Automatic Constraint Generation, Data Verification & Anomaly Detection

The TDDA library provides support for constraint generation, verification and anomaly detection for datasets, including .csv files and Pandas DataFrames.

The module includes:

The tdda Command-line Tool for discovering constraints in data, and for verifying data against those constraints, using the TDDA JSON file format (.tdda files).
A Python constraints library containing classes that implement constraint discovery and validation, for use from within other Python programs.
Python implementations of constraint discovery, verification and and anomaly detection for a number of data sources:
- .csv files
- Pandas and R DataFrames saved as .feather files
- PostgreSQL database tables (postgres:)
- MySQL database tables (mysql:)
- SQLite database tables (sqlite:)
- MongoDB document collections (mongodb:)

Note

To use databases, pandas, .feather files etc. you may need to install extra optional packages. See Optional Installations for using Databases, Feather Files, Pandas.

The `tdda` Command-line Tool

The tdda command-line utility provides a tool for discovering constraints in data and saving them as a .tdda file in the TDDA JSON file format, and also for verifying data using against constraints stored in a .tdda file.

It also provides some other functionality to help with using the tool. The following command forms are supported:

tdda discover —perform constraint discovery.
tdda verify — verify data against constraints.
tdda detect — detect anomalies in data by checking constraints.
tdda examples — generate example data and code.
tdda help — show help on how to use the tool.
tdda test — run the TDDA library’s internal tests.

See Examples for more detail on the code and data examples that are included as part of the tdda package.

See Tests for more detail on the tdda package’s own tests, used to test that the package is installed and configured correctly.

`tdda discover`

The tdda discover command can generate constraints for data, and save the generated constraints as a TDDA JSON file format file (.tdda).

Usage:

tdda discover [FLAGS] input [constraints.tdda]

input is one of:
- a .csv file
- a -, meaning that a .csv file should be read from standard input
- a feather file containing a DataFrame, with extension .feather
- a database table
constraints.tdda, if provided, specifies the name of a file to which the generated constraints will be written.

If no constraints output file is provided, or if - is used, the constraints are written to standard output (stdout).

Optional flags are:

-r or --rex, to include regular expression generation

-R or --norex, to exclude regular expression generation

See Constraints for CSV Files and Pandas DataFrames for details of how a .csv file is read.

See Constraints for Databases for details of how database tables are accessed.

`tdda verify`

The tdda verify command is used to validate data from various sources, against constraints from a TDDA JSON file format constraints file.

Usage:

tdda verify [FLAGS] input [constraints.tdda]

input is one of:
- a .csv file
- a -, meaning it will read a .csv file from standard input
- a feather file containing a DataFrame, with extension .feather
- a database table
constraints.tdda, if provided, is a JSON .tdda file constaining constraints.

If no constraints file is provided and the input is a .csv or a .feather file, a constraints file with the same path as the input file, but with a .tdda extension, will be used.

For database tables, the constraints file parameter is mandatory.

Optional flags are:

-a, --all
Report all fields, even if there are no failures
-f, --fields
Report only fields with failures
-7, --ascii
Report in ASCII form, without using special characters.
--epsilon E
Use this value of epsilon for fuzziness in comparing numeric values.
--type_checking strict|sloppy
By default, type checking is sloppy, meaning that when checking type constraints, all numeric types are considered to be equivalent. With strict typing, int is considered different from real.

See Constraints for CSV Files and Pandas DataFrames for details of how a .csv file is read.

See Constraints for Databases for details of how database tables are accessed.

`tdda detect`

The tdda detect command is used to detect anomalies on data, by checking against constraints from a TDDA JSON file format constraints file.

Usage:

tdda detect [FLAGS] input constraints.tdda output

input is one of:
- a .csv file name
- a -, meaning it will read a .csv file from standard input
- a feather file containing a DataFrame, with extension .feather
- a database table
constraints.tdda, is a JSON .tdda file constaining constraints.
output is one of:
- a .csv file to be created containing failing records
- a -, meaning it will write the .csv file containing failing records to standard output
- a feather file with extension .feather, to be created containing a DataFrame of failing records

If no constraints file is provided and the input is a .csv or feather file, a constraints file with the same path as the input file, but with a .tdda extension, will be used.

Optional flags are:

-a, --all
Report all fields, even if there are no failures
-f, --fields
Report only fields with failures
-7, --ascii
Report in ASCII form, without using special characters.
--epsilon E
Use this value of epsilon for fuzziness in comparing numeric values.
--type_checking strict|sloppy
By default, type-checking is sloppy, meaning that when checking type constraints, all numeric types are considered to be equivalent. With strict typing, int is considered different from real.
--write-all
Include passing records in the output.
--per-constraint
Write one column per failing constraint, as well as the n_failures total column for each row.
--output-fields FIELD1 FIELD2 ...
Specify original columns to write out. If used with no field names, all original columns will be included.
--index
Include a row-number index in the output file. The row number is automatically included if no output fields are specified. Rows are usually numbered from 1, unless the (feather) input file already has an index.

If no records fail any of the constraints, then no output file is created (and if the output file already exists, it is deleted).

See Constraints for CSV Files and Pandas DataFrames for details of how a .csv file is read.

See Constraints for Databases for details of how database tables are accessed.

Constraints for CSV Files and Pandas DataFrames

If a .csv file is used with the tdda command-line tool, it will be processed by the standard Pandas .csv file reader with the following settings:

index_col is None
infer_datetime_format is True
quotechar is "
quoting is csv.QUOTE_MINIMAL
escapechar is \ (backslash)
na_values are the empty string, "NaN", and "NULL"
keep_default_na is False

Constraints for Databases

When a database table is used with the any tdda command-line tool, the table name (including an optional schema) can be preceded by DBTYPE chosen from postgres, mysql, sqlite or mongodb:

DBTYPE:[schema.]tablename

The following example will use the file .tdda_db_conn_postgres from your home directory (see Database Connection Files), providing all of the default parameters for the database connection.

tdda discover postgres:mytable
tdda discover postgres:myschema.mytable

For MongoDB, document collections are used instead of database tables, and a document can be referred to at any level in the collection structure. Only scalar properties are used for constraint discovery and verification (and any deeper nested structure is ignored). For example:

tdda discover mongodb:mydocument
tdda discover mongodb:subcollection.mysubdocument

Parameters can also be provided using the following flags (which override the values in the .tdda_db_conn_DBTYPE file, if provided):

--conn FILE
Database connection file (see Database Connection Files)
--dbtype DBTYPE
Type of database
--db DATABASE
Name of database to connect to
--host HOSTNAME
Name of server to connect to
--port PORTNUMBER
IP port number to connect to
--user USERNAME
Username to connect as
--password PASSWORD
Password to authenticate with

If --conn is provided, then none of the other options are required, and the database connection details are read from the specified file.

If the database type is specified (with the --dbtype option, or by prefixing the table name, such as postgres:mytable), then a default connection file .tdda_db_conn_DBTYPE (in your home directory) is used, if present (where DBTYPE is the name of the kind of database server).

To use constraints for databases, you must have an appropriate DB-API (PEP-0249) driver library installed within your Python environment.

These are:

For PostgreSQL: pygresql or PyGreSQL
For MySQL: MySQL-python, mysqlclient or mysql-connector-python
For SQLite: sqlite3
For MongoDB: pymongo

Database Connection Files

To use a database source, you can either specify the database type using the --dbtype DBTYPE option, or you can prefix the table name with an appropriate DBTYPE: (one of the supported kinds of database server, such as postgres).

You can provide default values for all of the other database options in a database connection file .tdda_db_conn_DBTYPE, in your home directory.

Any database-related options passed in on the command line will override the default settings from the connection file.

A tdda_db_conn_DBTYPE file is a JSON file of the form:

{
    "dbtype": DBTYPE,
    "db": DATABASE,
    "host": HOSTNAME,
    "port": PORTNUMBER,
    "user": USERNAME,
    "password": PASSWORD,
    "schema": SCHEMA,
}

Some additional notes:

All the entries are optional.
If a password is provided, then care should be taken to ensure that the file has appropriate filesystem permissions so that it cannot be read by other users.
If a schema is provided, then it will be used as the default schema, when constraints are discovered or verified on a table name with no schema specified.
For MySQL (in a .tdda_db_conn_mysql file), the schema parameter must be specified, as there is no built-in default for it to use.
For Microsoft Windows, the connector file should have the same name as for Unix, beginning with a dot, even though this form of filename is not otherwise commonly used on Windows.

TDDA JSON file format

A .tdda file is a JSON file containing a single JSON object of the form:

{
    "fields": {
        field-name: field-constraints,
        ...
    }
}

Each field-constraints item is a JSON object containing a property for each included constraint:

{
    "type": one of int, real, bool, string or date
    "min": minimum allowed value,
    "max": maximum allowed value,
    "min_length": minimum allowed string length (for string fields),
    "max_length": maximum allowed string length (for string fields),
    "max_nulls": maximum number of null values allowed,
    "sign": one of positive, negative, non-positive, non-negative,
    "no_duplicates": true if the field values must be unique,
    "values": list of distinct allowed values,
    "rex": list of regular expressions, to cover all cases
}

Constraints Examples

The tdda.constraints module includes a set of examples.

To copy these constraints examples, run the command:

tdda examples constraints [directory]

If directory is not supplied, constraints_examples will be used.

Alternatively, you can copy all examples using the following command:

tdda examples

which will create a number of separate subdirectories.

Automatic Constraint Generation, Data Verification & Anomaly Detection

The tdda Command-line Tool

tdda discover

tdda verify

tdda detect

Constraints for CSV Files and Pandas DataFrames

Constraints for Databases

Database Connection Files

TDDA JSON file format

Constraints Examples

The `tdda` Command-line Tool

`tdda discover`

`tdda verify`

`tdda detect`