Join Charles Kelly for an in-depth discussion in this video Install software, part of NumPy Data Science Essential Training.
- [Narrator] In order to use NumPy you'll need Python and several other valuable libraries. Anaconda by ContinuumIO is a great tool for combining these downloads into a single source. What we can do at this point is navigate down the page and select Python 3.5. Since I'm working on a Windows machine I'll select the Windows 64-bit graphical installer. If you are working on a different operating system select the version that's correct for your machine.
The cheat sheet is valuable and it'll show you some keyboard shortcuts and other things that will make your work with Anaconda much simpler. If you'd like this type in your work email and they'll send it to you. I'm not going to download that right now. Once your download is completed you'll have an installer file. You can begin by double clicking the installer and following the prompts that you'll see on the screen. I suggest that you take all of the defaults. I'm clicking on next.
I'm going to agree to the license. And we'll do this installation for all users. We'll give ContinuumIO permission to use our hard drive. And we'll select the default folder. We'll accept the advanced options. Once the download is complete we'll test your installation by opening a command prompt. At the command prompt type python followed by hitting the enter key. We see that Python 3.5.2 has been installed on this computer.
If you see a similar message, perhaps the version number is different, then you'll know that Anaconda has been installed correctly. Now that Anaconda has been successfully installed on your computer you're ready to use Python and learn about NumPy.
- Using Jupyter Notebook
- Creating NumPy arrays from Python structures
- Slicing arrays
- Using Boolean masking and broadcasting techniques
- Plotting in Jupyter notebooks
- Joining and splitting arrays
- Rearranging array elements
- Creating universal functions
- Finding patterns
- Building magic squares and magic cubes with NumPy and Python