| Name | cuallee JSON |
| Version |
0.15.2
JSON |
| download |
| home_page | None |
| Summary | Python library for data validation on DataFrame APIs including Snowflake/Snowpark, Apache/PySpark and Pandas/DataFrame. |
| upload_time | 2024-12-14 09:13:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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# cuallee
[](https://badge.fury.io/py/cuallee)
[](https://github.com/canimus/cuallee/actions/workflows/ci.yml)
[](https://codecov.io/gh/canimus/cuallee)
[](https://pypi.org/project/cuallee/)
[](https://opensource.org/licenses/Apache-2.0)
[](https://joss.theoj.org/papers/db01d4f5a02a319fe2b4c49f68e3f859)
[](https://doi.org/10.5281/zenodo.12206787)
<div align="center">
<img src="https://raw.githubusercontent.com/canimus/cuallee/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/cuallee.png" width="250px" style="padding: 60px 20px" align="right"/>
</div>
Meaning `good` in Aztec ([Nahuatl](https://nahuatl.wired-humanities.org/content/cualli-0)), _pronounced: QUAL-E_
This library provides an intuitive `API` to describe data quality `checks` initially just for `PySpark` dataframes `v3.3.0`. And extended to `pandas`, `snowpark`, `duckdb`, `daft` and more.
It is a replacement written in pure `python` of the `pydeequ` framework.
I gave up in _deequ_ as after extensive use, the API is not user-friendly, the Python Callback servers produce additional costs in our compute clusters, and the lack of support to the newest version of PySpark.
As result `cuallee` was born
This implementation goes in hand with the latest API from PySpark and uses the `Observation` API to collect metrics
at the lower cost of computation.
When benchmarking against pydeequ, `cuallee` uses circa <3k java classes underneath and **remarkably** less memory.
## Support
`cuallee` is the data quality framework truly dataframe agnostic.
Provider | API | Versions
------- | ----------- | ------
| `snowpark` | `1.11.1`, `1.4.0`
| `pyspark` & `spark-connect` |`3.5.x`, `3.4.0`, `3.3.x`, `3.2.x`
| `bigquery` | `3.4.1`
| `pandas`| `2.0.2`, `1.5.x`, `1.4.x`
|`duckdb` | `1.0.0`, ~~`0.10.2`~~,~~`0.9.2`~~,~~`0.8.0`~~
|`polars`| `1.0.0`, ~~`0.19.6`~~
|`daft`| `0.2.24`, ~~`0.2.19`~~
<sub>Logos are trademarks of their own brands.</sub>
## Install
```bash
pip install cuallee
```
## Checks
The most common checks for data integrity validations are `completeness` and `uniqueness` an example of this dimensions shown below:
```python
from cuallee import Check, CheckLevel # WARN:0, ERR: 1
# Nulls on column Id
check = Check(CheckLevel.WARNING, "Completeness")
(
check
.is_complete("id")
.is_unique("id")
.validate(df)
).show() # Returns a pyspark.sql.DataFrame
```
>[!IMPORTANT]
> A new version of the `validate` output is currently under construction.
### Dates
Perhaps one of the most useful features of `cuallee` is its extensive number of checks for `Date` and `Timestamp` values. Including, validation of ranges, set operations like inclusion, or even a verification that confirms `continuity on dates` using the `is_daily` check function.
```python
# Unique values on id
check = Check(CheckLevel.WARNING, "CheckIsBetweenDates")
df = spark.sql(
"""
SELECT
explode(
sequence(
to_date('2022-01-01'),
to_date('2022-01-10'),
interval 1 day)) as date
""")
assert (
check.is_between("date", ("2022-01-01", "2022-01-10"))
.validate(df)
.first()
.status == "PASS"
)
```
### Membership
Other common test is the validation of `list of values` as part of the multiple integrity checks required for better quality data.
```python
df = spark.createDataFrame([[1, 10], [2, 15], [3, 17]], ["ID", "value"])
check = Check(CheckLevel.WARNING, "is_contained_in_number_test")
check.is_contained_in("value", (10, 15, 20, 25)).validate(df)
```
### Regular Expressions
When it comes to the flexibility of matching, regular expressions are always to the rescue. `cuallee` makes use of the regular expressions to validate that fields of type `String` conform to specific patterns.
```python
df = spark.createDataFrame([[1, "is_blue"], [2, "has_hat"], [3, "is_smart"]], ["ID", "desc"])
check = Check(CheckLevel.WARNING, "has_pattern_test")
check.has_pattern("desc", r"^is.*t$") # only match is_smart 33% of rows.
check.validate(df).first().status == "FAIL"
```
### Anomalies
Statistical tests are a great aid for verifying anomalies on data. Here an example that shows that will `PASS` only when `40%` of data is inside the interquartile range
```python
df = spark.range(10)
check = Check(CheckLevel.WARNING, "IQR_Test")
check.is_inside_interquartile_range("id", pct=0.4)
check.validate(df).first().status == "PASS"
+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+
|id |timestamp |check|level |column|rule |value|rows|violations|pass_rate|pass_threshold|status|
+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+
|1 |2022-10-19 00:09:39|IQR |WARNING|id |is_inside_interquartile_range|10000|10 |4 |0.6 |0.4 |PASS |
+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+
```
### Workflows (Process Mining)
Besides the common `citizen-like` checks, `cuallee` offers out-of-the-box real-life checks. For example, suppose that you are working __SalesForce__ or __SAP__ environment. Very likely your business processes will be driven by a lifecycle:
- `Order-To-Cash`
- `Request-To-Pay`
- `Inventory-Logistics-Delivery`
- Others.
In this scenario, `cuallee` offers the ability that the sequence of events registered over time, are according to a sequence of events, like the example below:
```python
import pyspark.sql.functions as F
from cuallee import Check, CheckLevel
data = pd.DataFrame({
"name":["herminio", "herminio", "virginie", "virginie"],
"event":["new","active", "new", "active"],
"date": ["2022-01-01", "2022-01-02", "2022-01-03", "2022-02-04"]}
)
df = spark.createDataFrame(data).withColumn("date", F.to_date("date"))
# Cuallee Process Mining
# Testing that all edges on workflows
check = Check(CheckLevel.WARNING, "WorkflowViolations")
# Validate that 50% of data goes from new => active
check.has_workflow("name", "event", "date", [("new", "active")], pct=0.5)
check.validate(df).show(truncate=False)
+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+
|id |timestamp |check |level |column |rule |value |rows|violations|pass_rate|pass_threshold|status|
+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+
|1 |2022-11-07 23:08:50|WorkflowViolations|WARNING|('name', 'event', 'date')|has_workflow|(('new', 'active'),)|4 |2.0 |0.5 |0.5 |PASS |
+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+
```
### Assertions
`[2024-09-28]` ✨ __New feature!__ Return a simple `true|false` as a unified result for your `check`
```python
import pandas as pd
from cuallee import Check
df = pd.DataFrame({"X":[1,2,3]})
# .ok(dataframe) method of a check will call validate and then verify that all rules are PASS
assert Check().is_complete("X").ok(df)
```
### Controls
Simplify the entire validation of a dataframe in a particular dimension.
```python
import pandas as pd
from cuallee import Control
df = pd.DataFrame({"X":[1,2,3], "Y": [10,20,30]})
# Checks all columns in dataframe for using is_complete check
Control.completeness(df)
```
### `cuallee` __VS__ `pydeequ`
In the `test` folder there are `docker` containers with the requirements to match the tests. Also a `perftest.py` available at the root folder for interests.
```
# 1000 rules / # of seconds
cuallee: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 162.00
pydeequ: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 322.00
```
## Catalogue
Check | Description | DataType
------- | ----------- | ----
`is_complete` | Zero `nulls` | _agnostic_
`is_unique` | Zero `duplicates` | _agnostic_
`is_primary_key` | Zero `duplicates` | _agnostic_
`are_complete` | Zero `nulls` on group of columns | _agnostic_
`are_unique` | Composite primary key check | _agnostic_
`is_composite_key` | Zero duplicates on multiple columns | _agnostic_
`is_greater_than` | `col > x` | _numeric_
`is_positive` | `col > 0` | _numeric_
`is_negative` | `col < 0` | _numeric_
`is_greater_or_equal_than` | `col >= x` | _numeric_
`is_less_than` | `col < x` | _numeric_
`is_less_or_equal_than` | `col <= x` | _numeric_
`is_equal_than` | `col == x` | _numeric_
`is_contained_in` | `col in [a, b, c, ...]` | _agnostic_
`is_in` | Alias of `is_contained_in` | _agnostic_
`not_contained_in` | `col not in [a, b, c, ...]` | _agnostic_
`not_in` | Alias of `not_contained_in` | _agnostic_
`is_between` | `a <= col <= b` | _numeric, date_
`has_pattern` | Matching a pattern defined as a `regex` | _string_
`is_legit` | String not null & not empty `^\S$` | _string_
`has_min` | `min(col) == x` | _numeric_
`has_max` | `max(col) == x` | _numeric_
`has_std` | `σ(col) == x` | _numeric_
`has_mean` | `μ(col) == x` | _numeric_
`has_sum` | `Σ(col) == x` | _numeric_
`has_percentile` | `%(col) == x` | _numeric_
`has_cardinality` | `count(distinct(col)) == x` | _agnostic_
`has_infogain` | `count(distinct(col)) > 1` | _agnostic_
`has_max_by` | A utilitary predicate for `max(col_a) == x for max(col_b)` | _agnostic_
`has_min_by` | A utilitary predicate for `min(col_a) == x for min(col_b)` | _agnostic_
`has_correlation` | Finds correlation between `0..1` on `corr(col_a, col_b)` | _numeric_
`has_entropy` | Calculates the entropy of a column `entropy(col) == x` for classification problems | _numeric_
`is_inside_interquartile_range` | Verifies column values reside inside limits of interquartile range `Q1 <= col <= Q3` used on anomalies. | _numeric_
`is_in_millions` | `col >= 1e6` | _numeric_
`is_in_billions` | `col >= 1e9` | _numeric_
`is_t_minus_1` | For date fields confirms 1 day ago `t-1` | _date_
`is_t_minus_2` | For date fields confirms 2 days ago `t-2` | _date_
`is_t_minus_3` | For date fields confirms 3 days ago `t-3` | _date_
`is_t_minus_n` | For date fields confirms n days ago `t-n` | _date_
`is_today` | For date fields confirms day is current date `t-0` | _date_
`is_yesterday` | For date fields confirms 1 day ago `t-1` | _date_
`is_on_weekday` | For date fields confirms day is between `Mon-Fri` | _date_
`is_on_weekend` | For date fields confirms day is between `Sat-Sun` | _date_
`is_on_monday` | For date fields confirms day is `Mon` | _date_
`is_on_tuesday` | For date fields confirms day is `Tue` | _date_
`is_on_wednesday` | For date fields confirms day is `Wed` | _date_
`is_on_thursday` | For date fields confirms day is `Thu` | _date_
`is_on_friday` | For date fields confirms day is `Fri` | _date_
`is_on_saturday` | For date fields confirms day is `Sat` | _date_
`is_on_sunday` | For date fields confirms day is `Sun` | _date_
`is_on_schedule` | For date fields confirms time windows i.e. `9:00 - 17:00` | _timestamp_
`is_daily` | Can verify daily continuity on date fields by default. `[2,3,4,5,6]` which represents `Mon-Fri` in PySpark. However new schedules can be used for custom date continuity | _date_
`has_workflow` | Adjacency matrix validation on `3-column` graph, based on `group`, `event`, `order` columns. | _agnostic_
`is_custom` | User-defined custom `function` applied to dataframe for row-based validation. | _agnostic_
`satisfies` | An open `SQL expression` builder to construct custom checks | _agnostic_
`validate` | The ultimate transformation of a check with a `dataframe` input for validation | _agnostic_
## Controls `pyspark`
Check | Description | DataType
------- | ----------- | ----
`completeness` | Zero `nulls` | _agnostic_
`information` | Zero nulls `and` cardinality > 1 | _agnostic_
`intelligence` | Zero nulls, zero empty strings and cardinality > 1 | _agnostic_
`percentage_fill` | `% rows` not empty | _agnostic_
`percentage_empty` | `% rows` empty | _agnostic_
## ISO Standard
A new module has been incorporated in `cuallee==0.4.0` which allows the verification of International Standard Organization columns in data frames. Simply access the `check.iso` interface to add the set of checks as shown below.
Check | Description | DataType
------- | ----------- | ----
`iso_4217` | currency compliant `ccy` | _string_
`iso_3166` | country compliant `country` | _string_
```python
df = spark.createDataFrame([[1, "USD"], [2, "MXN"], [3, "CAD"], [4, "EUR"], [5, "CHF"]], ["id", "ccy"])
check = Check(CheckLevel.WARNING, "ISO Compliant")
check.iso.iso_4217("ccy")
check.validate(df).show()
+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+
| id| timestamp| check| level|column| rule| value|rows|violations|pass_rate|pass_threshold|status|
+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+
| 1|2023-05-14 18:28:02|ISO Compliant|WARNING| ccy|is_contained_in|{'BHD', 'CRC', 'M...| 5| 0.0| 1.0| 1.0| PASS|
+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+
```
## Snowflake Connection
In order to establish a connection to your SnowFlake account `cuallee` relies in the following environment variables to be avaialble in your environment:
- `SF_ACCOUNT`
- `SF_USER`
- `SF_PASSWORD`
- `SF_ROLE`
- `SF_WAREHOUSE`
- `SF_DATABASE`
- `SF_SCHEMA`
## Spark Connect
Just add the environment variable `SPARK_REMOTE` to your remote session, then `cuallee` will connect using
```python
spark_connect = SparkSession.builder.remote(os.getenv("SPARK_REMOTE")).getOrCreate()
```
and convert all checks to `select` as opposed to `Observation` API compute instructions.
## Databricks Connection
By default `cuallee` will search for a SparkSession available in the `globals` so there is literally no need to ~~`SparkSession.builder`~~. When working in a local environment it will automatically search for an available session, or start one.
## DuckDB
For testing on `duckdb` simply pass your table name to your check _et voilà_
```python
import duckdb
conn = duckdb.connect(":memory:")
check = Check(CheckLevel.WARNING, "DuckDB", table_name="temp/taxi/*.parquet")
check.is_complete("VendorID")
check.is_complete("tpep_pickup_datetime")
check.validate(conn)
id timestamp check level column rule value rows violations pass_rate pass_threshold status
0 1 2022-10-31 23:15:06 test WARNING VendorID is_complete N/A 19817583 0.0 1.0 1.0 PASS
1 2 2022-10-31 23:15:06 test WARNING tpep_pickup_datetime is_complete N/A 19817583 0.0 1.0 1.0 PASS
```
## Roadmap
`100%` data frame agnostic implementation of data quality checks.
Define once, `run everywhere`
- ~~[x] PySpark 3.5.0~~
- ~~[x] PySpark 3.4.0~~
- ~~[x] PySpark 3.3.0~~
- ~~[x] PySpark 3.2.x~~
- ~~[x] Snowpark DataFrame~~
- ~~[x] Pandas DataFrame~~
- ~~[x] DuckDB Tables~~
- ~~[x] BigQuery Client~~
- ~~[x] Polars DataFrame~~
- ~~[*] Dagster Integration~~
- ~~[x] Spark Connect~~
- ~~[x] Daft~~
- [-] PDF Report
- [ ] Metadata check
- [ ] Help us in a discussion?
Whilst expanding the functionality feels a bit as an overkill because you most likely can connect `spark` via its drivers to whatever `DBMS` of your choice.
In the desire to make it even more `user-friendly` we are aiming to make `cuallee` portable to all the providers above.
## Authors
- [canimus](https://github.com/canimus) / Herminio Vazquez / 🇲🇽
- [vestalisvirginis](https://github.com/vestalisvirginis) / Virginie Grosboillot / 🇫🇷
## Contributors
<a href="https://github.com/canimus/cuallee/graphs/contributors">
<img src="https://contrib.rocks/image?repo=canimus/cuallee" />
</a>
### Guidelines
- [Contributing to cuallee](CONTRIBUTING.md)
## Documentation
- [https://canimus.github.io/cuallee/](https://canimus.github.io/cuallee/)
## Paper
`cuallee` has been published in the Journal of Open Source Software
```
Vazquez et al., (2024). cuallee: A Python package for data quality checks across multiple DataFrame APIs. Journal of Open Source Software, 9(98), 6684, https://doi.org/10.21105/joss.06684
```
If you use `cuallee` please consider citing this work. [Citation](CITATION.cff)
## License
Apache License 2.0
Free for commercial use, modification, distribution, patent use, private use.
Just preserve the copyright and license.
> Made with ❤️ in Utrecht 🇳🇱<br/>
> Maintained over ⌛ from Ljubljana 🇸🇮<br/>
> Extended 🚀 by contributions all over the 🌎
Raw data
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"author_email": "Herminio Vazquez <canimus@gmail.com>, Virginie Grosboillot <vestalisvirginis@gmail.com>",
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"description": "# cuallee\n\n[](https://badge.fury.io/py/cuallee)\n[](https://github.com/canimus/cuallee/actions/workflows/ci.yml)\n[](https://codecov.io/gh/canimus/cuallee)\n[](https://pypi.org/project/cuallee/)\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://joss.theoj.org/papers/db01d4f5a02a319fe2b4c49f68e3f859)\n[](https://doi.org/10.5281/zenodo.12206787)\n\n<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/canimus/cuallee/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/cuallee.png\" width=\"250px\" style=\"padding: 60px 20px\" align=\"right\"/>\n</div>\n\nMeaning `good` in Aztec ([Nahuatl](https://nahuatl.wired-humanities.org/content/cualli-0)), _pronounced: QUAL-E_\n\nThis library provides an intuitive `API` to describe data quality `checks` initially just for `PySpark` dataframes `v3.3.0`. And extended to `pandas`, `snowpark`, `duckdb`, `daft` and more.\nIt is a replacement written in pure `python` of the `pydeequ` framework.\n\nI gave up in _deequ_ as after extensive use, the API is not user-friendly, the Python Callback servers produce additional costs in our compute clusters, and the lack of support to the newest version of PySpark.\n\nAs result `cuallee` was born\n\nThis implementation goes in hand with the latest API from PySpark and uses the `Observation` API to collect metrics\nat the lower cost of computation.\nWhen benchmarking against pydeequ, `cuallee` uses circa <3k java classes underneath and **remarkably** less memory.\n\n## Support\n\n`cuallee` is the data quality framework truly dataframe agnostic.\n\nProvider | API | Versions\n ------- | ----------- | ------\n![snowflake](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/snowflake.svg?raw=true \"Snowpark DataFrame API\")| `snowpark` | `1.11.1`, `1.4.0`\n![databricks](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/databricks.svg?raw=true \"PySpark DataFrame API\")| `pyspark` & `spark-connect` |`3.5.x`, `3.4.0`, `3.3.x`, `3.2.x`\n![bigquery](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/bigquery.png?raw=true \"BigQuery Client API\")| `bigquery` | `3.4.1`\n![pandas](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/pandas.svg?raw=true \"Pandas DataFrame API\")| `pandas`| `2.0.2`, `1.5.x`, `1.4.x`\n![duckdb](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/duckdb.png?raw=true \"DuckDB API\")|`duckdb` | `1.0.0`, ~~`0.10.2`~~,~~`0.9.2`~~,~~`0.8.0`~~\n![polars](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/polars.svg?raw=true \"Polars API\")|`polars`| `1.0.0`, ~~`0.19.6`~~\n![daft](https://github.com/canimus/cuallee/blob/fbef98f5340279fc726b369a7ce879fd67ea1d1f/logos/daft.png?raw=true \"Daft API\")|`daft`| `0.2.24`, ~~`0.2.19`~~\n\n <sub>Logos are trademarks of their own brands.</sub>\n\n\n## Install\n```bash\npip install cuallee\n```\n\n## Checks\n\nThe most common checks for data integrity validations are `completeness` and `uniqueness` an example of this dimensions shown below:\n\n```python\nfrom cuallee import Check, CheckLevel # WARN:0, ERR: 1\n\n# Nulls on column Id\ncheck = Check(CheckLevel.WARNING, \"Completeness\")\n(\n check\n .is_complete(\"id\")\n .is_unique(\"id\")\n .validate(df)\n).show() # Returns a pyspark.sql.DataFrame\n```\n\n>[!IMPORTANT]\n> A new version of the `validate` output is currently under construction.\n\n### Dates\n\nPerhaps one of the most useful features of `cuallee` is its extensive number of checks for `Date` and `Timestamp` values. Including, validation of ranges, set operations like inclusion, or even a verification that confirms `continuity on dates` using the `is_daily` check function.\n\n```python\n# Unique values on id\ncheck = Check(CheckLevel.WARNING, \"CheckIsBetweenDates\")\ndf = spark.sql(\n \"\"\"\n SELECT\n explode(\n sequence(\n to_date('2022-01-01'),\n to_date('2022-01-10'),\n interval 1 day)) as date\n \"\"\")\nassert (\n check.is_between(\"date\", (\"2022-01-01\", \"2022-01-10\"))\n .validate(df)\n .first()\n .status == \"PASS\"\n)\n```\n\n### Membership\n\nOther common test is the validation of `list of values` as part of the multiple integrity checks required for better quality data.\n\n```python\ndf = spark.createDataFrame([[1, 10], [2, 15], [3, 17]], [\"ID\", \"value\"])\ncheck = Check(CheckLevel.WARNING, \"is_contained_in_number_test\")\ncheck.is_contained_in(\"value\", (10, 15, 20, 25)).validate(df)\n```\n\n### Regular Expressions\n\nWhen it comes to the flexibility of matching, regular expressions are always to the rescue. `cuallee` makes use of the regular expressions to validate that fields of type `String` conform to specific patterns.\n\n```python\ndf = spark.createDataFrame([[1, \"is_blue\"], [2, \"has_hat\"], [3, \"is_smart\"]], [\"ID\", \"desc\"])\ncheck = Check(CheckLevel.WARNING, \"has_pattern_test\")\ncheck.has_pattern(\"desc\", r\"^is.*t$\") # only match is_smart 33% of rows.\ncheck.validate(df).first().status == \"FAIL\"\n```\n\n### Anomalies\n\nStatistical tests are a great aid for verifying anomalies on data. Here an example that shows that will `PASS` only when `40%` of data is inside the interquartile range\n\n```python\ndf = spark.range(10)\ncheck = Check(CheckLevel.WARNING, \"IQR_Test\")\ncheck.is_inside_interquartile_range(\"id\", pct=0.4)\ncheck.validate(df).first().status == \"PASS\"\n\n+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+\n|id |timestamp |check|level |column|rule |value|rows|violations|pass_rate|pass_threshold|status|\n+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+\n|1 |2022-10-19 00:09:39|IQR |WARNING|id |is_inside_interquartile_range|10000|10 |4 |0.6 |0.4 |PASS |\n+---+-------------------+-----+-------+------+-----------------------------+-----+----+----------+---------+--------------+------+\n```\n\n### Workflows (Process Mining)\nBesides the common `citizen-like` checks, `cuallee` offers out-of-the-box real-life checks. For example, suppose that you are working __SalesForce__ or __SAP__ environment. Very likely your business processes will be driven by a lifecycle:\n- `Order-To-Cash`\n- `Request-To-Pay`\n- `Inventory-Logistics-Delivery`\n- Others.\n In this scenario, `cuallee` offers the ability that the sequence of events registered over time, are according to a sequence of events, like the example below:\n\n ```python\nimport pyspark.sql.functions as F\nfrom cuallee import Check, CheckLevel\n\ndata = pd.DataFrame({\n \"name\":[\"herminio\", \"herminio\", \"virginie\", \"virginie\"],\n \"event\":[\"new\",\"active\", \"new\", \"active\"],\n \"date\": [\"2022-01-01\", \"2022-01-02\", \"2022-01-03\", \"2022-02-04\"]}\n )\ndf = spark.createDataFrame(data).withColumn(\"date\", F.to_date(\"date\"))\n\n# Cuallee Process Mining\n# Testing that all edges on workflows\ncheck = Check(CheckLevel.WARNING, \"WorkflowViolations\")\n\n# Validate that 50% of data goes from new => active\ncheck.has_workflow(\"name\", \"event\", \"date\", [(\"new\", \"active\")], pct=0.5)\ncheck.validate(df).show(truncate=False)\n\n+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+\n|id |timestamp |check |level |column |rule |value |rows|violations|pass_rate|pass_threshold|status|\n+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+\n|1 |2022-11-07 23:08:50|WorkflowViolations|WARNING|('name', 'event', 'date')|has_workflow|(('new', 'active'),)|4 |2.0 |0.5 |0.5 |PASS |\n+---+-------------------+------------------+-------+-------------------------+------------+--------------------+----+----------+---------+--------------+------+\n\n ```\n### Assertions\n`[2024-09-28]` \u2728 __New feature!__ Return a simple `true|false` as a unified result for your `check`\n```python\nimport pandas as pd\nfrom cuallee import Check\ndf = pd.DataFrame({\"X\":[1,2,3]})\n# .ok(dataframe) method of a check will call validate and then verify that all rules are PASS\nassert Check().is_complete(\"X\").ok(df)\n```\n\n\n### Controls\nSimplify the entire validation of a dataframe in a particular dimension.\n```python\nimport pandas as pd\nfrom cuallee import Control\ndf = pd.DataFrame({\"X\":[1,2,3], \"Y\": [10,20,30]})\n# Checks all columns in dataframe for using is_complete check\nControl.completeness(df)\n```\n\n### `cuallee` __VS__ `pydeequ`\nIn the `test` folder there are `docker` containers with the requirements to match the tests. Also a `perftest.py` available at the root folder for interests.\n\n```\n# 1000 rules / # of seconds\n\ncuallee: \u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 162.00\npydeequ: \u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587\u2587 322.00\n```\n\n\n## Catalogue\n\nCheck | Description | DataType\n ------- | ----------- | ----\n`is_complete` | Zero `nulls` | _agnostic_\n`is_unique` | Zero `duplicates` | _agnostic_\n`is_primary_key` | Zero `duplicates` | _agnostic_\n`are_complete` | Zero `nulls` on group of columns | _agnostic_\n`are_unique` | Composite primary key check | _agnostic_\n`is_composite_key` | Zero duplicates on multiple columns | _agnostic_\n`is_greater_than` | `col > x` | _numeric_\n`is_positive` | `col > 0` | _numeric_\n`is_negative` | `col < 0` | _numeric_\n`is_greater_or_equal_than` | `col >= x` | _numeric_\n`is_less_than` | `col < x` | _numeric_\n`is_less_or_equal_than` | `col <= x` | _numeric_\n`is_equal_than` | `col == x` | _numeric_\n`is_contained_in` | `col in [a, b, c, ...]` | _agnostic_\n`is_in` | Alias of `is_contained_in` | _agnostic_\n`not_contained_in` | `col not in [a, b, c, ...]` | _agnostic_\n`not_in` | Alias of `not_contained_in` | _agnostic_\n`is_between` | `a <= col <= b` | _numeric, date_\n`has_pattern` | Matching a pattern defined as a `regex` | _string_\n`is_legit` | String not null & not empty `^\\S$` | _string_\n`has_min` | `min(col) == x` | _numeric_\n`has_max` | `max(col) == x` | _numeric_\n`has_std` | `\u03c3(col) == x` | _numeric_\n`has_mean` | `\u03bc(col) == x` | _numeric_\n`has_sum` | `\u03a3(col) == x` | _numeric_\n`has_percentile` | `%(col) == x` | _numeric_\n`has_cardinality` | `count(distinct(col)) == x` | _agnostic_\n`has_infogain` | `count(distinct(col)) > 1` | _agnostic_\n`has_max_by` | A utilitary predicate for `max(col_a) == x for max(col_b)` | _agnostic_\n`has_min_by` | A utilitary predicate for `min(col_a) == x for min(col_b)` | _agnostic_\n`has_correlation` | Finds correlation between `0..1` on `corr(col_a, col_b)` | _numeric_\n`has_entropy` | Calculates the entropy of a column `entropy(col) == x` for classification problems | _numeric_\n`is_inside_interquartile_range` | Verifies column values reside inside limits of interquartile range `Q1 <= col <= Q3` used on anomalies. | _numeric_\n`is_in_millions` | `col >= 1e6` | _numeric_\n`is_in_billions` | `col >= 1e9` | _numeric_\n`is_t_minus_1` | For date fields confirms 1 day ago `t-1` | _date_\n`is_t_minus_2` | For date fields confirms 2 days ago `t-2` | _date_\n`is_t_minus_3` | For date fields confirms 3 days ago `t-3` | _date_\n`is_t_minus_n` | For date fields confirms n days ago `t-n` | _date_\n`is_today` | For date fields confirms day is current date `t-0` | _date_\n`is_yesterday` | For date fields confirms 1 day ago `t-1` | _date_\n`is_on_weekday` | For date fields confirms day is between `Mon-Fri` | _date_\n`is_on_weekend` | For date fields confirms day is between `Sat-Sun` | _date_\n`is_on_monday` | For date fields confirms day is `Mon` | _date_\n`is_on_tuesday` | For date fields confirms day is `Tue` | _date_\n`is_on_wednesday` | For date fields confirms day is `Wed` | _date_\n`is_on_thursday` | For date fields confirms day is `Thu` | _date_\n`is_on_friday` | For date fields confirms day is `Fri` | _date_\n`is_on_saturday` | For date fields confirms day is `Sat` | _date_\n`is_on_sunday` | For date fields confirms day is `Sun` | _date_\n`is_on_schedule` | For date fields confirms time windows i.e. `9:00 - 17:00` | _timestamp_\n`is_daily` | Can verify daily continuity on date fields by default. `[2,3,4,5,6]` which represents `Mon-Fri` in PySpark. However new schedules can be used for custom date continuity | _date_\n`has_workflow` | Adjacency matrix validation on `3-column` graph, based on `group`, `event`, `order` columns. | _agnostic_\n`is_custom` | User-defined custom `function` applied to dataframe for row-based validation. | _agnostic_\n`satisfies` | An open `SQL expression` builder to construct custom checks | _agnostic_\n`validate` | The ultimate transformation of a check with a `dataframe` input for validation | _agnostic_\n\n\n## Controls `pyspark`\n\nCheck | Description | DataType\n ------- | ----------- | ----\n`completeness` | Zero `nulls` | _agnostic_\n`information` | Zero nulls `and` cardinality > 1 | _agnostic_\n`intelligence` | Zero nulls, zero empty strings and cardinality > 1 | _agnostic_\n`percentage_fill` | `% rows` not empty | _agnostic_\n`percentage_empty` | `% rows` empty | _agnostic_\n\n\n## ISO Standard\nA new module has been incorporated in `cuallee==0.4.0` which allows the verification of International Standard Organization columns in data frames. Simply access the `check.iso` interface to add the set of checks as shown below.\n\nCheck | Description | DataType\n ------- | ----------- | ----\n`iso_4217` | currency compliant `ccy` | _string_\n`iso_3166` | country compliant `country` | _string_\n\n```python\ndf = spark.createDataFrame([[1, \"USD\"], [2, \"MXN\"], [3, \"CAD\"], [4, \"EUR\"], [5, \"CHF\"]], [\"id\", \"ccy\"])\ncheck = Check(CheckLevel.WARNING, \"ISO Compliant\")\ncheck.iso.iso_4217(\"ccy\")\ncheck.validate(df).show()\n+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+\n| id| timestamp| check| level|column| rule| value|rows|violations|pass_rate|pass_threshold|status|\n+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+\n| 1|2023-05-14 18:28:02|ISO Compliant|WARNING| ccy|is_contained_in|{'BHD', 'CRC', 'M...| 5| 0.0| 1.0| 1.0| PASS|\n+---+-------------------+-------------+-------+------+---------------+--------------------+----+----------+---------+--------------+------+\n```\n\n\n## Snowflake Connection\nIn order to establish a connection to your SnowFlake account `cuallee` relies in the following environment variables to be avaialble in your environment:\n- `SF_ACCOUNT`\n- `SF_USER`\n- `SF_PASSWORD`\n- `SF_ROLE`\n- `SF_WAREHOUSE`\n- `SF_DATABASE`\n- `SF_SCHEMA`\n\n## Spark Connect\nJust add the environment variable `SPARK_REMOTE` to your remote session, then `cuallee` will connect using\n```python\nspark_connect = SparkSession.builder.remote(os.getenv(\"SPARK_REMOTE\")).getOrCreate()\n```\nand convert all checks to `select` as opposed to `Observation` API compute instructions.\n\n\n## Databricks Connection\nBy default `cuallee` will search for a SparkSession available in the `globals` so there is literally no need to ~~`SparkSession.builder`~~. When working in a local environment it will automatically search for an available session, or start one.\n\n## DuckDB\n\nFor testing on `duckdb` simply pass your table name to your check _et voil\u00e0_\n\n```python\nimport duckdb\nconn = duckdb.connect(\":memory:\")\ncheck = Check(CheckLevel.WARNING, \"DuckDB\", table_name=\"temp/taxi/*.parquet\")\ncheck.is_complete(\"VendorID\")\ncheck.is_complete(\"tpep_pickup_datetime\")\ncheck.validate(conn)\n\n id timestamp check level column rule value rows violations pass_rate pass_threshold status\n0 1 2022-10-31 23:15:06 test WARNING VendorID is_complete N/A 19817583 0.0 1.0 1.0 PASS\n1 2 2022-10-31 23:15:06 test WARNING tpep_pickup_datetime is_complete N/A 19817583 0.0 1.0 1.0 PASS\n```\n\n## Roadmap\n\n`100%` data frame agnostic implementation of data quality checks.\nDefine once, `run everywhere`\n- ~~[x] PySpark 3.5.0~~\n- ~~[x] PySpark 3.4.0~~\n- ~~[x] PySpark 3.3.0~~\n- ~~[x] PySpark 3.2.x~~\n- ~~[x] Snowpark DataFrame~~\n- ~~[x] Pandas DataFrame~~\n- ~~[x] DuckDB Tables~~\n- ~~[x] BigQuery Client~~\n- ~~[x] Polars DataFrame~~\n- ~~[*] Dagster Integration~~\n- ~~[x] Spark Connect~~\n- ~~[x] Daft~~\n- [-] PDF Report\n- [ ] Metadata check\n- [ ] Help us in a discussion?\n\n\n\nWhilst expanding the functionality feels a bit as an overkill because you most likely can connect `spark` via its drivers to whatever `DBMS` of your choice.\nIn the desire to make it even more `user-friendly` we are aiming to make `cuallee` portable to all the providers above.\n\n## Authors\n- [canimus](https://github.com/canimus) / Herminio Vazquez / \ud83c\uddf2\ud83c\uddfd\n- [vestalisvirginis](https://github.com/vestalisvirginis) / Virginie Grosboillot / \ud83c\uddeb\ud83c\uddf7\n\n## Contributors\n<a href=\"https://github.com/canimus/cuallee/graphs/contributors\">\n <img src=\"https://contrib.rocks/image?repo=canimus/cuallee\" />\n</a>\n\n### Guidelines\n- [Contributing to cuallee](CONTRIBUTING.md)\n\n## Documentation\n- [https://canimus.github.io/cuallee/](https://canimus.github.io/cuallee/)\n\n\n## Paper\n\n`cuallee` has been published in the Journal of Open Source Software\n```\nVazquez et al., (2024). cuallee: A Python package for data quality checks across multiple DataFrame APIs. Journal of Open Source Software, 9(98), 6684, https://doi.org/10.21105/joss.06684\n```\nIf you use `cuallee` please consider citing this work. [Citation](CITATION.cff)\n\n## License\nApache License 2.0\nFree for commercial use, modification, distribution, patent use, private use.\nJust preserve the copyright and license.\n\n\n> Made with \u2764\ufe0f in Utrecht \ud83c\uddf3\ud83c\uddf1<br/>\n> Maintained over \u231b from Ljubljana \ud83c\uddf8\ud83c\uddee<br/>\n> Extended \ud83d\ude80 by contributions all over the \ud83c\udf0e\n",
"bugtrack_url": null,
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