Goodtables is a framework to inspect tabular data.
Project description
# goodtables
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[](https://pypi-hypernode.com/pypi/goodtables)
[](http://semver.org/)
[](https://gitter.im/frictionlessdata/chat)
Goodtables is a framework to inspect tabular data.
> [BREAKING] Version `v1.0.0-alpha8` has merged preset `tables` and preset `datapackages` into universal `nested` preset.
---
> [BREAKING] Version `v1.0` has renewed API introduced in NOT backward-compatibility manner. Previous version could be found [here](https://github.com/frictionlessdata/goodtables-py/tree/4b85254cc0358c0caf85bbd41d0c2023df99fb9b).
## Features
- tabular data inspection and validation
- general, structure and schema checks
- support for different input data presets
- parallel computation for multitable datasets
- builtin command-line interface
## Getting Started
### Installation
```bash
$ pip install goodtables --pre
$ pip install goodtables[ods] --pre # With ods format support
```
### Example
Let's start with the simple example:
```python
from goodtables import Inspector
inspector = Inspector()
print(inspector.inspect('data/invalid.csv'))
# will print
#{'time': 0.029,
# 'valid': False',
# 'error-count': 2,
# 'table-count': 1,
# 'warnings': [],
# 'tables': [
# {'time': 0.027,
# 'valid': False',
# 'headers': ['id', 'name', ''],
# 'row-count': 4,
# 'source': 'data/invalid.csv'
# 'error-count': 2,
# 'errors': [
# {'row': None,
# 'code': 'blank-header',
# 'message': 'Blank header',
# 'row-number': None,
# 'column-number': 2},
# {'row': [],
# 'code': 'blank-row',
# 'message': 'Blank row',
# 'row-number': 3,
# 'column-number': None}]}]}
```
### Inspection
Goodtables inspects your tabular data to find general, structure and schema errors. As presented in an example above to inspect data:
- `Inspector(**options)` class should be instantiated
- `inspector.inspect(source, preset=<preset>, **options)` should be called
- a returning value will be a report dictionary
#### Dataset
Goodtables support different sources for an inspection. But it should be convertable to dataset presented on a figure 1. Details will be explained in the next sections:
#### Report
As a result of inspection goodtables returns a report dictionary. It includes valid flag, count of errors, list of reports per table including errors etc. See example above for an instance. A report structure and all errors are standartised and described in **data quality spec**:
> https://github.com/frictionlessdata/goodtables-py/blob/next-initial/goodtables/spec.json
#### Errors
Report errors are categorized by type:
- source - data can't be loaded or parsed
- structure - general tabular errors like duplicate headers
- schema - error of checks against JSON Table Schema
Report errors are categorized by context:
- table - the whole table errors like IO, HTTP or encoding error
- head - headers errors
- body - contents errors
### Presets
Table is a main inspection object in goodtables. The simplest option is to pass to `Inspector.inspect` path and other options for one table (see example above). But when multitable parallized inspection is needed different presets could be used to process a dataset.
Let's see how to inspect a datapackage:
```python
from goodtables import Inspector
inspector = Inspector()
inspector.inspect('datapackage.json', preset='datapackage')
```
A preset function proceses passed source and options and fills tables list for the following inspection. If any issues have happened a preset function should add them to warnings list.
#### Builtin presets
Goodtables by default supports the following presets:
- table
- datapackage
- nested (a special preset allows to nest `inspect` calls - [example](https://github.com/frictionlessdata/goodtables-py/blob/master/examples/nested.py))
#### Custom presets
> It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete `goodtables` version to your requirements file.
To register a custom preset user could use a `preset` decorator. This way the builtin preset could be overriden or could be added a custom preset.
```python
from tabulator import Stream
from jsontableschema import Schema
from goodtables import Inspector, preset
@preset('custom-preset')
def custom_preset(source, **options):
warnings = []
tables = []
for table in source:
try:
tables.append({
'source': str(source),
'stream': Stream(...),
'schema': Schema(...),
'extra': {...},
})
except Exception:
warnings.append('Warning message')
return warnings, tables
inspector = Inspector(custom_presets=[custom_preset])
inspector.inspect(source, preset='custom-preset')
```
See builtin presets to learn more about the dataset extration protocol.
### Checks
Check is a main inspection actor in goodtables. Every check is associated with a specification error. Checking order is the same as order of errors in the specification. List of checks could be customized using inspector's `checks` argument. Let's explore options on an example:
```python
inspector = Inspector(checks='all/structure/schema') # type
inspector = Inspector(checks={'bad-headers': False}) # exclude
inspector = Inspector(checks={'bad-headers': True}) # cherry-pick
```
Check gets input data from framework based on context (e.g. `columns, sample` for `head` context) and update errors and columns lists in-place.
#### Buitin checks
Goodtables by default supports the following checks:
- [check for every error from the specification]
#### Custom checks
> It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete `goodtables` version to your requirements file.
To register a custom check user could use a `check` decorator. This way the builtin check could be overriden (use the spec error code like `duplicate-row`) or could be added a check for a custom error (use `type`, `context` and `after/before` arguments):
```python
from goodtables import Inspector, check
@check('custom-error', type='structure', context='body', after='blank-row')
def custom_check(errors, columns, row_number, state=None):
for column in columns:
errors.append({
'code': 'custom-error',
'message': 'Custom error',
'row-number': row_number,
'column-number': column['number'],
})
columns.remove(column)
inspector = Inspector(custom_checks=[custom_check])
```
See builtin checks to learn more about checking protocol.
### CLI
> It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete `goodtables` version to your requirements file.
All common goodtables tasks could be done using a command-line interface (command per preset excluding `tables`):
```
$ goodtables
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--json
--error-limit INTEGER
--table-limit INTEGER
--row-limit INTEGER
--infer-schema
--infer-fields
--order-fields
--help Show this message and exit.
Commands:
datapackage
table
```
For example write a following command to the shell:
```
$ goodtables table data/invalid.csv
```
And a report (the same as in the initial example) will be printed to the standard output.
## FAQ
### Is it an inspection or validation?
For now we use the word `inspector` because we create reports as the result of an inspection. One difference to validation - goodtables will not raise an exception if the dataset is invalid. Final naming is under consideration and based on exposed methods (only `inspect` or like `inspect/validate/stream`).
### Is it possible to stream reporting?
For now - it's not. But it's under consideration. Not for multitable datasets because of parallelizm, but for one table it could be exposed to public via API because internally that's how goodtables works. The question here is "what should be streamed?" - errors, or valid/invalid per row indication with errors, etc. We would be happy to see a real world use case for this feature.
## API Reference
### Snapshot
```
Inspector(checks='all',
table_limit=10,
row_limit=1000,
error_limit=1000,
infer_schema=False,
infer_fields=False,
order_fields=False,
custom_presets=[],
custom_checks=[])
inspect(source, preset='table', **options)
~@preset(name)
~@check(error)
exceptions
spec
~cli
```
### Detailed
- [Docstrings](https://github.com/frictionlessdata/goodtables-py/tree/master/goodtables)
- [Changelog](https://github.com/frictionlessdata/goodtables/commits/master)
## Contributing
Please read the contribution guideline:
[How to Contribute](CONTRIBUTING.md)
Thanks!
[](https://travis-ci.org/frictionlessdata/goodtables-py)
[](https://coveralls.io/r/frictionlessdata/goodtables-py?branch=master)
[](https://pypi-hypernode.com/pypi/goodtables)
[](http://semver.org/)
[](https://gitter.im/frictionlessdata/chat)
Goodtables is a framework to inspect tabular data.
> [BREAKING] Version `v1.0.0-alpha8` has merged preset `tables` and preset `datapackages` into universal `nested` preset.
---
> [BREAKING] Version `v1.0` has renewed API introduced in NOT backward-compatibility manner. Previous version could be found [here](https://github.com/frictionlessdata/goodtables-py/tree/4b85254cc0358c0caf85bbd41d0c2023df99fb9b).
## Features
- tabular data inspection and validation
- general, structure and schema checks
- support for different input data presets
- parallel computation for multitable datasets
- builtin command-line interface
## Getting Started
### Installation
```bash
$ pip install goodtables --pre
$ pip install goodtables[ods] --pre # With ods format support
```
### Example
Let's start with the simple example:
```python
from goodtables import Inspector
inspector = Inspector()
print(inspector.inspect('data/invalid.csv'))
# will print
#{'time': 0.029,
# 'valid': False',
# 'error-count': 2,
# 'table-count': 1,
# 'warnings': [],
# 'tables': [
# {'time': 0.027,
# 'valid': False',
# 'headers': ['id', 'name', ''],
# 'row-count': 4,
# 'source': 'data/invalid.csv'
# 'error-count': 2,
# 'errors': [
# {'row': None,
# 'code': 'blank-header',
# 'message': 'Blank header',
# 'row-number': None,
# 'column-number': 2},
# {'row': [],
# 'code': 'blank-row',
# 'message': 'Blank row',
# 'row-number': 3,
# 'column-number': None}]}]}
```
### Inspection
Goodtables inspects your tabular data to find general, structure and schema errors. As presented in an example above to inspect data:
- `Inspector(**options)` class should be instantiated
- `inspector.inspect(source, preset=<preset>, **options)` should be called
- a returning value will be a report dictionary
#### Dataset
Goodtables support different sources for an inspection. But it should be convertable to dataset presented on a figure 1. Details will be explained in the next sections:
#### Report
As a result of inspection goodtables returns a report dictionary. It includes valid flag, count of errors, list of reports per table including errors etc. See example above for an instance. A report structure and all errors are standartised and described in **data quality spec**:
> https://github.com/frictionlessdata/goodtables-py/blob/next-initial/goodtables/spec.json
#### Errors
Report errors are categorized by type:
- source - data can't be loaded or parsed
- structure - general tabular errors like duplicate headers
- schema - error of checks against JSON Table Schema
Report errors are categorized by context:
- table - the whole table errors like IO, HTTP or encoding error
- head - headers errors
- body - contents errors
### Presets
Table is a main inspection object in goodtables. The simplest option is to pass to `Inspector.inspect` path and other options for one table (see example above). But when multitable parallized inspection is needed different presets could be used to process a dataset.
Let's see how to inspect a datapackage:
```python
from goodtables import Inspector
inspector = Inspector()
inspector.inspect('datapackage.json', preset='datapackage')
```
A preset function proceses passed source and options and fills tables list for the following inspection. If any issues have happened a preset function should add them to warnings list.
#### Builtin presets
Goodtables by default supports the following presets:
- table
- datapackage
- nested (a special preset allows to nest `inspect` calls - [example](https://github.com/frictionlessdata/goodtables-py/blob/master/examples/nested.py))
#### Custom presets
> It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete `goodtables` version to your requirements file.
To register a custom preset user could use a `preset` decorator. This way the builtin preset could be overriden or could be added a custom preset.
```python
from tabulator import Stream
from jsontableschema import Schema
from goodtables import Inspector, preset
@preset('custom-preset')
def custom_preset(source, **options):
warnings = []
tables = []
for table in source:
try:
tables.append({
'source': str(source),
'stream': Stream(...),
'schema': Schema(...),
'extra': {...},
})
except Exception:
warnings.append('Warning message')
return warnings, tables
inspector = Inspector(custom_presets=[custom_preset])
inspector.inspect(source, preset='custom-preset')
```
See builtin presets to learn more about the dataset extration protocol.
### Checks
Check is a main inspection actor in goodtables. Every check is associated with a specification error. Checking order is the same as order of errors in the specification. List of checks could be customized using inspector's `checks` argument. Let's explore options on an example:
```python
inspector = Inspector(checks='all/structure/schema') # type
inspector = Inspector(checks={'bad-headers': False}) # exclude
inspector = Inspector(checks={'bad-headers': True}) # cherry-pick
```
Check gets input data from framework based on context (e.g. `columns, sample` for `head` context) and update errors and columns lists in-place.
#### Buitin checks
Goodtables by default supports the following checks:
- [check for every error from the specification]
#### Custom checks
> It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete `goodtables` version to your requirements file.
To register a custom check user could use a `check` decorator. This way the builtin check could be overriden (use the spec error code like `duplicate-row`) or could be added a check for a custom error (use `type`, `context` and `after/before` arguments):
```python
from goodtables import Inspector, check
@check('custom-error', type='structure', context='body', after='blank-row')
def custom_check(errors, columns, row_number, state=None):
for column in columns:
errors.append({
'code': 'custom-error',
'message': 'Custom error',
'row-number': row_number,
'column-number': column['number'],
})
columns.remove(column)
inspector = Inspector(custom_checks=[custom_check])
```
See builtin checks to learn more about checking protocol.
### CLI
> It's a provisional API excluded from SemVer. If you use it as a part of other program please pin concrete `goodtables` version to your requirements file.
All common goodtables tasks could be done using a command-line interface (command per preset excluding `tables`):
```
$ goodtables
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--json
--error-limit INTEGER
--table-limit INTEGER
--row-limit INTEGER
--infer-schema
--infer-fields
--order-fields
--help Show this message and exit.
Commands:
datapackage
table
```
For example write a following command to the shell:
```
$ goodtables table data/invalid.csv
```
And a report (the same as in the initial example) will be printed to the standard output.
## FAQ
### Is it an inspection or validation?
For now we use the word `inspector` because we create reports as the result of an inspection. One difference to validation - goodtables will not raise an exception if the dataset is invalid. Final naming is under consideration and based on exposed methods (only `inspect` or like `inspect/validate/stream`).
### Is it possible to stream reporting?
For now - it's not. But it's under consideration. Not for multitable datasets because of parallelizm, but for one table it could be exposed to public via API because internally that's how goodtables works. The question here is "what should be streamed?" - errors, or valid/invalid per row indication with errors, etc. We would be happy to see a real world use case for this feature.
## API Reference
### Snapshot
```
Inspector(checks='all',
table_limit=10,
row_limit=1000,
error_limit=1000,
infer_schema=False,
infer_fields=False,
order_fields=False,
custom_presets=[],
custom_checks=[])
inspect(source, preset='table', **options)
~@preset(name)
~@check(error)
exceptions
spec
~cli
```
### Detailed
- [Docstrings](https://github.com/frictionlessdata/goodtables-py/tree/master/goodtables)
- [Changelog](https://github.com/frictionlessdata/goodtables/commits/master)
## Contributing
Please read the contribution guideline:
[How to Contribute](CONTRIBUTING.md)
Thanks!
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