Before diving into the FlatBuffers usage in Python, it should be noted that the [Tutorial](@ref flatbuffers_guide_tutorial) page has a complete guide to general FlatBuffers usage in all of the supported languages (including Python). This page is designed to cover the nuances of FlatBuffers usage, specific to Python.
You should also have read the [Building](@ref
flatbuffers_guide_building) documentation to build
and should be familiar with [Using the schema compiler](@ref
flatbuffers_guide_using_schema_compiler) and [Writing a schema](@ref
The code for the FlatBuffers Python library can be found at
flatbuffers/python/flatbuffers. You can browse the library
code on the [FlatBuffers GitHub page](https://github.com/google/flatbuffers/tree/master/
The code to test the Python library can be found at
flatbuffers/tests. The test code itself is located in
To run the tests, use the [PythonTest.sh](https://github.com/google/flatbuffers/ blob/master/tests/PythonTest.sh) shell script.
Note: This script requires python to be installed.
Note: See [Tutorial](@ref flatbuffers_guide_tutorial) for a more in-depth example of how to use FlatBuffers in Python.
There is support for both reading and writing FlatBuffers in Python.
To use FlatBuffers in your own code, first generate Python classes
from your schema with the
--python option to
flatc. Then you can include both FlatBuffers and the
generated code to read or write a FlatBuffer.
For example, here is how you would read a FlatBuffer binary file in
Python: First, import the library and the generated code. Then read a
FlatBuffer binary file into a
bytearray, which you pass to
import MyGame.Example as example import flatbuffers buf = open('monster.dat', 'rb').read() buf = bytearray(buf) monster = example.GetRootAsMonster(buf, 0)
Now you can access values like this:
hp = monster.Hp() pos = monster.Pos()
The Flatbuffers python library also has support for accessing scalar
vectors as numpy arrays. This can be orders of magnitude faster than
iterating over the vector one element at a time, and is particularly
useful when unpacking large nested flatbuffers. The generated code for a
scalar vector will have a method
<vector name>AsNumpy(). In the case of the Monster
example, you could access the inventory vector like this:
inventory = monster.InventoryAsNumpy() # inventory is a numpy array of type np.dtype('uint8')
inventory =  for i in range(monster.InventoryLength()): inventory.append(int(monster.Inventory(i)))
Numpy is not a requirement. If numpy is not installed on your system,
then attempting to access one of the
will result in a
There currently is no support for parsing text (Schema's and JSON) directly from Python, though you could use the C++ parser through SWIG or ctypes. Please see the C++ documentation for more on text parsing.