Validate tabular data in Python
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Goodtables is a framework to validate tabular data. It can check the structure of your data (e.g. all rows have the same number of columns), and its contents (e.g. all dates are valid).


  • Structural checks: Ensure that there are no empty rows, no blank headers, etc.
  • Content checks: Ensure that the values have the correct types ("string", "number", "date", etc.), that their format is valid ("string must be an e-mail"), and that they respect the constraints ("age must be a number greater than 18").
  • Support for multiple tabular formats: CSV, Excel files, LibreOffice, Data Package, etc.
  • Parallelized validations for multi-table datasets
  • Command line interface

Table of Contents

Getting Started


pip install goodtables
pip install goodtables[ods]  # If you need LibreOffice's ODS file support

Running on CLI

goodtables data.csv

Use goodtables --help to see the different options.

Running on Python

from goodtables import validate

report = validate('invalid.csv')
report['valid'] # false
report['table-count'] # 1
report['error-count'] # 3
report['tables'][0]['valid'] # false
report['tables'][0]['source'] # 'invalid.csv'
report['tables'][0]['errors'][0]['code'] # 'blank-header'

You can read a more in depth explanation on using goodtables with Python on the developer documentation section. Check also the examples folder for other examples.


Basic checks

The basic checks can't be disabled, as they deal with goodtables being able to read the files.

check description
io-error Data reading error because of IO error.
http-error Data reading error because of HTTP error.
source-error Data reading error because of not supported or inconsistent contents.
scheme-error Data reading error because of incorrect scheme.
format-error Data reading error because of incorrect format.
encoding-error Data reading error because of an encoding problem.

Structural checks

These checks validate that the structure of the file are valid.

check description
blank-header There is a blank header name. All cells in the header row must have a value.
duplicate-header There are multiple columns with the same name. All column names must be unique.
blank-row Rows must have at least one non-blank cell.
duplicate-row Rows can't be duplicated.
extra-value A row has more columns than the header.
missing-value A row has less columns than the header.

Content checks

These checks validate the contents of the file. To use them, you need to pass a Table Schema. If you don't have a schema, goodtables can infer it if you use the infer_schema option.

If your schema only covers part of the data, you can use the infer_fields to infer the remaining fields.

Lastly, if the order of the fields in the data is different than in your schema, enable the order_fields option.

check description
schema-error Schema is not valid.
non-matching-header The header's name in the schema is different from what's in the data.
extra-header The data contains a header not defined in the schema.
missing-header The data doesn't contain a header defined in the schema.
type-or-format-error The value can’t be cast based on the schema type and format for this field.
required-constraint This field is a required field, but it contains no value.
pattern-constraint This field value's should conform to the defined pattern.
unique-constraint This field is a unique field but it contains a value that has been used in another row.
enumerable-constraint This field value should be equal to one of the values in the enumeration constraint.
minimum-constraint This field value should be greater or equal than constraint value.
maximum-constraint This field value should be less or equal than constraint value.
minimum-length-constraint A length of this field value should be greater or equal than schema constraint value.
maximum-length-constraint A length of this field value should be less or equal than schema constraint value.

Advanced checks

check description
blacklisted-value Ensure there are no cells with the blacklisted values.
deviated-value Ensure numbers are within a number of standard deviations from the average.
sequential-value Ensure numbers are sequential.
truncated-value Detect values that were potentially truncated.
custom-constraint Defines a constraint based on the values of other columns (e.g. value * quantity == total).


Sometimes we have to check for some values we don't want to have in out dataset. It accepts following options:

option type description
column int/str Column number or name
blacklist list of str List of blacklisted values

Consider the following CSV file:


Let's check that the name column doesn't contain rows with bug or bad:

from goodtables import validate

report = validate('data.csv', checks=[
    {'blacklisted-value': {'column': 'name', 'blacklist': ['bug', 'bad']}},
# error on row 3 with code "blacklisted-value"
# error on row 4 with code "blacklisted-value"


This check helps to find outlines in a column containing positive numbers. It accepts following options:

option type description
column int/str Column number or name
average str Average type, either "mean", "median" or "mode"
interval int Values must be inside range average ± standard deviation * interval

Consider the following CSV file:


We use median to get an average of the column values and allow interval of 3 standard deviations. For our case median is 2.0 and standard deviation is 29.73 so all valid values must be inside the [-87.19, 91.19] interval.

report = validate('data.csv', checks=[
    {'deviated-value': {'column': 'temperature', 'average': 'median', 'interval': 3}},
# error on row 10 with code "deviated-value"


This checks is for pretty common case when a column should have integers that sequentially increment. It accepts following options:

option type description
column int/str Column number or name

Consider the following CSV file:


Let's check if the id column contains sequential integers:

from goodtables import validate

report = validate('data.csv', checks=[
    {'sequential-value': {'column': 'id'}},
# error on row 5 with code "sequential-value"


Some database or spreadsheet software (like MySQL or Excel) could cutoff values on saving. There are some well-known heuristics to find this bad values. See for more detailed information.

Consider the following CSV file:


To detect all probably truncated values we could use truncated-value check:

report = validate('data.csv', checks=[
# error on row 3 with code "truncated-value"
# error on row 4 with code "truncated-value"
# error on row 5 with code "truncated-value"


With Table Schema we could create constraints for an individual field but sometimes it's not enough. With a custom constraint check every row could be checked against given limited python expression in which variable names resolve to column values. See list of available operators. It accepts following options:

constraint (str)
Constraint definition (e.g. col1 + col2 == col3)

Consider csv file like this:


Let's say our business rule is to be shy on bonuses:

report = validate('data.csv', checks=[
    {'custom-constraint': {'constraint': 'salary > bonus * 4'}},
# error on row 4 with code "custom-constraint"

Validation report format

The validation report follows the JSON Schema defined on goodtables/schemas/report.json. As an example, this is the report generated by running goodtables --row-limit 5 --json on the file data/datapackages/invalid/datapackage.json:

    "time": 0.015,
    "valid": false,
    "error-count": 2,
    "table-count": 2,
    "tables": [
            "datapackage": "data/datapackages/invalid/datapackage.json",
            "time": 0.005,
            "valid": false,
            "error-count": 1,
            "row-count": 4,
            "source": "data/datapackages/invalid/data.csv",
            "headers": [
            "format": "inline",
            "schema": "table-schema",
            "errors": [
                    "code": "blank-row",
                    "row-number": 3,
                    "message": "Row 3 is completely blank"
            "datapackage": "data/datapackages/invalid/datapackage.json",
            "time": 0.004,
            "valid": false,
            "error-count": 1,
            "row-count": 5,
            "source": "data/datapackages/invalid/data2.csv",
            "headers": [
            "format": "inline",
            "schema": "table-schema",
            "errors": [
                    "code": "blank-row",
                    "row-number": 4,
                    "message": "Row 4 is completely blank"
    "warnings": [
        "Table \"data/datapackages/invalid/data2.csv\" inspection has reached 5 row(s) limit"
    "preset": "datapackage"

Developer documentation

Semantic versioning

We follow the Semantic Versioning specification to define our version numbers. This means that we'll increase the major version number when there's a breaking change. Because of this, we recommend you to explicitly specify the goodtables version on your dependency list (e.g. or requirements.txt).


Goodtables validates your tabular dataset to find structural and content errors. Consider you have a file named invalid.csv. Let's validate it:

report = validate('invalid.csv')

We could also pass a remote URI instead of a local path. It supports CSV, XLS, XLSX, ODS, JSON, and all other formats supported by the tabulator library.

Validation report

The output of the validate() method is a report dictionary. It includes information if the data was valid, count of errors, list of table reports, which individual checks failed, etc.

Resulting report will be looking like this:

    "time": 0.009,
    "error-count": 1,
    "warnings": [
        "Table \"data/invalid.csv\" inspection has reached 1 error(s) limit"
    "preset": "table",
    "valid": false,
    "tables": [
            "errors": [
                    "row-number": null,
                    "message": "Header in column 3 is blank",
                    "row": null,
                    "column-number": 3,
                    "code": "blank-header"
            "error-count": 1,
            "headers": [
            "scheme": "file",
            "row-count": 2,
            "valid": false,
            "encoding": "utf-8",
            "time": 0.007,
            "schema": null,
            "format": "csv",
            "source": "data/invalid"
    "table-count": 1

Rase report errors are standardized and described in Data Quality Spec. The errors are divided in one of the following categories:

  • source - data can't be loaded or parsed
  • structure - general tabular errors like duplicate headers
  • schema - error of checks against Table Schema
  • custom - custom checks errors


Check is a main validation actor in goodtables. The list of enabled checks can be changed using checks and skip_checks arguments. Let's explore the options on an example:

report = validate('data.csv') # by default structure and schema (if available) checks
report = validate('data.csv', checks=['structure']) # only structure checks
report = validate('data.csv', checks=['schema']) # only schema (if available) checks
report = validate('data.csv', checks=['bad-headers']) # check only 'bad-headers'
report = validate('data.csv', skip_checks=['bad-headers']) # exclude 'bad-headers'

By default a dataset will be validated against all available Data Quality Spec errors. Some checks can be unavailable for validation. For example, if the schema isn't provided, only the structure checks will be done.


Goodtables support different formats of tabular datasets. They're called presets. A tabular dataset is some data that can be split in a list of data tables, as:


We can change the preset using the preset argument for validate(). By default, it'll be inferred from the source, falling back to table. To validate a data package, we can do:

report = validate('datapackage.json') # implicit preset
report = validate('datapackage.json', preset='datapackage') # explicit preset

This will validate all tabular resources in the datapackage.

It's also possible to validate a list of files using the "nested" preset. To do so, the first argument to validate() should be a list of dictionaries, where each key in the dictionary is named after a parameter on validate(). For example:

report = validate([{'source': 'data1.csv'}, {'source': 'data2.csv'}]) # implicit preset
report = validate([{'source': 'data1.csv'}, {'source': 'data2.csv'}], preset='nested') # explicit preset

Is similar to:

report_data1 = validate('data1.csv')
report_data2 = validate('data2.csv')

The difference is that goodtables validates multiple tables in parallel, so calling using the "nested" preset should run faster.


This project follows the Open Knowledge International coding standards.

We recommend you to use virtualenv to isolate goodtables from the rest of the packages in your machine.

To install goodtables and the development dependencies, run:

$ make install

To run the tests, use:

$ make test


How can I add a new custom check?

To create a custom check user could use a check decorator. This way the builtin check could be overridden (use the spec error code like duplicate-row) or could be added a check for a custom error (use type, context and position arguments):

from goodtables import validate, check, Error

@check('custom-check', type='custom', context='body')
def custom_check(cells):
    errors = []
    for cell in cells:
        message = 'Custom error on column {column_number} and row {row_number}'
        error = Error(
    return errors

report = validate('data.csv', checks=['custom-check'])

For now this documentation section is incomplete. Please see builtin checks to learn more about checking protocol.

How can I add support for a new tabular file type?

To create a custom preset user could use a preset decorator. This way the builtin preset could be overridden or could be added a custom preset.

from tabulator import Stream
from tableschema import Schema
from goodtables import validate

def custom_preset(source, **options):
    warnings = []
    tables = []
    for table in source:
                'source':  str(source),
                'stream':  Stream(...),
                'schema': Schema(...),
                'extra': {...},
        except Exception:
            warnings.append('Warning message')
    return warnings, tables

report = validate(source, preset='custom-preset')

For now this documentation section is incomplete. Please see builtin presets to learn more about the dataset extraction protocol.



New features:

  • A new key added to the error.to_dict return: message-data


Breaking changes:

  • Checks method signature now only receives the current row's cells list
  • Checks raise errors by returning an array of Error objects
  • Cells have the row number in the row-number key
  • Files with ZIP extension are presumed to be datapackages, so goodtables works
  • Improvements to goodtables CLI (#233)
  • New goodtables init <data paths> command to create a new datapackage.json with the files passed as parameters and their inferred schemas.

Bug fixes:

  • Fix bug with truncated-values check on date fields (#250)


New API added:

  • Validation source now could be a pathlib.Path


Improved behaviour:

  • rebased on Data Quality Spec v1
  • rebased on Data Package Spec v1
  • rebased on Table Schema Spec v1
  • treat primary key as required/unique field


New advanced checks added:

  • blacklisted-value
  • custom-constraint
  • deviated-value
  • sequential-value
  • truncated-value


New API added:

  • report.preset
  • report.tables[].schema


New API added:

  • report.tables[].scheme
  • report.tables[].format
  • report.tables[].encoding


This version includes various big changes. A migration guide is under development and will be published here.


First version of goodtables.