What are the most common data formats for optimization models in Python?

I think CSV files are the most used due to the simplicity and adaptability they offer in their integration. It is sufficient to ensure a good structuring of the file before importing it or once imported converted into a panda’s data frame to exploit the exported data in different structures, edit the headers, reset the indexes differently if needed, etc.

Optimization of data in python doesn't really need much , however the most common format are either parquet or a feather format as it improves dàta writing, reading and storage performance

Despite its limitations, CSV remains a useful format due to its portability, as it is based on plain text and it's accepted by many different tools from Excel to Unix command line tools. The main challenge is ensuring adherence to the selected conventions for representation (delimiters, headings, quotations, etc.).

CSV files CSV stands for comma-separated values, and it is a simple and widely used format for storing tabular data, such as matrices, tables, or spreadsheets. You can read and write CSV files in Python using the built-in csv module or the pandas library. CSV files are easy to create, edit, and share, and they can store large amounts of data efficiently. However, CSV files have some limitations, such as the lack of metadata, the ambiguity of separators and delimiters, the difficulty of handling missing or irregular values, and the potential for data corruption or loss.

Based on my experience, CSV files are particularly useful in the context of optimization models when it comes to developing quick Proof of Concepts (PoCs) to assess the feasibility of the models with both business and technical users. CSV format makes it easier to gather initial data, process it, and share the model's output with the team. However, for the MVP and production stages of the model, I typically avoid CSV files.

I like to use JSON file as the input to my optimization model. When it is loaded in Python, it is readily organized into dictionary. I only have to do very minimal data processing before feeding them to the constraints. What I like about JSON is that it supports both static and dynamic data. Schema validation is also simple, to ensure that the data follows the right format.

Probably the main advantage of JSON files is that it maps more easily into an object-oriented approach than table-based formats, like Excel or CSV. This makes it more tractable to interpret the data when eyeballing it, and to load it into internal representations that reflect the domain.

Utilizing a JSON file as the input for your optimization model offers a structured and versatile approach to parameter configuration. JSON's key-value pair format allows you to encapsulate various input parameters, making it easy to organize and modify the model's settings. This method enhances flexibility, as you can dynamically adjust parameters without altering the underlying code. Additionally, the human-readable nature of JSON simplifies the understanding and communication of input specifications. By employing JSON for your optimization model, you establish a clear and adaptable framework that facilitates efficient parameter management and model customization.

I found JSON files very useful for deploying models with the web application as a user interface, especially capturing user input data for the model to work with a production phase.

Excel is almost every where in the business world. Getting the data from the business applications, pre and post processing of the data becomes simpler due to it's familiarity with the business users.

The only true advantage of Excel is ubiquity and familiarity, especially among non-technical users. This makes it a useful exchange protocol between end users and modellers.

I believe a more interesting format for analysis is as follows: If the dataset is compatible in terms of size to be visualized in Excel, it should initially be analyzed by Excel to observe any inconsistencies and also make quick adjustments in the data structure. Subsequently, the dataset should be analyzed by Python following a protocol to evaluate pre-optimization inconsistencies, such as missing data, incorrect information, imbalance of information (to avoid impossible solutions), among others.

I second this opinion familiarity is a big advantage of using Excel files. Otherwise its use on large data sets can contains many incoherences and inconsistencies that will need to be corrected afterwards.

I avoid Excel files for my modeling work in Python. I had several experiences where hidden formulas and external changes resulted in redoing most of the work.

While pickle files have their usefulness for quick and dirty prototyping, anything else needs to move to other representations. Since they are a serialization of the internal memory representation of the corresponding Python objects, it's a very fragile object that will fail as soon as there is any library mismatch. Ideally, one should chose a format that stores the data, rather than the object. It will take more time to do the serialization and deserialization, but it will be much more robust for the long term.

I used pickle files but only storing the model objects rather than data storage. My advice is don't use data in pickle format.

I’ve used LP files with the Cplex solver. If the problem is precise and standardized then yes its pretty useful. Otherwise it is better to integrate an extended mathematical formulation for better problem solving then handling the errors step by step for more accuracy

These I have rarely seen used. The more usual options are keeping the model in an algebraic modelling language and integrating it directly in code if using an API. Most solvers provide an algebraic modeling language to specify models, with more versatility than the LP format and typically more readable and maintainable, as it closely resembles the mathematical formulation. Solver-agnostic languages like AMPL or GMPL also exist. With the main solvers (and others like AMPL) offering Python APIs, it is becoming more common to not have a separate model, but to build it dynamically in code through the API.

In the development phase, it is crucial to modify the input data multiple times. It is essential to employ various aggregation functions, check for unique values, manipulate data, and select different time periods. Therefore, a tool that is easy, fast, and flexible becomes imperative for this task. Based on my experience, I have found Microsoft Power Query and Power Pivot to be highly useful for such manipulations.

In my experience of working on different projects mostly the final solver engine consume data as csv or xlsx or json format. However you may have layer or wrapper which reading data from backend server/data cluster and converting it to desired format.

We're going to consider the following formats to store our data. Plain-text CSV — a good old friend of a data scientist. Pickle — a Python's way to serialize things. MessagePack — it's like JSON but fast and small. HDF5 —a file format designed to store and organize large amounts of data. thanks oneness healthiest times

Parquet is another data format commonly used in the context of Data Science and optimization models in Python. Parquet is a columnar storage format that is highly efficient for analytics and data processing. It is particularly well-suited for large datasets and is supported by various data processing frameworks, including Apache Arrow and Apache Spark. In Python, you can work with Parquet files using libraries like PyArrow or pandas.

I frequently use Parquet when working with models that require the use of reasonably sized data because of its effectiveness in storing and processing large datasets, especially when it comes to columnar data. Parquet offers a great way for column-based access, which can be handy in cases where not all columns are used in the model. However, I don't recommend using it for small datasets.