How to Get Started with Data Science and Machine Learning on Raspberry Pi using Anaconda

Updated October 13, 2023

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This article will guide you through the process of installing Anaconda on your Raspberry Pi, a free and open-source distribution of Python and R programming languages for scientific computing. We will cover everything from downloading and installing Anaconda to creating an environment and running a sample program.

Are you a data scientist or machine learning enthusiast looking to work on your Raspberry Pi? Do you want to get started with Python and R programming languages for scientific computing? Installing Anaconda on your Raspberry Pi is a great way to start. Anaconda is a free and open-source distribution of Python and R programming languages that provides easy access to libraries, packages, and pre-built functions for data analysis, machine learning, and deep learning. In this article, we will go through the process of installing Anaconda on your Raspberry Pi step by step.

Downloading Anaconda: First, you need to download the Anaconda installer for Linux ARMv7 platform from the official website (https://www.anaconda.com/download/#linux). You can choose either Python 3.7 or Python 2.7 version based on your preference. Since Raspberry Pi runs on a 32-bit ARM processor, you need to download the installer for Linux ARMv7 platform.
```
wget https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-ARMv7l.sh
```
Installing Anaconda: After downloading the installer, you need to make it executable and run it. Use the following commands in your terminal:
```
chmod +x Anaconda3-2019.10-Linux-ARMv7l.sh
./Anaconda3-2019.10-Linux-ARMv7l.sh
```
The installer will guide you through the installation process. Follow the instructions and agree to the license terms. When prompted, choose to install Anaconda for all users or just for yourself.
Updating your Path: After installing Anaconda, you need to update your PATH environment variable so that the Anaconda executables are accessible from any location in your system. Open your .bashrc file using a text editor (e.g., nano):
```
nano ~/.bashrc
```
Add the following lines at the end of the file:
```
# Anaconda
export PATH="/home/<your-username>/anaconda3/bin:$PATH"
```
Replace <your-username> with your Raspberry Pi username. Save and exit the file (Ctrl+X, Y, Enter). Then, run the following command to reload your .bashrc file:
```
source ~/.bashrc
```
Creating a Conda Environment: A conda environment is a self-contained Python environment that you can create with all the necessary libraries and packages for your specific project. To create an environment, run the following command in your terminal:
```
conda create -n myenv python=3.7
```
Replace myenv with a name of your choice for your environment. This will create a new environment with Python 3.7. You can also specify other packages to include in the environment by adding them after the python=3.7 argument, separated by spaces. For example:
```
conda create -n myenv python=3.7 numpy pandas matplotlib
```
This will create an environment with Python 3.7, NumPy, and Matplotlib installed.
Activating the Environment: To use the newly created environment, you need to activate it using the following command:
```
conda activate myenv
```
Replace myenv with the name of your environment. You will notice that your terminal prompt changes to show the active environment’s name.
Running a Sample Program: To test if everything is working correctly, let’s run a simple Python script using the newly created environment. Create a new file called test.py and add the following code:
```
import numpy as np
print(np.__version__)
```
Save and exit the file (Ctrl+X, Y, Enter). Then, run the script using Python:
```
python test.py
```
If everything is set up correctly, you should see the version of NumPy printed in your terminal.

Now you have successfully installed Anaconda on your Raspberry Pi and created a new environment to work with Python and its scientific computing libraries. You can install more packages and use them in your environment as needed. Remember to always activate your environment before starting your data science projects!