We should attempt to follow the coding practices of the Python community as closely as possible. The folks who've taken the time to develop them have thought much more than any of us about what makes Python code easier to read. Assuming the majority of Python code outside Callysto also follows the community's style guide, this will also make outside resources easier to use.
PEP 8 is considered the de-facto style guide of the Python community. Note that one of the first things it talks about is the importance of being willing to break the rules. This may be of special importance to this project, as we are not looking to teach Python so much as teach computational thinking, and sometimes what is natural and idiomatic in a particular language is just plain weird compared to the majority of other languages. Still, we should think hard about deviations from PEP 8 and note them here.
A simple, but effective, way to make sure you're following these coding guidelines is to first take a look at existing content and follow the style of that. If you notice something about the style that seems odd to you, that may be a good time to review these guidelines and/or the underlying PEP 8 guidelines. In doing this, you may find that something in the repository isn't following these guidelines.
This will almost certainly happen with many people contributing code and as certain coding practices are modified. This section of the training manual won't assume the way your team is working. You may have an issue log that's kept to address later, you may have a single person in charge of the notebook that you inform, or if there's an appropriate Git process in place, you may be able to submit your own change and have it merged.
Anyone should feel comfortable with suggesting changes to these best practices, as we will all likely discover some important and necessary ones along the way. Conforming to PEP 8 when there's not a compelling reason to deviate will help avoid purely stylistic arguments. As the project gains momentum and more code, changes will be harder, so the sooner we discover any major changes we want to make, the better.
Note: If you're used to another language or following another style guide, it's natural for some things to feel awkward. This, in itself, isn't a great argument for a change to these guidelines. It's likely different things will feel awkward for everyone. When suggesting a change, be sure to consider the costs and benefits of making it. The more objective the argument you can make, the better its chances (provided it's also a good argument). But let's not let this stuff get out of control, either. At the end of the day, we should be spending most of our time actually writing code, not arguing over best practices.
When a module is imported by Python, the functions within it still need to be prefixed with the module's name. So, for example, a module called "fizz" with a function called "buzz" would be used in the following way:
This is called namespacing and helps to avoid conflicts when functions are named similarly. While namespacing helps avoid conflicts, it can also make Python code harder to read when overused and when the module names themselves are long. There are variations of the
import statement that can deal with these cases.
A common variation used with certain popular packages is to create a shorter local name. For example, the
numpy package is often imported like this:
import numpy as npx = np.array([2,3,1,0])
Aliasing modules like this is good practice when those modules are given the same local names by the community surrounding them. Numpy is a great example of this.
You should avoid defining your own local names. If there is a module without a community defined local name that you feel strongly should have one within our notebooks, make sure to bring it up with your fellow developers and team lead. If it is adopted, it will be something we'll want to do consistently in other modules within the project.
Functions (or submodules) that are used very often within a notebook can be specifically imported without a namespace:
from os import pathfrom math import pisome_path = path.join("folder1", "folder2", "file.txt")radius = 5area = pi * radius**2
There isn't a perfect rule for when and when not to do this, but here are some things to consider:
Does the function name tell someone reading the code enough about what it does without the module name? If you're writing a math notebook, using
cos is probably pretty self explanatory, and there's no real need to remind people that it's from the math package by forcing them to read and write
Does the module name help in understanding what the function does? For example, not only would
import join from os.path conflict with the basic array
join function, but it would also mislead readers as to the true purpose of
os.path.join. The true purpose is to join path components with the path separator, so leaving at least enough of the module path to reflect this makes for clearer code.
When in doubt, or if you're using the package infrequently, put the module path with the function. This is the clearest way of avoiding namespace conflicts and allowing people to trace where functions are coming from.
Two other variations worth noting:
from math import cos as cosine
This should probably be avoided in most cases, especially with standard Python libraries. The standard names will be easy to search for help outside of Callysto. The local names you might make up won't. However, there are cases where this may be useful. For example, if you find a rather obscure library with poorly named functions that nevertheless has one function (also poorly named) that's really, really useful, importing it this way would likely improve the readability of your code.
from math import *
As noted in PEP 8, you really shouldn't be doing this, as it's really easy to pollute your current namespace and wind up with weird bugs due to unintended function replacements. PEP 8 notes some exceptions, and there's one exception noted below for our purposes.
Here's a list of packages that we know of along with the local names you should use (based on local names used by the communities surrounding them) when importing and referencing them.
If there's a module that uses a common local name and is missing from this list, please add it.
import numpy as npimport scipy as spimport matplotlib as mplimport matplotlib.pyplot as pltimport pandas as pd
When developing a module, spend some time considering what aspects of computational thinking you're actually trying to demonstrate and whether or not you can assume any prerequisite knowledge in various programming concepts.
Whenever there is a need to reduce complexity without also reducing functionality (i.e. you want to show a fancy plot but not go over all of the necessary knowledge to produce that plot), consider extracting that complexity into a function in a separate Python file that is included in a notebook_code subfolder:
import osimport numpy as npimport sysimport hashlibdef complex_fizz():md5 = hashlib.md5()for x in ["buzz", "fizz", "buzz"]:md5.update(x.encode("utf-8"))return md5.hexdigest()
Example My Awesome Notebook.ipynb:
from notebook_code.my_awesome_support import *complex_fizz()os.mkdir('somedir')
You may note that the use of
import * is discouraged in PEP 8. In this case, we really do want to grab everything in the file. The functions we've defined in it are specifically for this notebook (so we're not worried about them clashing with existing functions), and we're bringing in imports, such as
numpy as np that are already namespaced.
By maintaining the convention of placing helper code under a notebook_code folder alongside the notebooks that use it, we make it easy to find this code for anyone who's curious. Don't go too crazy with this. Our main purpose of putting code like this in a separate file is to improve the ability to focus on the most important code in a notebook, not to obscure it. You'll likely only need one file per notebook and/or one file for common functionality between notebooks in the module. Try to keep imports of this code simple, as demonstrated above. For example, if you have the following:
from common import *
from common import *
from notebook_code.a import *
from notebook_code.b import *
This is really as complicated as things should ever get for most notebooks. And remember, you may not even need helper code. If your notebook code is simple enough and/or you can assume advanced users, you may not have much reason to do anything outside of it.
There likely won't be a need for more than one support file per notebook (or any at all), so we'll delay talking about more complex setups until they're needed. However, if we find we're extracting the same functions over and over again, this might be a sign that a separate package should be maintained.
This will involve some communication with your fellow developers, and it wouldn't hurt from time to time to browse any support
.py files, as functions extracted here may become good candidates for a separate package if they are used often enough across notebooks. You likely won't see functions that are exactly the same, but if they are similar enough, consider extracting common functionality and rewriting as necessary.
Please don't start writing any packages on your own. We'll want to coordinate anything like this and consider the maintenance implications of having to roll out updates to packages used by many notebooks. However, please do bring up opportunities if you spot them.
If your demo has many files associated with it (images,source code, etc.) it is best to keep them all organized into folders and subfolders, so users know what "package" to copy. Here's a suggested structure. You don't have to use every folder (if you don't store or use any data, for example, you won't need a "data" folder), but following similar names will help people navigate when using other notebooks:
.gitignore file in
data to ignore any files that you don't intend to store in Git when checking in changes to the notebook. For example, in one of your notebooks you may download some data but don't want people to have to continually download the data if they've already done it once. If you cache this under
data, you don't want someone to accidentally check it in. Adding an entry to
.gitignore will prevent that from happening. Or perhaps you encourage whoever's running the notebook to upload their own file to use in an exercise. You could create an
uploads folder under
data and ignore that, or just ignore all files of a particular filetype. If you're using a small amount of data that isn't sensitive, you may want to check it in with the notebook. In this case, you wouldn't include an entry in
.gitignore, or if you're already ignoring files of that type, you'd want to make a special entry to exclude the file you want to check in from the ones ignored. Example:
There's some more information on ignoring files from the good folks at GitHub.
Consider the following example of two ways of creating an array of integers:
x = [1,2,3,4]import numpy as npx = np.array([1,2,3,4])
This is an area where our experience with Python and especially some of the more popular packages may work against us. There are various optimizations and additions to arrays that make the use of
np.array to create them worth the extra typing and effort to read. However, if you've never encountered an array before, this makes it much harder to understand what the code is doing.
For Callysto, we should prefer the first, simpler way of initializing an array. An added benefit (and something to also consider) is that this is also a common way of initializing an array in many languages.
Note that we can always convert a regular array into a numpy array internally if it is needed for some interesting package we want to use, even if this is less efficient, it allows us to hide the complexity up front.
List comprehensions (i.e.
x = [a for a in y]) are generally considered "good Python" by the community. However, while many languages have mapping functions that serve a similar purpose, the syntax for list comprehensions is pretty unique to Python. When at all possible, you should use classic for-loops, even if you end up with less concise code:
x = for a in y:x.append(a)
PEP 8 has some great guidelines for comments in general. These guidelines are especially useful in any supporting python files.
But inside notebooks, also consider the other tool at your disposal: the markdown blocks themselves. If you've explained what you're doing in a code block in the markdown above it, there's no need to repeat that explanation in an actual code comment. With longer pieces of code, when you find yourself about to write a comment, consider whether it would be better to actually create another markdown block and continue your code below that.
Rule of thumb: the more important a particular section of code is to understand, the more likely it should be its own code block.
Good variable names, abstractions, modularization, and data structures are just as important for readability as comments.
Example: Which is easier to understand?
nums = range(0,10)x = 0 # even sumy = 0 # odd sumfor num in nums:# check if the number is evenif num % 2 == 0:x = x + numelse:y = y + num
nums = range(0,10)even_sum = 0odd_sum = 0def is_even(x):return x % 2 == 0for num in nums:if is_even(num):even_sum = even_sum + numelse:odd_sum = odd_sum + num
is_even function may be overkill once you get used to seeing the
% operator enough. Combined with more appropriately named variables, however, it makes it much easier to follow what the code is doing. Once someone gets comfortable with the
sum functions, which are nice abstractions for certain operations over lists (and assuming we also define an
is_odd function), we can write even more readable code:
even_nums = filter(is_even, nums)odd_nums = filter(is_odd, nums)even_sum = sum(even_nums)odd_sum = sum(odd_nums)
However, it's important to note that passing functions into functions is a more advanced programming concept, and the improved readability here relies heavily on the understanding of this concept.
Be careful when installing Python packages. We don't want to have any install requirements for end users of these notebooks, so if there's a package you really need, this will eventually have to be communicated to the maintainers of hub.callysto.ca.
Keep a list of any packages you install this way and consider how much you really need it. How much code would you have to write in a supporting python file to do what the library is doing for you? Reinventing the wheel carries a cost (you likely haven't thought as much about the problem as the maintainers of an existing package created to solve it), but external dependences have their own costs.
In the case where certain libraries are not installed when running or developing notebooks, users may install packages in their userspace via:
pip install PACKAGE_NAME --user
in a terminal session or
!pip install PACKAGE_NAME --user
in a notebook code block.
When installing packages in the userspace, it should be noted which are added in order to subsequently have the package installed system-wide (if it is a commonly used package). Alternatively, the notebook or a setup file should be configured to automatically install the packages on a user by user basis.