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data-expectations-1.2.0
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
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Are your data meeting all your expecations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | data-expectations-1.2.0 |
| نام | data-expectations |
| نسخه کتابخانه | 1.2.0 |
| نگهدارنده | ['Joocer'] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/joocer/data_expectations |
| آدرس اینترنتی | https://pypi.org/project/data-expectations/ |
| مجوز | - |
<img src="icon.png" height="92px" />
## Data Expectations
_Are your data meeting your expectations?_
----
[](https://github.com/joocer/data_expectations/blob/main/LICENSE)
[](https://github.com/joocer/data_expectations/actions/workflows/regression_suite.yaml)
[](https://github.com/joocer/data_expectations/actions/workflows/static_analysis.yml)
[](https://codecov.io/gh/joocer/data_expectations)
[](https://pepy.tech/project/data-expectations)
[](https://github.com/psf/black)
[](https://pypi.org/project/data-expectations/)
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fjoocer%2Fdata_expectations?ref=badge_shield)
Data Expectations is a Python library which takes a delarative approach to asserting qualities of your datasets. Instead of tests like `is_sorted` to determine if a column is ordered, the expectation is `column_values_are_increasing`. Most of the time you don't need to know _how_ it got like that, you are only interested _what_ the data looks like.
Expectations can be used alongside, or in place of a schema validator, however Expectations is intended to perform validation of the data in a dataset, not just the structure of a table. Records should be a Python dictionary (or dictionary-like object) and can be processed one-by-one, or against an entire list of dictionaries.
[Data Expectations](https://github.com/joocer/data_expectations) was inspired by the great [Great Expectations](https://github.com/great-expectations/great_expectations) library, but I wanted something which was easier to quickly set up and run. Data Expectations can do less, but it does it with a fraction of the effort. Data Expectations was written to run as a step in data processing pipelines, testing the data before it is committed to the warehouse.
## Provided Expectations
- **expect_column_to_exist** (column)
- **expect_column_names_to_match_set** (columns, ignore_excess:true)
- **expect_column_values_to_not_be_null** (column)
- **expect_column_values_to_be_of_type** (column, expected_type, ignore_nulls:true)
- **expect_column_values_to_be_in_type_list** (column, type_list, ignore_nulls:true)
- **expect_column_values_to_be_more_than** (column, threshold, ignore_nulls:true)
- **expect_column_values_to_be_less_than** (column, threshold, ignore_nulls:true)
- **expect_column_values_to_be_between** (column, maximum, minimum, ignore_nulls:true)
- **expect_column_values_to_be_increasing** (column, ignore_nulls:true)
- **expect_column_values_to_be_decreasing** (column, ignore_nulls:true)
- **expect_column_values_to_be_in_set** (column, symbols, ignore_nulls:true)
- **expect_column_values_to_match_regex** (column, regex, ignore_nulls:true)
- **expect_column_values_to_match_like** (column, like, ignore_nulls:true)
- **expect_column_values_length_to_be_be** (column, length, ignore_nulls:true)
- **expect_column_values_length_to_be_between** (column, maximum, minimum, ignore_nulls:true)
## Install
~~~bash
pip install data_expectations
~~~
Data Expectations has no external dependencies, can be used ad hoc and in-the-moment without complex set up.
## Example Usage
~~~python
import data_expectations as de
TEST_DATA = {"name":"charles","age":12}
set_of_expectations = [
{"expectation": "expect_column_to_exist", "column": "name"},
{"expectation": "expect_column_to_exist", "column": "age"},
{"expectation": "expect_column_values_to_be_between", "column": "age", "minimum": 0, "maximum": 120},
]
expectations = de.Expectations(set_of_expectations)
try:
de.evaluate_record(expectations, TEST_DATA)
except de.errors.ExpectationNotMetError:
print("Data Didn't Meet Expectations")
~~~
نحوه نصب
نصب پکیج whl data-expectations-1.2.0:
pip install data-expectations-1.2.0.whl
نصب پکیج tar.gz data-expectations-1.2.0:
pip install data-expectations-1.2.0.tar.gz