Skip to content


Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

🌧️ rain

build codecov

A live example to illustrate python packaging, testing, building, & deploying

rain is an online reference that can be explored by humans. It provides a living, dynamic alternative to commonly available static documentation. This repository covers the following topics.

  • correct way of importing within packages
  • entry point scripts
  • storing and distributing data files
  • properly storing version information in one location
  • specifying dependencies
  • running and skipping tests
  • obtaining coverage metrics and uploading them to a website like
  • installing and uninstalling this package locally
  • building this package locally
  • example CI/CD using GitHub Actions
  • deploying to PyPI
  • adding badges to your README

How is it different from pyscaffold or cookiecutter?

The aims and capabilities of rain are very different than that of pyscaffold or cookiecutter.


Both pyscaffold and cookiecutter provide you with executable tools to generate a package skeleton on disk. They are excellent tools for expert package writers who want a quick start. rain provides no such capability. In fact, if you were to clone this repository to start your project, you'll have to modify a lot of code by hand which would almost always take more time (and may still have errors) than if you were to manually create all the files one-by-one.


rain is a live and unencumbered reference that aims to educate not execute.

Online reference

Unlike pyscaffold or cookiecutter, you don't have to execute any command on your machine to get a working example as a reference. You can explore the code online on GitHub.

Explanation by examples

Concepts and decisions are explained via examples. Package authors often want to understand the consequences of their decisions. For example, in this README, we answer the question of why can we list dependencies in both and requirements.txt.

Less code to browse through

The famous python packages (for example, numpy or pandas) probably have the functionality/feature that you want to emulate in your own package but you will have to browse through a lot of complexity before you can find what you're looking for. This repository only contains files that help illustrate python packaging.

Add features at your own pace

Tools that generate a package skeleton either overwhelm you with all the features from the start or require you to know enough to select only the features you need. rain is a reference that you can consult to understand various features, make an informed decision on which features you need, and then learn how to correctly implement them manually in your own package.

Tests and builds via GitHub Actions

Writing a package often comes with the additional task of setting up tests and CI/CD pipeline(s). These concepts are best explained via live, working examples instead of theory alone.

Clone/fork and experiment

Since rain is live, you can clone or fork this repository and experiment with feature modifications before you decide to implement them in your own python package.

Folder Structure

β”œβ”€β”€ .coveragerc
β”œβ”€β”€ .github
β”‚Β Β  └── workflows
β”‚Β Β      └── build.yml
β”œβ”€β”€ .gitignore
β”œβ”€β”€ LICENSE
β”œβ”€β”€ pytest.ini
β”œβ”€β”€ rain
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”œβ”€β”€ module_circular
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  └──
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”œβ”€β”€ module_three
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  └──
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”œβ”€β”€ resources
β”‚Β Β  β”‚Β Β  └── mathematicians.txt
β”‚Β Β  β”œβ”€β”€ scripts
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  └──
β”‚Β Β  β”œβ”€β”€ tests
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  β”œβ”€β”€
β”‚Β Β  β”‚Β Β  └──
β”‚Β Β  └──
β”œβ”€β”€ requirements.txt
β”œβ”€β”€ scripts
β”‚Β Β  β”œβ”€β”€ build-package
β”‚Β Β  β”œβ”€β”€
β”‚Β Β  └──

The package is designed to work with both python2 and python3 but is only tested on python3, as of now. Throughout this documentation, python3 typically refers to the user-site python installations that are outside the virtual environments. Within a virtual environment, replace python3 with python. Same goes for pip3 to pip.

Why do we define dependencies in both and requirements.txt?

Both these files serve different objectives that are somewhat mutually exclusive and therefore we often need both. This Stack Overflow post describes this issue quite well. This post also describes how you can only list the dependencies in and "link" them in requirements.txt. Expanding upon this post:


  • helps you setup your environment in "one fell swoop"
  • can be used by pip instead of using python
  • makes setting up virtual environments easy
  • lets you install dependencies of a package easily without having to install the package (rain, in this case) itself. This is especially useful for CI/CD pipelines (see an example in rain).
  • allows you to install packages that are not necessarily dependencies of your package. For example, your package (eg: rain) may not depend on pandas but pandas may be a commonly used package that is used alongside rain. In such a case, it may not be appropriate to put pandas in's install_requires, setup_requires, tests_require, or extras_require. One reason is that while you may use pandas alongside rain but not everyone else will.
  • is easier to edit than and allows comments

  • helps you install all dependencies when installing the package (depending upon options used with python install)
  • ensures that all specified dependencies of the package are installed before you install the intended package
  • specifies only those dependencies that are necessary for the packages

Run Code

You don't need to install the package to use the code. You just need to be in $REPO_ROOT. The following example (if it runs) verifies that a complicated importing situation within a package correctly works.

python3 -c "from rain import banana; print(banana())"
# plantain

Run Tests

Run selected tests

Run tests from $REPO_ROOT; you will get an error otherwise. You don't need to install the package to run tests. See pytest documentation for more examples.

# Run all tests without coverage
python3 -m pytest rain/tests

# Run only the "sleepy" tests
python3 -m pytest -m sleepy rain/tests

# Run the non-sleepy tests
python3 -m pytest -m "not sleepy" rain/tests

# Run tests that are sleepy but not random
# You can use any valid python syntax within quotes ""
python3 -m pytest -m "sleepy and (not random)" rain/tests"

Make sure that the markers are defined in $REPO_ROOT/pytest.ini to avoid a warning.

Run tests with coverage

You need to install pytest-cov for this to work. See pytest-cov documentation for more details.

Without a .coveragerc file

By default, you can get coverage for everything including the tests. The tests typically have 100% coverage (unless you don't run specific tests) which inflates the coverage metrics.

# In $REPO_ROOT with .coveragerc file deleted

# Get coverage for all tests
python3 -m pytest --cov=rain rain/tests

# Get coverage for only the "sleepy" tests. This will make the coverage
# for some test files be less than 100%.
python3 -m pytest -m sleepy --cov=rain rain/tests

# Get coverage for only the non-"sleepy" tests. This will make the coverage
# for some test files be less than 100%.
python3 -m pytest -m "not sleepy" --cov=rain rain/tests

Typical example:

# In $REPO_ROOT with .coveragerc file deleted
$ python3 -m pytest --cov=rain rain/tests
---------- coverage: platform darwin, python 3.7.3-final-0 -----------
Name                                 Stmts   Miss  Cover
rain/                        17      6    65%
rain/                           5      2    60%
rain/                    9      1    89%
rain/                       9      0   100%
rain/                  6      0   100%
rain/module_three/            2      0   100%
rain/module_three/       8      1    88%
rain/module_three/       7      1    86%
rain/                       9      0   100%
rain/scripts/                 0      0   100%
rain/scripts/              12     12     0%
rain/tests/                   0      0   100%
rain/tests/        10      0   100%
rain/tests/           17      0   100%
rain/                          1      0   100%
TOTAL                                  112     23    79%

With a .coveragerc file

To exclude the tests from coverage calculation, put a .coveragerc file in $REPO_ROOT. See more details at documentation. See $REPO_ROOT/rain/.coveragerc as an example. You can run the same commands as above.

The following is a typical output in which we run all the tests but don't count $REPO_ROOT/rain/tests and $REPO_ROOT/rain/scripts in the total coverage percentage.

# In $REPO_ROOT with .coveragerc file present
$ python3 -m pytest --cov=rain rain/tests
---------- coverage: platform darwin, python 3.7.3-final-0 -----------
Name                                 Stmts   Miss  Cover
rain/                        17      6    65%
rain/                           5      2    60%
rain/                    9      1    89%
rain/                       9      0   100%
rain/                  6      0   100%
rain/module_three/            2      0   100%
rain/module_three/       8      1    88%
rain/module_three/       7      1    86%
rain/                       9      0   100%
rain/                          1      0   100%
TOTAL                                   73     11    85%

Installation & Uninstallation

Install outside a virtual environment

Install using pip at user-site instead of python This makes it easier to uninstall. The script rain_maker is installed in user-site/bin.

# Installation
pip3 install --user ./

# Uninstallation
# From any location. This will uninstall the script `rain_maker` as well.
pip3 uninstall rain

Install within a virtual environment

# For fish shell that has virtualfish installed
vf new rain  # Uses Python 3 by default
vf activate rain
which pip  # Check that this is the pip in the `rain` virtualenv
pip install ./
pip uninstall rain  # Try repeatedly until `pip show rain` is clean
pip show rain
vf deactivate

Run script to check your installation

# From anywhere when installed outside the virtual environment
# and assuming user-site/bin is is your PATH.
$ rain_maker --help
$ rain_maker --times 10
$ rain_maker

Sample output of rain_maker looks like:

$ rain_maker
Galois, Weirstrauss, Descartes, Euler, Cauchy, VonNeumann, Russell, Chebyshev, Newton, Leibniz
Russell, DeMoivre, Cauchy, Erdos, Bayes, Newton, Leibniz, Peano, Weirstrauss, Nash
Taylor, Galois, Godel, Riemann, Weirstrauss, Pythagoras, Leibniz, Chebyshev, Russell, Maxwell
Turing, Frege, Cantor, Bernoulli, Weirstrauss, Einstein, Boole, Kolmogorov, Gauss, Bayes
Godel, Poincare, Banach, Bernoulli, Newton, Maxwell, Lagrange, Huygens, Riemann, Chebyshev

Build Package & Source Distributions

You can build a tarball and a wheel like so.

# Ensure that the package `wheel` installed.

# builds only .tar.gz
python sdist bdist_wheel

# builds both zip and tar.gz
python3 sdist --formats=gztar,zip bdist_wheel

# Check $REPO_ROOT/dist for both tarball and wheel files.

If you encounter an error like error: invalid command 'bdist_wheel', you may not have wheel package installed. You can install wheel via pip:

pip install --user wheel

Including package data and other files

rain contains a data file $REPO_ROOT/rain/resources/mathematicians.txt that is used by the package. This file is used by the function load_mathematicians . We want this data file to travel with any distribution of this python package. In rain, we generate three distributions -- a wheel, a tar.gz, and a zip file. The tar.gz and zip files have the exact same contents and behavior with the difference only being in the compression format. But, a wheel and a tar.gz/zip often different contents and have different purposes.

Wheel (.whl) vs sdists (.tar.gz or .zip)

This Stack Overflow post describes the advantages of a wheel over a tar.gz (or zip) file. A tar.gz/zip file is great for distributing source code but installing from a tar.gz/zip file requires use of distutils and setuptools which is slow and requires running arbitrary code. A wheel avoids these issues and is the better way to distribute/store artifacts (analogous to jar in Java).

Packaging data within distributions

There seem to be two ways to include data in the distributions -- via and via Sadly, the combination of these produces a variety of results as shown in the table below. You can also read about it here.

package_data in include_package_data in .whl contains data .zip contains data .tar.gz contains data
1 Not present Not present Does not exist or is empty ❌ ❌ ❌
2 {PACKAGE_NAME: ['resources/*.txt']} Not present Does not exist or is empty βœ… βœ… βœ…
3 {PACKAGE_NAME: ['resources/*.txt']} True Does not exist or is empty βœ… ❌ ❌
4 Not present True Does not exist or is empty ❌ ❌ ❌
5 {PACKAGE_NAME: ['resources/*.txt']} True include */resources/*.txt βœ… βœ… βœ…
6 {PACKAGE_NAME: ['resources/*.txt']} Not present include */resources/*.txt βœ… βœ… βœ…
7 Not present Not present include */resources/*.txt ❌ βœ… βœ…
8 Not present True include */resources/*.txt βœ… βœ… βœ…

(there was no need to put a file in $REPO_ROOT/rain/resources for any of the above rows)

The above table demonstrates the confusing behavior of python packaging. Rows #2, #5, #6, #8 seem to ensure that we can distribute the data in all three formats. Note that #7 is especially egregious because it creates a .whl file that does not have the data resuling in an error when the package is used (even though the installation succeeds).

The following is a good rule of thumb to remember:

  1. If include_package_data=True, you must provide a
  2. If you want to enforce, you must set include_package_data=True.

In rain, we choose the approach from row #8 because it works for all three distribution formats while still allowing us to list the files in only one location (

Including tests in distributions

Typically, tests folder contains file which makes it a python module. In rain, rain/tests does indeed contain file. When tests are indeed a python module, the best way to include tests is to simply list tests as a package in like so. Python modules are always distributed in all formats irrespective of whether or not you have a file.


Why does my .tar.gz/.zip file contain a rain.egg-info folder?

There are messy reasons for this and you can see this Stack Overflow post for more discussion. Note that trying to exclude PACKAGE_NAME.egg-info via doesn't work. This seems to be alluded to here. Unless you have a very advanced/specific use case, having PACKAGE_NAME.egg-info in your .tar.gz/.zip file should not cause any problems and you can simply ignore it.

Example CI/CD using GitHub Actions

Setting up CI/CD depends on the CI/CD platform you are using. As of writing this README, there is no universal CI/CD. However, most modern CI/CD follow the same overall process:

  1. Define the CI/CD steps in some .yml file that is stored in the repository as a git-tracked file. You can have multiple .yml files defining different steps for different purposes. Here are some common steps. Not all workflows may include all of these steps.
    • setup the test environment
    • pull your repository with the changes that need to be tested/deployed
    • run tests, possibly in various versions of python
    • get code coverage metrics
    • upload the coverage metrics to a website like
    • build the package
    • upload the package as an artifact (eg: tarball or a wheel)
    • deploy the package to run in production
  2. Define trigger(s) than runs the steps specified in the .yml file. Here are some examples of triggers. Of course, you can define other triggers.
    • a push to master, as well as
    • a pull request to merge to master
    • cutting a release (eg: by git taging a commit on master)
  3. The steps defined in a .yml file are typically run in an ephemeral virtual machine or a docker container that exists only for the purpose of implementing CI/CD.

GitHub Actions is one CI/CD framework. You can see the corresponding YAML files in .github/workflows.

Uploading coverage metrics to

The build.yml workflow shows this example with comments. The process has two main parts:


Releasing your package essentially involves attaching a sensible version (eg: using Semantic Versioning) to your code and releasing the code as a distributeable (eg: a tarball or a wheel). There are various automated ways to "cutting a release" which we don't cover here. Instead, we note the individual steps involved.

Version in

The version in is the canonical source of truth for the version. This is the version used by python sdist bdist_wheel. The resulting files (tarball, wheel) in $REPO_ROOT/dist folder are named using the version specified in as the version argument to setuptools.setup() function. These are some example filenames:

$ ls dist/

Typically, we want to make the package version available to the python interpreter as well. However, we want to store the version exactly once within the source code -- this way we only have to modify it at one place when we release a new version. There are various ways to do this and we follow one specific method in rain.

  • We define the version as a str-valued variable called __version__ in
  • We execute inside file in a particular way, as shown here. This is done for two reasons (a) compatibility between python 2 and python 3, (b) avoiding referencing the variable __version__ without defining it (while it would be legal python syntax to execute and reference __version__, linters and IDEs flag that as an error)
  • We import __version__ in

Version via git tag

We can git tag a commit (on an appropriate branch such as master or release) with a version string. Note that even though we use git tag to mark versions, git tags are not specific to versions (you can do git tag rick-and-morty; git won't care). We can do this via command line or via GitHub Web UI (recommended). An example of command line example is:

# Commit all changes and ensure you're on the correct branch
git tag x.y.z

# Push the tags upstream. Push only the tag you made. Note that
# `git push origin --tags` will push all the tags which can cause problems.
git push origin x.y.z

On GitHub, releases are available on the Releases page. This works even for tags generated from command line (as shown above) but it's better to Draft a new release via GitHub webpage because it lets you easily write good release notes.

Note the version in git tag may not be the same as the version in Conflicts between the version in git tag and version in can cause CI/CD errors.

Steps to follow

The following steps when performed in order helps you avoid CI/CD errors.

  1. Commit all code changes to master (typically via a pull request).
  2. Pull all changes to master locally.
    git checkout master
    git pull
  3. Create a new branch off of master. Let's call this new branch release-new-version.
    git checkout master
    git checkout -b release-new-version
  4. Bump up the version in Ensure that the new version string is something that has never been used before. This is important or you may get errors when you deploy and you may have to repeat all of the steps.
  5. Push the new branch to GitHub
    git push --set-upstream origin release-new-version
  6. Create a pull request from release-new-version to master. This can be done via GitHub webpage. The benefit of bumping up the version via a PR is that you can run your CI/CD workflow on the PR. Typically, this CI/CD builds a python package (even if it doesn't deploy it) and can find errors before you finally merge.
  7. Once you've verified that everything looks good, merge the PR to master (after getting approval of reviewers, if necessary). At this point, any build off of master would have the latest version. If you have CI/CD pipeline that runs on merge to master, let it run to confirm that everything looks good.
  8. Now, when you're sure that everything is good, git tag the commit on master either via command line or via GitHub releases (recommended), as discussed in the subsection above.
  9. At this point, if you have a CI/CD pipeline setup to deploy the package (such as to an artifact repository or PyPI), it will run and deploy your package.

Deploying to PyPI

Since I don't own the name rain on PyPI (and, at this point, I don't want to change the name of my package), I don't have a live example of deploying this package on PyPI.

Please be careful before deploying a package to PyPI (or Test PyPI). PyPI and Test PyPI are open to the public and once a package is uploaded, it might not be possible to undo it. You don't want to accidentally upload any proprietary or private company code/data to PyPI (or Test PyPI).

Note that you cannot overwrite an existing version of your package on PyPI (or Test PyPI). For example, if you've already uploaded version 0.1.2 to PyPI (or Test PyPI), you cannot modify the source and overwrite the same version 0.1.2 on PyPI (or Test PyPI). You must bump the version up and then deploy again. This is because someone else might've written code against version 0.1.2 and we don't want to break their code.

Deploying from local machine

We can also deploy by building the package locally and then using twine to upload the builds to PyPI. Note that you will need an account on PyPI and on Test PyPI (optional but recommended). While uploading the package to PyPI (or Test PyPI), you will need to enter your account password. This prevents any random person from uploading a new version of your package. These are the typical instructions but they're not relevant to rain, as mentioned above.


# Clean the git state. An unclean git state (even untracked files) can
# accidentally be included in your build if you're not careful.

# Build the package locally (requires `wheel` package to be installed).
# See $REPO_ROOT/dist for the tarball and the wheel
python sdist bdist_wheel

# Upload to PyPI (or Test PyPI)
# Requires `twine` package to be installed (`pip install --user twine`).
twine upload --verbose --repository testpypi dist/*
twine upload --verbose --repository pypi dist/*

Note that you may need to add the following lines to your $HOME/.pypirc:

index-servers =



Deploying via GitHub Actions

We can use GitHub Actions to define a workflow that publishes to Test PyPI and production PyPI. rain doesn't contain such as example but you can see an example in another python package called flicker. The official instructions provide some more detail.

Note about using Markdown-formatted long_description

Many repositories use Markdown-formatted as the value to the long_description argument in setuptools.setup() in This is especially true for GitHub repositories and rain itself does this.

PyPI, by default, expects long_description in reStructuredText. If a Markdown-formatted long_description is provided when PyPI is expecting reStructuredText, publishing to PyPI fails with an error message like this:

HTTPError: 400 Client Error: The description failed to render in the default
format of reStructuredText. See
for more information.

Luckily, this can be easily fixed by simply providing the markup format in


See the example in this repository here. Also, see the FAQs on Test PyPI or PyPI.

Adding badges to your README

Adding badges is a great way of letting people know about key information. See the top of this README file in raw format to see the Markdown that include badges.

GitHub Actions badge

After you've defined a Github Action (aka workflow), you can get the Markdown for the badge from the GitHub Actions webpage. Click on an event (eg: build for rain) and click on Create Status badge. Here is an example:

![build]( coverage badge

Once you've added a GitHub repository to your account, you can obtain the badge Markdown from the "Badge" tab in the "Settings" page. Here is an example:


PyPI version badge

If you package is on PyPI, you can generate a badge Markdown from a third-party website such as or This is an example Markdown for another package (because this package is not the rain package on PyPI).

[![PyPI Latest Release](](

Correct way to do intra-package imports

Python importing is complicated. These links provide good information:

But, here I provide a quick guide. There are four types of imports:

  • Absolute import
    • Absolute "full-path" import
    • Absolute "non-full-path" import
  • Relative import
    • Explicit relative import
    • Implicit relative import
# Absolute "non-full-path" import
# Standard way for client code to use a package. Package typically is
# installed but need not be installed if running the following line is run
# in $REPO_ROOT. This is also used in test files; see
# $REPO_ROOT/rain/tests/test_*.py for more details.
from rain import function_one

# Absolute "full-path" import
# Recommended way to perform intra-package imports. See example in
# $REPO_ROOT/rain/ and $REPO_ROOT/rain/
# This is typically not used for tests but can be used; see
# $REPO_ROOT/rain/tests/test_*.py for more details.
from rain.module_plantain import plantain

# Explicit relative import
# This is second-best for intra-package imports after the Absolute "full-path"
# import. Must start with `from`. The only way to perform relative imports
# in Python 3 or in Python 2 (with `absolute_import` imported). The following
# line should exist in a sibling file/module of
# $REPO_ROOT/rain/
from .module_plantain import plantain

# Implicit relative import
# Never use this. This is illegal in Python 3. In Python 2, this becomes
# illegal if we import `absolute_import` from `__future__`. The following line
# would exist in a sibling file/module of $REPO_ROOT/rain/
from module_plantain import plantain

See $REPO_ROOT/rain/ and $REPO_ROOT/rain/ as examples.

See the Stack Overflow post that recommends using absolute full-path imports within a package, whenever possible. But, explicit relative imports are okay.

Circular imports or dependencies

It is common to encounter circular dependencies in complicated projects with many interrelated classes. As an example, consider any dataframe with a groupby method. For specificity, consider a pandas DataFrame: DataFrame.groupby() returns a DataFrameGroupBy object and DataFrameGroupBy.count() returns a DataFrame object. Since DataFrame and DataFrameGroupBy are defined in different files (for maintainability reasons), we need to import DataFrameGroupBy in the file that contains the definition of DataFrame and vice versa. This forms a circular dependency.

When we have a circular dependency, we cannot import at the top of the files like we usually do. Doing so causes ImportError, as discussed in the StackOverflow posts, here and here.

# Example ImportError which we fixed using one of the methods below
ImportError: cannot import name 'Array' from 'rain.module_circular.array'

The error appears only when you actually import from one of the files that are involved in the circular import. Thus, successful import of the package alone is insufficient in testing whether or not the circular dependencies are indeed imported correctly. Note that the error exists irrespective of whether you use absolute full-path imports and explicit relative imports. The issue of circular dependencies is orthogonal to the issue of absolute vs relative imports.

In rain, we have recreated a similar circular dependency using Array and GroupedArray in the files $REPO_ROOT/rain/module_circular/ and $REPO_ROOT/rain/module_circular/, respectively.

  1. calls GroupedArray
  2. GroupedArray.groups() calls Array

See $REPO_ROOT/rain/scripts/ for a working demo.

Note that having a circular dependency is itself not a problem for python. The problem is only that we cannot import at the top of the files. There are a few ways to fix circular import issues:

  1. As mentioned in the comments here, we can move the two classes in the same file. For our example, that would mean defining both Array and GroupedArray classes in the same file. This option may be suitable for small classes but is often not recommended for complicated projects with large classes for readability and maintainability.

  2. Local or deferred imports. Instead of importing at the top of the file, we import locally, within a method, as needed. See $REPO_ROOT/rain/module_circular/ for an example. Pandas uses this method as well, see this line.

  3. Move the import statements involved in the circular dependency to the end of the file, as suggested here.

Finally, for circular imports needed for type hinting alone, this may be yet another solution.

Check that everything imports correctly

You can run the following commands in one of two ways:

  • Run from $REPO_ROOT without installing the package. Event if you install the local one, i.e. $REPO_ROOT takes precedence over the installed code that is typically in user-site/bin.
  • Run from outside $REPO_ROOT after installing the package.

The banana() function is the one with complicated import order issue but should import correctly.

from rain import (function_one, function_two, function_three, function_four,
                  apple, banana, plantain)
# one
# one
# one
# four
# apple
# plantain
# plantain

Check that circular dependencies can be imported correctly. Alternatively, you can run $REPO_ROOT/rain/scripts/

from rain import Array
x = Array([1, 2, 3, 1])
# Array[3]


This section lists some common errors.

Cannot import the package

Not listing the package(s) in file

This typically happens when you're sure that you've installed your package and you're using the matching/corresponding version of python executable that can find the package in the site-packages where the package was installed. This might be because you forgot to provide the list of package(s) to setup() in file. This is an example of what you should add:

# In file

If you don't list the package(s), then you might find that your package installs but is actually empty in site-packages location.




Contributions are welcome. If you find errors or identify need for improvement, please look into Issues and open an issue.


🌧️ A live example to illustrate python packaging, testing, building, & deploying








No packages published