collection
• Data quality assurance (maximum, minimum allowed value, Null check, custom check)
• Validating data from any ORM/CRM systems etc.

What to expect?

parseval is built to validate one value at a time(not an entire file at a single go), which gives the user extreme flexibility. Theoretically, user can validate any data (structured, semi structured and unstructured) using the library.

As an add-on feature this library also has a built in Parser class which can handle following data collections TextIO, list of json and list of rows, we will discuss about the usage in detail in later sections.

The library is also capable of validating slice of data, which makes it absolutely trivial to parse fixed-width rows. One regex pattern check API also comes as built-in feature.

How to use?

Now the fun part. We will first check the available features. Then we will go through the actual parsing & validation of atomic data. We will also see how the built in parser API can parse and validate entire data-collection. Example modules that are available in the repository are highly recommended.

Features

Current version comes with following six types of parsers:

• FieldParser - the parser to handle data which has no strict type specification
• StringParser - the parser to handle String type data
• FloatParser - the parser to handle Numeric/Float type data
• IntegerParser - the parser to handle Integer type data
• BooleanParser - the parser to handle Boolean type data
• DatetimeParser - the parser to handle Date and Timestamp type data
• ConstantParser - the parser which always returns a specified constant value, mostly used in data-collection parsing

Each of these parsers comes with some common validations. Some parsers come with specific validations also. Following are all available validations.

	FieldParser	StringParser	FloatParser	IntegerParser	BooleanParser	DatetimeParser	ConstantParser	Remarks
not_null	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:	:heavy_check_mark:	:x:	Checks if the input data is not null
value_set	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:	:heavy_check_mark:	:x:	Checks if input data matches with any of the values of a provided list of value
max_value	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:	:heavy_check_mark:	:x:	Checks if input data is lower than or equal to Maximum allowed value
min_value	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:	:heavy_check_mark:	:x:	Checks if input data is higher than or equals to Minimum allowed value
range	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:x:	:heavy_check_mark:	:x:	Checks if input data reside in Allowed range of values
regex_match	:x:	:heavy_check_mark:	:x:	:x:	:x:	:x:	:x:	Checks if input data matches with provided pattern
change_case	:x:	:heavy_check_mark:	:x:	:x:	:x:	:x:	:x:	Returns data with altered case, Not a validator
convert	:x:	:x:	:x:	:x:	:x:	:heavy_check_mark:	:x:	Returns data in desired format, Not a validator

Apart from these APIs user can use add_func API of any parser to add their custom validation/conversion function, given that the function always returns the same data or processed data. We will see one example related to that also.

Atomic value parsing

> Principle: Now based on the data in hand, first choose a parser. Then apply the validation (not_null, max_val etc) as per requirements.

> Scenario: Built a parser for string input data that is not null and starts with 'DEMO_' string.

> Solution:
The process contains two trivial steps,

1. Define the parser based on requirement
2. Build the parser
3. Pass the value through the parser

First let's build the parser,

>>> from parseval.parser import StringParser  
>>> p_def = StringParser().not_null().regex_match(r'DEMO\_.*')  

• StringParser() part will initialize the parser,
• not_null(), regex_match() are validators

Then we will build the parser:

>>> p_func = p_def.build()  # p_func = p_def()

• build() API needs to be applied on each the to build the validation functions to be applied on the data. Or the parser object can be called directly, which will have same effect.

Now we will pass the input to the parser to get validated/parsed/converted data,

>>> input_data = "DEMO_DATA"  
>>> ret = p_func(input_data)  
>>> print(ret)  
'DEMO_DATA'  

> Special features,

Controlling Output Type
All the parser's come with trivial tendency to convert the input data to the type of parser it is. Meaning IntegerParser will always try to convert the input data to int type and produce the output, which kind of makes sense. But talking out of experience, some time we just want to check whether the data is compatible to be integer, we might not want to change it right away. Hence all the parsers accept one parameter enforce_type, which can be set to False to achieve exactly that. But keep in mind enforce_type is by-default set to True, hence if user don't disable it explicitly, parsers will change the type of the data:

>>> from parseval.parser import IntegerParser

>>> input = '13' ## string data

>>> p_def = IntegerParser().build() ## Building right away

>>> print(p_func(input))

13 ----------> Data is converted to Integer type while parsing

>>> p_def = IntegerParser(enforce_type=False).build()

>>> print(p_func(input))

'13' ----------> Data is still String type even after parsing

Note: By default the parsers change the type of data because it's trivial that an IntegerParser will return Integer.

Adding custom validation
Let's say user want's to check whether the input number is even or odd, if the number is odd then return 0. First we define the function encapsulating the logic:

>>> def odd_even_handler(data: int):

>>>        return data if data % 2 == 0 else 0

Then add this function to parser object:

>>> from parseval.parser import IntegerParser

>>> p_def = IntegerParser().not_null().add_func(odd_even_handler)

>>> p_func = p_def()

>>> odd_input = 13

>>> even_input = 12

>>> print(p_func(odd_input))

0

>>> print(p_func(even_input))

12

Note: Accept only one argument(input data) in the custom function and remember to return validated/parsed/converted data.

Parsing data collections (TextIO, List of rows or json)

> Principle: Create an object of parseval.Parser class by providing expected structure of the data (we call it a schema) and call parser method of that object and pass the schema and data collection to get the parsed valid rows in return.

> Scenario: Parse the data of a file containing 3 columns.

> Solution:
The process contains three simple steps,

1. Define a schema of parsers
2. Create Parser object by passing the schema. There is a way to set error threshold using stop_on_error parameter while creating the object. Please check API documents. Moreover row delimiter must be provided at this stage only for delimited files.
3. Call parse method of that object and pass the data collection.

First let's built the schema, schema structure must be list of tuples, tuples will hold the column name as first element and the parser definition as second element. The sequence of list should match the column list in a record. We will create one file to use as a source file also:

>>> from parseval.parser import StringParser, IntegerParser,DatetimeParser
>>> from parseval.parser import Parser  
>>> with open('some_file.txt', 'w') as sf:  
>>>       sf.writelines('1|MAEVE WILEY|19911024')  
>>>       sf.writelines('2|OTIS MILLBURN|19920314')  
>>> schema = [('id', IntegerParser(enforce_type=False).not_null()), ('name', StringParser(enforce_type=False).not_null()), ('dob', DatetimeParser(formats=['%Y%m%d'], enforce_type=False).convert('%Y/%m/%d'))]

Now, we will create an object of Parser class. Notice, apart from schema we are providing some more parameters, to know the functionality of those parameters in depth, please visit the API reference link mentioned above.

>>> parser_obj = Parser(schema=schema,  
 input_row_format = "delimited", parsed_row_format = "dict", input_row_sep = "|", stop_on_error=0 )

Now that we have the schema and the parsed object, we can parse the data:

>>> with open('some_file.txt', 'r') as sf:  
>>>    parsed_data = parser_obj.parse(sf)  
>>> for l in parsed_data:  
>>>    print(l)  
{'id': 1, 'name': 'MAEVE WILEY', 'dob': '1991/10/24'}  
{'id': 2, 'name': 'OTIS MILLBURN', 'dob': '1992/03/14'}  

Parser takes any kind of iterator as input data wrapper, provided that the wrapper returns one row at a time while looping. The data wrappers can be anything like File I/O Wrapper, List, Generator Object etc etc. It accepts data in multiple formats also, which can be tweaked using input_row_format parameter while creating the Parser object. Supported formats are:

• delimited: Delimited Lines in String format (input_row_sep parameter can be used to specify the delimiter, by-default it is |)
• fixed-width: Fixed-width Lines in String format
• json: Python dictionary object or Json data

Naturally parser supports multiple output data formats also, but keep in mind it will always return an generator object encapsulating the output rows. Supported output formats are:

• delimited: Delimited Lines in String format (parsed_row_sep parameter must be used to specify the delimiter)
• fixed-width: Fixed-width Lines in String format, Supported only if the input_row_format is fixed-width type.
• dict: Python dictionary object, keys will the column names in the provided schema.
• json: Json data, Supported only if the input_row_format is json type and the input data is Json data (Python Dict is also not supported). This constraint is to make sure the serialize-ability of the data.

Note Again: Irrespective of the output data format, the rows/lines will always be in wrapped in Python Generator object.

> Special features,

Parsing Fixed-Width dataset
If the source data rows are not delimited like previous case, instead it is fixed width, then use start and end parameter provided in each parser to mention start and end position for each column while creating the schema, and mention input_row_format as fixed-width. Note input_row_sep parameter has no effect in this scenario. Rest of the processes are same. Please checkout following code snippet which handled the act same data in fixed-width format.

>>> from parseval.parser import StringParser, IntegerParser,DatetimeParser, Parser  
>>> with open('some_file.txt', 'w') as sf:  
>>>       sf.writelines('1MAEVE WILEY  19911024')  
>>>       sf.writelines('2OTIS MILLBURN19920314')  
>>> schema = [  
 ('id', IntegerParser(start=1, end=1, enforce_type=False).not_null()), ('name', StringParser(start=2, end=14, enforce_type=False).not_null().regex_match(r'DEMO\_.*')), ('dob', DatetimeParser(start=15, end=22, formats='%Y%m%d', enforce_type=False).convert('%Y/%m/%d'))]  
>>> parser_obj = Parser(schema=schema,  
 input_row_format = "delimited", parsed_row_format = "json", input_row_sep = "|", stop_on_error=0 )>>> with open('some_file.txt', 'r') as sf:  
>>>    parsed_data = parser_obj.parse(sf)  
>>> for l in parsed_data:  
>>>    print(l)  
{'id': 1, 'name': 'MAEVE WILEY', 'dob': '1991/10/24'}  
{'id': 2, 'name': 'OTIS MILLBURN', 'dob': '1992/03/14'}  

Note: start and end are position of the characters(starting with 1, not 0) in the row. Moreover, both positions are included while slicing.

P.S.: It is highly recommended to checkout the example codes provided in the repository to understand the usage more clearly.

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That's all from my end. Hope you find the library useful in your daily data engineering. Please reach out for any queries or suggestion. Feel free to use and enrich the code. I am always avaiable at [e-mail] and **linkedin.com/in/saumalya-sarkar-b3712817b

#Happy Parsing!!