Data versioning is crucial, especially in machine learning and data analytics projects. 1. Use DVC to manage data versions, which enables efficient tracking and reproduction by recording data hashing instead of storing complete files; 2. Simple version management through naming specifications and metadata files (such as metadata.json), recording data sources, processing script versions and hash values; 3. Corresponding to the code version, such as specifying the current data version in the configuration file or recording data hashing in the log, so as to ensure that the experimental results are traceable and reproducible.
When using Python to do projects, data version management is often ignored, but in fact it is as important as code version management. Especially when you are doing machine learning, data cleaning or analysis projects, the data changes, and the results may all change. At this time, if you don’t know which version of data you are using, it will easily lead to problems.

The following methods can help you make the data version more clear and controllable.
Why do data also require version control?
You may have already managed code with Git, but data files are often much larger than code. Putting them directly into Git will slow down and even make the repository bloated. Moreover, the data changes are not changed line by line like the code, it is updated as a whole. For example, if you export a CSV file and add a few columns next time, if you don’t record it, it is difficult to know which data was used.

Therefore, the goal of data versioning is:
- Can trace what data is used for each experiment/run
- Reproduce historical results
- Avoid the embarrassment of "I remember changing the data but I don't know what I changed"
Use DVC for data version control
DVC (Data Version Control) is a version control tool specially designed for data, which is very convenient to use with Git. It does not actually store the data into Git, but records the hash value of the data and stores the real data in local or remote storage.

The basic usage is as follows:
- Install:
pip install dvc
- Initialization:
dvc init
- Add data directory to DVC management:
dvc add data/
- Commit Git:
git add data.dvc
and thengit commit
In this way, every time the data changes, DVC will generate a new hash and record it in Git. You can switch the data version like you would switch the code branch.
Record data versions with file names or metadata
If you don't want to introduce additional tools, you can also use naming standard metadata files to perform simple version control.
for example:
- The data file name is taken with the date or version number:
data_v1.csv
,data_20241010.csv
- Every time you export data, write a
metadata.json
to record:- Source of data
- Process script version
- Generation time
- Data hash value (such as MD5)
Although this method is simple, as long as you persist in doing it, you can avoid a lot of confusion.
Correlate the data version and the code version
Ideally, you should be able to use a Git commit to know which version of the data you used at that time. for example:
- Add a
config.py
or.env
file to the code to record the current data version path - Or print the data hash at the beginning of the training script and save it to the log
In this way, if you look back at the results of a certain run, you will know which data it is based on.
Basically that's it. The data version is difficult or not, but it is easy to ignore. Using DVC is a tricky approach, but if it is just a small project, it can be handled by naming specifications and metadata. The key is: don't let the data change become a black box .
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