Detecting Bad Data and Anomalies with the TDDA Library (Part I)

Posted on Fri 04 May 2018 in TDDA

The test-driven data analysis library, tdda, has two main kinds of functionality

  • support for testing complex analytical processes with unittest or pytest
  • support for verifying data against constraints, and optionally for discovering such constraints from example data.

Until now, however, the verification process has only reported which constraints failed to be satisfied by a dataset.

We have now extended the tdda library to allow identification of individual failing records, allowing it to act as a general purpose anomaly detection framework.

The new functionality is available through a new detect_df API call, and from the command line with the new tdda detect command.

The diagram shows conceptually how detection works, separating out anomalous records from the rest.

A simple anomaly detection process, splitting input data into anomalous and non-anomalous records

With the TDDA framework, anomalous simply means fails at least one constraint. We'll discuss cases in which the constraints have been developed to try to model some subset of data of interest (defects, high-risk applicants, heart arrythmias, flawless diamonds, model patients etc.) in part II of this post. In those cases, we start to be able to discuss classifications such as true and false positives, and true and false negatives.

Example Usage from the Command Line

Suppose we have a simple transaction stream with just three fields, id, category and price, like this:

       id    category     price
710316821       QT       150.39
516025643       AA       346.69
414345845       QT       205.83
590179892       CB        55.61
117687080       QT       142.03
684803436       AA       152.10
611205703       QT        39.65
399848408       AA       455.67
289394404       AA       102.61
863476710       AA       297.82
534170200       KA        80.96
898969231       QT        81.39
255672456       QT        71.67
133111344       TB       229.19
763476994       CB       338.40
769595502       QT       310.19
464477044       QT        54.41
675155634       QT       199.07
483511995       QT       209.53
416094320       QT        83.31

and the following constraints (which might have been created by hand, or generated using the tdda discover command).

  • id (integer): Identifier for item. Should not be null, and should be unique in the table

  • category (string): Should be one of “AA”, “CB”, “QT”, “KA” or “TB”

  • price (floating point value): unit price in pounds sterling. Should be non-negative and no more than 1,000.00.

This would be represented in a TDDA file with the following constraints.

  "fields": {
    "id": {
      "type": "int",
      "max_nulls": 0,
      "no_duplicates": true
    "category": {
      "type": "string",
      "max_nulls": 0,
        ["AA", "CB", "QT", "KA", "TB"]
    "price": {
      "type": "real",
      "min": 0.0,
      "max": 1000.0,
      "max_nulls": 0

We can use the tdda verify command to verify a CSV file or a feather file1 against these files, and get a summary of which constraints pass and fail. If our data is in the file items.feather and the JSON constraints are in constraints.tdda, and there are some violations we will get output exemplified by the following:

$ tdda verify items.feather constraints.tdda

id: 1 failure  2 passes  type ✓  max_nulls ✓  no_duplicates ✗

category: 1 failure  2 passes  type ✓  max_nulls ✓  allowed_values ✗

price: 2 failures  2 passes  type ✓  min ✓  max ✗  max_nulls ✗


Constraints passing: 6
Constraints failing: 4

The new tdda detect command allows us to go further and find which individual records fail.

We can use the following command to write out a CSV file, bads.csv, containing the records that fail constraints:

$ tdda detect items.feather constraints.tdda bads.csv ––per-constraint ––output-fields

The flag ––per-constraint tells the software to write out a boolean column for each constraint, indicating whether the record passed, and the ––output-fields tells the software to include all the input fields in the output.

The result is the following CSV file:


which, we can read a bit more easily if we reformat this (using · to denote nulls) as:

       id  category        price        id  category  price     price  n_failures
                                   _nodups   _values   _max  _nonnull
                                       _ok       _ok    _ok       _ok
113791348        TQ       318.63      true     false   true      true           1
102829374        AA        65.24     false      true   true      true           1
720313295        TB     1,004.72      true      true  false      true           1
384044032        QT       478.65     false      true   true      true           1
602948968        TB       209.31     false      true   true      true           1
105983384        AA         8.95     false      true   true      true           1
444140832        QT     1132.87       true      true  false      true           1
593548725        AA       282.58     false      true   true      true           1
545398672        QT     1,026.40      true      true  false      true           1
759425162        CB     1,052.72      true      true  false      true           1
452691252        AA     1,028.19      true      true  false      true           1
105983384        QT       242.64     false      true   true      true           1
102829374        KA        71.64     false      true   true      true           1
105983384        AA        10.24     false      true   true      true           1
405321922        QT        85.23     false      true   true      true           1
102829374         ·   100,000.00     false     false  false      true           3
872018391        QT        51.69     false      true   true      true           1
862101984        QT       158.53     false      true   true      true           1
274332319        AA     1,069.25      true      true  false      true           1
827919239        QT     1,013.00      true      true  false      true           1
105983384        QT       450.68     false      true   true      true           1
102829374         ·   100,000.00     false     false  false      true           3
872018391        QT       199.37     false      true   true      true           1
602948968        KA       558.73     false      true   true      true           1
328073211        CB     1,031.67      true      true  false      true           1
405321922        TB       330.97     false      true   true      true           1
334193154        QT     1,032.31      true      true  false      true           1
194125540        TB            ·      true      true      ·     false           1
724692620        TB     1,025.81      true      true  false      true           1
862101984        QT       186.76     false      true   true      true           1
593548725        QT       196.56     false      true   true      true           1
384044032        AA       157.25     false      true   true      true           1

Command Line Syntax

The basic form of the command-line command is:



  • INPUT is normally either a .csv file, in a suitable format, or a .feather file1 containing a DataFrame, preferably with an accompanying .pmm file1
  • CONSTRAINTS is a JSON file containing constraints, usually with a .tdda suffix. This can be created by the tdda discover command, or edited by hand.
  • OUTPUT is again either a .csv or .feather file to be created with the output rows. If the pmmif library is installed, a .pmm metadata file will be generated alongside the .feather file, when .feather output is requested.


Several command line options are available to control the detailed behaviour:

  • defaults: If no command-line options are supplied:

    • only failing records will be written
    • only a record identifier and the number of failing constraints will be written.
      • When the input is Pandas, the record identifier will be the index for the failing records;
      • when the input is a CSV file, the record identifier will be the row number, with the first row after the header being numbered 1.
  • ––per-constraint when this is added, an _ok column will also be written for every constraint that has any failures, with true for rows that satisfy the contraint, false for rows that do not satisfy the constraint and a missing value where the constraint is inapplicable (which does not count as a failure).

  • ––output-fields [FIELD1 FIELD2 ...] If the ––output-fields flag is used without specifying any fields, all fields from the input will be included in the output. Alternatively, a space-separated list of fields may be provided, in which case only those will be included. Whenever this option is used, no index or row-number is written unless specifically requested

  • ––write-all If this flag is used, all records from the input will be included in the output, including those that have no constraint failures.

  • ––index This flag forces the writing of the index (for DataFrame inputs) or row number (for CSV inputs).

  • ––int When writing boolean values to CSV files (either from input data or as per-constraint output fields), use 1 for true and 0 for false.

API Access

The detection functionality is also available through the TDDA library's API with a new detect_df function, which takes similar parameters to the command line. The corresponding call, with a DataFrame df in memory, would be:

from tdda.constraints import detect_df

verification = detect_df(df, 'constraints.tdda', per_constraint=True,
bads_df = verification.detected()

  1. The feather file format is an interoperable way to save DataFrames from Pandas or R. Its aim is to preserve metadata better and be faster than CSV files. It has a few issues, particularly around types and nulls, and when available, we save a secondary .pmm file alongside .feather files which makes reading and writing them more robust when our extensions in the pmmif library are used. We'll do a future blog post about this, but if you install both feather and pmmif with pip install feather and pip install pmmif, and use featherpmm.write_dataframe, imported from pmmif, rather than feather.write_dataframe, you should get more robust behaviour.