![]() Step 7: Once you see the screen below, the installation is completed successfully. You can write the path, where you want to install Canopy, or just hit enter, to start installation in the home directory. Step 6: Now you will be asked to enter the Canopy Python installer path, where you want to install Canopy, and the default path will be in your Linux Home directory. After reading the license, you will have to type ‘ yes’, and hit the enter key, to accept the agreement, and carry on with the remaining part of the installation process. Step 5: Now, after that, the license agreement will be shown, and you will have to press the space bar when asked, to read the remaining part of the license. Once you start the installation, it will ask for permission, and you will have to hit the Enter key to proceed with the installation process. Step 4: The installation will start now, and you will have to give some permissions at the time of installation, and the installation will take no longer than 5 minutes, even with a decent hardware. If you cannot remember the filename, just type ‘ ls’, and hit the enter key, to find a list of files, which will also include the installer, in. sh>, where the filename should be the name of the file you downloaded. Without further delay, let’s start with the installation of Canopy on Linux. Canopy is also available for servers, and enterprises, which make Canopy the cross-platform tool, for development, and a number of advanced tasks. Canopy is free, and thus, there isn’t any Canopy subscription, when you are using it for personal use. There is Python Canopy for Max, as well, but I will be discussing the methods to install it on Windows and Linux. ![]() I will guide you through the Canopy installation instructions, on Windows and Linux (like Ubuntu & LinuxMint). ![]() Canopy python is completely free to download, and thus, if you are a Python programmer, get Canopy today. Apart from that, you can also get access to a number of advanced tools, which can eventually be helpful in application development, and scientific analysis. It comes with numerous tools for data analysis, data visualization, in the field of data mining and data science. That being said, you might need to use a number of dependencies from time to time, to get to greater heights, when you are using Python. Python is no doubt one of the most popular languages for programming and is also extensively used in data science. ![]()
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