Hi everyone, my name is Daniel Schorin and I'm a student assistant in this class. Today we'll be going over how to set up your local programming environment on a Windows computer. We'll go through and install all the libraries, packages, and software you'll need to run the files in this course. Well, the Jupyter console and the Coursera module, comes included with all of the libraries and packages you'll need to run the lessons in this class. Some people may prefer to do that on your own computer. The only prerequisites for this tutorial are computer running Windows with administrative access, that is connected to the internet. We'll be completing the installation using the command line. It's just a way to pass instructions to your computer using text. The command line is also known as a shell. It is a powerful tool for modifying, automating, and organizing tasks on your computer. Before we get too into the command line, let's first install Python. Navigate to this URL, https://www.python.org/downloads/windows, and download the latest version. At the time of this video recording, that's python 3.7.3. This downloads an installer which will automatically configure the paths and dependencies, along with the programming language to be interpreted by your computer. Follow the instructions and you'll be good to go. Python automatically comes with a package called PIP, which allows you to install libraries super easily. We'll touch more on PIP in just a little bit. In order to download and manage libraries, we'll need to download a package manager. Package manager is a set of software tools that automate complex installation processes, which include downloading, upgrading, configuring, and removing software. Their most robust and common package manager for Windows is Anaconda, which we'll be using in this guide. Anaconda is a free and open source package, an environment management system that makes installing software on Windows pain-free. Additionally, Anaconda is a Python data science distribution. And it comes loaded with lots of useful libraries for data mining, machine learning, and statistics programming work. If you are interested in using these libraries, be sure to check out the University of Michigan's applied data science and Python programming course on Coursera after this class. To download Anaconda, go into your web browser and navigate to www.anaconda.com/distribution, and download the package manager corresponding to the Python version you downloaded. This one's another installer program similar to the one used for the Python installation. Follow the instructions, download it to the default location, and you'll be ready to go. If you already have Anaconda installed on your computer, you can update it to the latest version by typing conda update conda in your command line interface. Now that we have our package manager installed, let's talk about virtual environments. Virtual environments allow developers to have separate space for programming projects, ensuring that the dependencies of one project don't inadvertently affect another projects. Using virtual environments can prevent a lot of compilation issues. As well as giving more control over our Python projects. It's a best practice to create a program environment for each programming project, as you can create as many as them as you like. Let's go a head and create a virtual environment for this class pie three. To do this we'll use Anaconda's built in virtual environment capabilities using the command line. On Windows computers, we can use the command line application to access the command line interface, which can run scripts, download software, and more. You can find the command line by opening the Start menu and scrolling through your applications, or by using the search bar. Once you have your command line opened, let's create a home directory for the files in this class. For this tutorial, we'll place it in a folder on your desktop although you can put it wherever you'd like. Type cd ~. That'll just take you back to your home directory, and then type cd desktop. Cd stands for change directory, that'll help us change the directories and folders as we navigate using the command line. So cd desktop helps us navigate to our desktop. And then we're going to make a folder using the command make directory or make dir, M-K-D-I-R. And then the name of our home folder for this class, and for this we use pie 3 Now that we've had that folder created, we'll go into that folder using the change directory command. Now that we're in our class directory, we'll create our virtual environment simply by typing this command. Conda create -n py3env python=3.7.2 anaconda. Where py3env is the name of our virtual environment, and the Python version corresponds with the Python version you downloaded. You can check the Python version by typing python --version. Here on this computer we're using Python 3.5. So that we've created our virtual environment we can activate it using the following command. Just simply source activate py3env, where py3env is the name of our environment. As you can see, the Windows is built-in command prompt is having a hard time working with Anaconda. To work around this, we're going to use a command line interface program called Git bash. Git bash is a shell, which works on top of the command prompt to make it easier to download libraries and easier to work with your command line as a whole. To download Git bash, just Google search Git bash. Click on the downloads page. Click on your Windows operating system, and your download will start immediately. Follow the instructions on that download, and you'll be all set to use Git bash. We'll go to our main directory, go to our desktop, go to our py3 folder. And now, since we already created the environment using the command line interface from the windows computer, we'll go ahead and see if that's working with Git bash. So, we'll try source activate py3env, and wonderful. We're in our Python 3 virtual environment. You can tell that you are in your Python 3 environment if the name of your environment is in parentheses after any command. Now that we've created and activated our virtual environment, let's install the packages we'll need for this course. We'll describe more about what these packages do and how to use them in different lessons. For now, run the following commands one at a time, in your py3 folder with your virtual environment activated. Due to the power of editing, we're going to speed through the process of copying and pasting these downloads into your Git bash shell or command prompt. And additionally, on this computer, these packages were already installed, so don't be alarmed if you see different instructions in your command prompt. We'll install pip install pillow, Pip install pytesseract, Pip install numpy, pip install matplotlib, and pip install opencv-python. With all of your libraries downloaded, we're nearly ready to go. We just need to get our files ready to run. Let's navigate to the Py3 Coursera class. Download the Jupyter notebook files with their I, P, Y, and B extensions, and their accompanying data files into the Py3 directory on your local computer. To run these files, we'll simply navigate to our folder in the command line and type Jupyter notebook. As you can see, our notebook is up here all ready to go. If you can solve the files, they'll be ready for you to run. Thank you all so much have a ton of fun in this class.
