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ticdat is a Python package that provides lightweight, ORM style functionality around either a dict-of-dicts or
pandas.DataFrame representation of tables. It is well suited for defining and validating the input data for complex
solve engines (i.e. optimization and scheduling-type problems).
ticdat functionality is organized around two classes - TicDatFactory and PanDatFactory. Both classes define a
simple database style schema on construction. Data integrity rules can then be added in the form of foreign key
relationships, data field types (to include numerical ranges and allowed strings) and row predicates
(functions that check if a given row violates a particular data condition). The factory classes can then be used to
construct TicDat/PanDat objects that contain tables consistent with the defined schema. By design,
ticdat, allows these data objects to violate the data integrity rules while providing convenient bulk query functions
to determine where those violations occur.
TicDat objects (created by a TicDatFactory) contain tables in a dict-of-dict format. The outer dictionary maps
primary key values to data rows. The inner dictionaries are data rows indexed by field names (similar to
csv.DictReader/csv.DictWriter). Tables that do not have primary keys are rendered as a list of data row dictionaries.
PanDat objects (created by PanDatFactory) render tables as pandas.DataFrame objects. The columns in each
DataFrame will contain all of the primary key and data fields that were defined in the PanDatFactory schema. The
PanDatFactory code can be thought of as implementing a shim library that organizes DataFrame objects into a
predefined schema, and facilitates rich integrity checks based on schema defined rules.
The ticdat example library is focused on two patterns for building optimization engines - using TicDatFactory in
conjunction with gurobipy and using PanDatFactory in conjunction with amplpy. That said, ticdat can also be
used with libraries like pyomo, pulp, docplex and xpress. It also has functionality to support the OPL and
LINGO modeling languages, although the AMPL support is far more mature.
ticdat's association with AMPL is particularly strong. The design goals of ticdat were inspired by AMPL’s pattern
of cleanly separating model from data, as well as its ability to thoroughly validate input data prior to solving.
AMPL Optimization has endorsed ticdat as their preferred library for bridging the world’s leading modeling language
(AMPL) with Python’s most powerful data analysis package (pandas).
The ticdat library is distributed under the BSD2 open source license.