NumPy is a library for the Python programming language which is used for working with multi-dimensional arrays and matrices. This is very useful in large scientific computing. Because NumPy ndarrays is way faster compared to a regular python list. Arrays are very frequently used in data science too, where speed and resources are very important. That’s why NumPy is a very handy tool in data-science.
Individual NumPy Array elements can be accessed by index, using syntax identical to Python lists: arrayindex for a single element, or arraystart:end for a slice, where start and end are the starting and ending indexes for the slice. Nested Arrays or elements can be accessed by adding additional comma-separated parameters.
But remembering all the NumPy commands might be overwhelming for both beginners and professionals. So Datalators makes the complex simple.
It’s also a good idea to check the official NumPy documentation from time to time. Even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs. Also, the documentation goes into a lot more detail than we can fit in a single sheet anyway!
Numpy - Functions (cont) np.on es((5, 2)) Returns a 5x2 matrix filled with 1 Numpy has all functions and constants in the library Math. Numpy arrays can be used to do all sorts of linear algebra calcul ations since they are treated as mathem atical tensors (vectors and matrices) rather than Python lists. This cheat sheet was created in python’s interactive mode. In the interactive mode you type commands into the interpreter and directly. Import module (numpy) Create a numpy array of floats called numpyarray Use numpy to calculate the mean of the values in numpyarray. Title: Pythoncheatsheet. Use this cheat sheet as a guide in the beginning and come back to it when needed, and you’ll be well on your way to mastering the NumPy library. Join my email list with 2k+ people to get The Complete Python for Data Science Cheat Sheet Booklet for Free. MATLAB/Octave Python Description a(2:end) a1: miss the first element a(1:9) miss the tenth element a(end) a-1 last element a(end-1:end) a-2: last two elements Maximum and minimum MATLAB/Octave Python Description max(a,b) maximum(a,b) pairwise max max(a b) concatenate((a,b)).max max of all values in two vectors v,i = max(a) v,i = a.max(0),a.argmax(0).
Creating Arrays:
Import Export:
Inspecting Array:
Data Types:
Array Mathematics:
Statistics on NumPy:
Indexing and Slicing
Array Manipulation:
I hope this cheat sheet will be useful to you. No matter you are new to python who is learning python for data science or a data professional. Happy Programming.
Numpy Array Cheat Sheet Examples
You can also download the printable PDF file from here.
Numpy Array Cheat Sheet Pdf
The source code for NumPy is located at this GitHub repository.
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