If you've ever wondered...

- How to convert a CSV file to Excel without losing data ?
- Which file format to choose to optimize storage and data exchange ?
- How to quickly and efficiently convert a JSON file to XML ?
- Why use the Parquet or Feather format instead of a simple CSV ?
- How to convert an HDF5 file to CSV without losing its hierarchical structure ?
- What is the difference between Pickle, RData, and SPSS for data storage ?
- How to prepare a data file compatible with a specific software ?

I offer a custom data file conversion service: transform your datasets into the most suitable format for your needs without data loss or errors.

Data files are at the heart of our exchanges, storage, and analysis, bringing numbers and ideas to life.

Each format tells a different story : a CSV table loaded with valuable statistics, or a JSON file structured like a world of nested information. Behind every file type lies a tool designed to meet specific needs and simplify our explorations.

Choosing the right format means selecting the key that unlocks clearer and more effective understanding of your data.

Here are some of the most commonly used data file types to give meaning to your work.

1. Structured Data Files (Storage and Exchange Formats)

CSV (.csv)
Excel (.xlsx, .xls)
JSON (.json)
Parquet (.parquet)
XML (.xml)
HDF5 (.h5)
TXT (.txt)
Feather (.feather)
YAML (.yaml)
Pickle Files (.pkl)
RData (.RData)
SPSS Data Files (.sav)

2. Script Files and Notebooks (Code Files for Data Analysis and Manipulation)

R Script (.R)
Python (.py)
Julia Scripts (.jl)
MATLAB Scripts (.m)
SAS Scripts (.sas)
Stata Scripts (.do)
SPSS Scripts (.sps)
Jupyter Notebooks (.ipynb)

3. Database Files (Formats for Storing Complete Databases)

SQL (.sql)
SQLite (.db, .sqlite)

And many more !

They are used to organize, query, and share data, avoiding redundancy and optimizing access.

These formats and languages are widely used across various fields of data science, statistical analysis, and scientific computing.

I offer to convert your data from one type to another, whenever possible.

I will process up to 2 files at most.

Any questions ? Feel free to send me a message