Best Practice

When writing your own scripts python is great for new developers to pick up and become productive, but you can also pick up odd habits or at least write scripts that are not easy for others to understand.

For your own work this is of course fine, but if you want to collaborate with others or have your work included with blender there are practices we encourage.

Style Conventions

For Blender/Python development we have chosen to follow python suggested style guide to avoid mixing styles amongst our own scripts and make it easier to use python scripts from other projects.


This style guide is known as pep8 and can be found here

A brief listing of pep8 criteria.

  • camel caps for class names: MyClass
  • all lower case underscore separated module names: my_module
  • indentation of 4 spaces (no tabs)
  • only use explicit imports, (no importing *)




    bpy.context.scene.render.image_settings.file_format = 'PNG'
    bpy.context.scene.render.filepath = "//render_out"


# <pep8 compliant>

To enable line length checks use this instead.

# <pep8-80 compliant>

User Interface Layout

Some notes to keep in mind when writing UI layouts:

  • UI code is quite simple. Layout declarations are there to easily create a decent layout.

    General rule here: If you need more code for the layout declaration, then for the actual properties, you do it wrong.

Example layouts:

  • layout()

    The basic layout is a simple Top -> Bottom layout.

  • layout.row()

    Use row(), when you want more than 1 property in one line.

    row = layout.row()
  • layout.column()

    Use column(), when you want your properties in a column.

    col = layout.column()
  • layout.split()

    This can be used to create more complex layouts. For example you can split the layout and create two column() layouts next to each other. Don’t use split, when you simply want two properties in a row. Use row() for that.

    split = layout.split()
    col = split.column()
    col = split.column()

Declaration names:

Try to only use these variable names for layout declarations:

  • row for a row() layout
  • col for a column() layout
  • split for a split() layout
  • flow for a column_flow() layout
  • sub for a sub layout (a column inside a column for example)

Script Efficiency

List Manipulation (General Python Tips)

Searching for list items

In Python there are some handy list functions that save you having to search through the list.

Even though you’re not looping on the list data python is, so you need to be aware of functions that will slow down your script by searching the whole list.

if list_item in my_list: ...

Modifying Lists

In python we can add and remove from a list, this is slower when the list length is modified, especially at the start of the list, since all the data after the index of modification needs to be moved up or down 1 place.

The most simple way to add onto the end of the list is to use my_list.append(list_item) or my_list.extend(some_list) and the fastest way to remove an item is my_list.pop() or del my_list[-1].

To use an index you can use my_list.insert(index, list_item) or list.pop(index) for list removal, but these are slower.

Sometimes its faster (but more memory hungry) to just rebuild the list.

Say you want to remove all triangular faces in a list.

Rather than...

faces = mesh.tessfaces[:]  # make a list copy of the meshes faces
f_idx = len(faces)     # Loop backwards
while f_idx:           # while the value is not 0
    f_idx -= 1

    if len(faces[f_idx].vertices) == 3:
        faces.pop(f_idx)  # remove the triangle

It’s faster to build a new list with list comprehension.

faces = [f for f in mesh.tessfaces if len(f.vertices) != 3]

Adding List Items

If you have a list that you want to add onto another list, rather than...

for l in some_list:


my_list.extend([a, b, c...])

Note that insert can be used when needed, but it is slower than append especially when inserting at the start of a long list.

This example shows a very sub-optimal way of making a reversed list.

reverse_list = []
for list_item in some_list:
    reverse_list.insert(0, list_item)

Python provides more convenient ways to reverse a list using the slice method, but you may want to time this before relying on it too much:

some_reversed_list = some_list[::-1]

Removing List Items

Use my_list.pop(index) rather than my_list.remove(list_item)

This requires you to have the index of the list item but is faster since remove() will search the list.

Here is an example of how to remove items in 1 loop, removing the last items first, which is faster (as explained above).

list_index = len(my_list)

while list_index:
    list_index -= 1
    if my_list[list_index].some_test_attribute == 1:

This example shows a fast way of removing items, for use in cases where you can alter the list order without breaking the scripts functionality. This works by swapping 2 list items, so the item you remove is always last.

pop_index = 5

# swap so the pop_index is last.
my_list[-1], my_list[pop_index] = my_list[pop_index], my_list[-1]

# remove last item (pop_index)

When removing many items in a large list this can provide a good speedup.

Avoid Copying Lists

When passing a list/dictionary to a function, it is faster to have the function modify the list rather than returning a new list so python doesn’t have to duplicate the list in memory.

Functions that modify a list in-place are more efficient than functions that create new lists.

This is generally slower so only use for functions when it makes sense not to modify the list in place.

>>> my_list = some_list_func(my_list)

This is generally faster since there is no re-assignment and no list duplication.

>>> some_list_func(vec)

Also note that passing a sliced list makes a copy of the list in python memory

>>> foobar(my_list[:])

If my_list was a large array containing 10000’s of items, a copy could use a lot of extra memory.

Writing Strings to a File (Python General)

Here are 3 ways of joining multiple strings into 1 string for writing

This really applies to any area of your code that involves a lot of string joining.

Python’s string addition, don’t use if you can help it, especially when writing data in a loop.

>>> file.write(str1 + " " + str2 + " " + str3 + "\n")

String formatting. Use this when you’re writing string data from floats and ints

>>> file.write("%s %s %s\n" % (str1, str2, str3))

Python’s string joining function. To join a list of strings

>>> file.write(" ".join([str1, str2, str3, "\n"]))

join is fastest on many strings, string formatting is quite fast too (better for converting data types). String arithmetic is slowest.

Parsing Strings (Import/Exporting)

Since many file formats are ASCII, the way you parse/export strings can make a large difference in how fast your script runs.

There are a few ways to parse strings when importing them into Blender.

Parsing Numbers

Use float(string) rather than eval(string), if you know the value will be an int then int(string), float() will work for an int too but it’s faster to read ints with int().

Checking String Start/End

If you’re checking the start of a string for a keyword, rather than...

>>> if line[0:5] == "vert ": ...


>>> if line.startswith("vert "):

Using startswith() is slightly faster (approx 5%) and also avoids a possible error with the slice length not matching the string length.

my_string.endswith(“foo_bar”) can be used for line endings too.

If you are unsure whether the text is upper or lower case use lower() or upper() string function.

>>> if line.lower().startswith("vert ")

Use try/except Sparingly

The try statement is useful to save time writing error checking code.

However try is significantly slower than an if since an exception has to be set each time, so avoid using try in areas of your code that execute in a loop and runs many times.

There are cases where using try is faster than checking whether the condition will raise an error, so it is worth experimenting.

Value Comparison

Python has two ways to compare values a == b and a is b, the difference is that == may run the objects comparison function __cmp__() whereas is compares identity, that both variables reference the same item in memory.

In cases where you know you are checking for the same value which is referenced from multiple places, is is faster.

Time Your Code

While developing a script it’s good to time it to be aware of any changes in performance, this can be done simply.

import time
time_start = time.time()

# do something...

print("My Script Finished: %.4f sec" % time.time() - time_start)