Including Data Files¶

In this section you will:

• Understand the importance of keeping large files out of your package.

• Learn some alternative approaches.

• Learn how to include small data files in your package.

Consider Alternatives¶

Never include large binary files in your Python package or git repository. Once committed, the file lives in git history forever. Git will become sluggish, because it is not designed to operate on large binary files, and your package will become an annoyingly large download.

Removing accidentally-committed files after the fact is possible but destructive, so it’s important to avoid committing large files in the first place.

Alternatives:

• Can you generate the file using code instead? This is a good approach for test data: generate the test data files as part of the test. Of course it’s important to test against real data from time to time, but for automated tests, simulated data is just fine. If you don’t understand your data well enough to simulate it accurately, you don’t know enough to write useful tests against it.

• Can you write a Python function that fetches the data on demand from some public URL? This is the approach used by projects such as scikit-learn that need to download large datasets for their examples and tests.

If you use one these alternatives, add the names of the generated or downloaded files to the project’s .gitignore file, which was provided by the cookiecutter template. This helps protect you against accidentally committing the file to git.

If the file in question is a text file and not very large (< 100 kB) than it’s reasonable to just bundle it with the package.

How to Package Data Files¶

What’s the problem we are solving here? If your Python program needs to access a data file, the naïve solution is just to hard-code the path to that file.

data_file = open('peak_spacings/LaB6.txt')


But this is not a good solution because:

• The data file won’t be included in the distribution: users who pip install your package will find it’s missing!

• The path to the data file depends on the platform and on how the package is installed. We need Python to handle those details for us.

As an example, suppose we have text files with Bragg peak spacings of various crystalline structures, and we want to use these files in our Python package. Let’s put them in a new directory named peak_spacings/.

# peak_spacings/LaB6.txt

4.15772
2.94676
2.40116

# peak_spacings/Si.txt

3.13556044
1.92013079
1.63749304
1.04518681


To access these files from the Python package, you need to edit the code in three places:

1. Include the data files’ paths to setup.py to make them accessible from the package.

# setup.py (excerpt)

package_data={
'YOUR_PACKAGE_NAME': [
# When adding files here, remember to update MANIFEST.in as well,
# or else they will not be included in the distribution on PyPI!
'peak_spacings/*.txt',
]
},


We have used the wildcard * to capture all filenames that end in .txt. We could alternatively have listed the specific filenames.

2. Add the data files’ paths to MANIFEST.in to include them in the source distribution. By default the distribution omits extraneous files that are not .py files, so we need to specifically include them.

# MANIFEST.in (excerpt)

include peak_spacings/*.txt

3. Finally, wherever we actually use the files in our scientific code, we can access them like this.

from pkg_resources import resource_filename

filename = resource_filename('peak_spacings/LaB6.txt')

# filename is the specific path to this file in this installation.
# We can now, for example, read the file.
with open(filename) as f:
# Read in each line and convert the string to a number.
spacings = [float(line) for line in f.read().splitlines()]