Development of Android applications in Python: from scratch to publication in 2026

Python has long gone beyond server-side scripts and data analysis - today full-fledged mobile applications for Androidare written in it. But how does it work if officially Android SDK only supports Java/Kotlin i C++? The answer lies in specialized frameworks that translate Python code into native APK files or integrate with Android NDK. This article will cover all the nuances: from choosing a tool to optimizing performance and bypassing key limitations.

We will look at three main approaches: Kivy (for cross-platform UI applications), BeeWare (with a native Python interface) and Chaquopy (Python integration into Java/Kotlin projects). We will pay special attention Critical performance issues when working with graphics and multi-threading, which often become the reason for abandoning Python in favor of native development. You will learn how to get around these limitations and when Python is really justified, and when it is better to immediately choose Kotlin.

Why Python for Android: pros and pitfalls

The main advantage of Python is speed of development. Python code is 3-5 times shorter than its equivalent by Java, and the abundance of libraries (from numpy to requests) allows you to quickly implement complex logic. This is ideal for:

• 📊 Prototypes and MVP (Minimum Viable Product) - when you need to show an investor a working product in a week.
• 🔬 Scientific/engineering applicationswhere integration with matplotlib, pandas or TensorFlow Lite.
• 🤖 Automation on the device (for example, bots for Telegram or parsing data from sensors).
• 🎓 Educational projects —Python is easier to learn than Kotlin, and the result can be immediately seen on a smartphone.

However, there is a downside. Python applications on Android:

• 🐢 Slower than native ones 2-10 times (depending on the framework and task). Critical for games or applications with intensive graphics.
• 📦 Weigh more - even a simple application on Kivy will take 15-20 MB versus 2-3 MB on Kotlin.
• 🔋 Consume more battery due to the additional layer of code interpretation.
• 🛠️ Require manual configuration to work with native APIs (camera, GPS, Bluetooth).

⚠️ Attention: If your application requires high performance (for example, 3D games or real-time video processing), Python is not suitable. Consider Unity (C#) or native development in Kotlin/C++.

Top 3 frameworks for Python on Android: comparison 2026

The choice of framework determines not only the syntax, but also the capabilities of your application. We analyzed current solutions for 2026:

Kivy is the most popular choice due to its simplicity and cross-platform. It uses OpenGL for rendering, which allows you to create animations and games, but the interface will not look like native AndroidSuitable for projects where the appearance is not the same. is critical.

BeeWare —an ambitious project that translates Python code into native widgets. Android. Applications look and work like native ones, but the ecosystem is still young (as of 2026, some libraries may be missing).

Chaquopy — a plugin for Android Studioallows you to embed Python code in Java/Kotlin projects. This is the only way to use Python for individual modules (for example, for machine learning) in the main native application.

Step-by-step guide: the first application on Kivy

Let's start with Kivy the most accessible option. You will need:

• 💻 Python 3.10+ (recommended 3.11 for better compatibility).
• 📱 Android device with USB debugging enabled or an emulator (Android Studio).
• 🔧 buildozer - a tool for building APK from Python code.

Install dependencies:

pip install kivy buildozer

Create a file main.py with a simple interface:

from kivy.app import App
from kivy.uix.button import Button

class MyApp(App):
def build(self):
return Button(text="Hello Android!", size_hint=(0.5, 0.5))

MyApp().run()

Now generate an APK To do this:

1. Create a file buildozer.spec command buildozer init.
2. Edit it by specifying requirements = python3,kivy and title = My application.
3. Run the build: buildozer -v android debug (may take 10-30 minutes).

The finished APK will appear in the folder bin. Install it on the device:

adb install bin/MyApp-debug.apk

Install Python 3.10+|Install Java JDK 17+|Install Android SDK (via Android Studio)|Configure environment variables for adb|Connect the device in mode debugging-->

⚠️ Attention: During the first build, buildozer will download ~1.5 GB of dependencies (including Android NDKIf the build aborts with an error SSL: CERTIFICATE_VERIFY_FAILED, add to buildozer.spec line android.ndk = 25b and try again.

Working with native APIs: camera, GPS, notifications

The main problem of Python on Android is limited access to hardware. For example, to use camera, you will have to write a wrapper in Java or use libraries like Plyer:

from plyer import camera

def take_picture(instance, filename):
camera.take_picture(filename=filename, on_complete=lambda *args: print("Photo saved!"))

camera.request_permissions(take_picture)

For GPS suitable Plyer or PyJNIus (bridge to Java code):

from jnius import autoclass

Gaining access to Android LocationManager
LocationManager = autoclass('android.location.LocationManager')
context = autoclass('org.kivy.android.PythonActivity').mActivity
location_manager = context.getSystemService(LocationManager.GPS_PROVIDER)

With notifications it's easier - use plyer.notification:

from plyer import notification

notification.notify(
title="Greetings from Python!",
message="This is a notification from your application",
app_name="MyApp"
)

For complex tasks (for example, working with Bluetooth Low Energyyou will have to write Java code and connect it with Python via PyJNIus or Chaquopy. An alternative is to use ready-made solutions like bleak (but they may not support all functions Android 14+).

Without this, the application will crash when trying to access protected data.-->

Performance optimization: how to speed up Python on Android

Python code on Android is slower than native due to the interpreter. But there are ways to speed it up:

1. Use Cython - compile critical parts of the code in C:

   # file module.pyx
   def heavy_calculation(int n):
   cdef int i, result = 0
   for i in range(n):
   result += i * i
   return result
     

   Then compile it and import it into the main one code.

2. Move heavy calculations to C++ via PyJNIus or Chaquopy.
3. Disable unnecessary modules to buildozer.spec:

   # Remove unnecessary ones dependencies
   requirements = python3,kivy,sqlalchemy # instead of python3,kivy,pandas,numpy,...
     

4. Use multiprocessing instead threading - because GIL (Global Interpreter Lock) threads in Python do not provide a performance boost on multitasking operations.

// In Kotlin (MainActivity.kt)
val python = Python.getInstance()
val module = python.getModule("script")
val result = module.callAttr("calculate", 10, 20).toJava(Int::class.java)

# In Python (script.py)
def calculate(a, b):
return a + b

import tensorflow as tf

Loading the model from assets
model_path = "./assets/model.tflite"
interpreter = tf.lite.Interpreter(model_path=model_path)