This is by far the most clear and comprehensive tutorial on numpy basics I have seen on the Internet. Great teacher, Great example and nice questions from audience as well.
Great Video! Just a little rectification at 1:05:09 you stated that (and), (or) and (not) are binary operators. (not) is not a binary Operator, as a binary operator works on two operands, but a (not) just works on one.
"Why would the axis argument be the dimension that you collapse?" what other dimension would you collapse? (does your answer generalize to any operation and any number of dimensions? consider non-commutative operations and dimensions greater than 2)
at 02:36:35, alex slices the dates array and masks all january entries as true. When this mask is used as an argument in the winds array, how are boolean values converted to index of the winds array.
Hello. How should I go about creating a mask specifically for a diagonal, given that all elements have the same value? For example, i have this: identity = np.arange(16).reshape(4, 4) identity.fill(0) Now i want to apply some mask such that i am able to convert the main diagonal to 1
Whenever he types a variable for example "a" and hit enters, the output is automatically the value i.e. "array.a=([1 2 3])". Why is it on my microsoft visual studio, it produces no output when i hit "a"? Is it because the ide Canopy automatically produces a result? So is this a capability of ide rather than python?
Because he is writing the code in the output window for example when using IDLE you can either write a script or write the code in the "shell" which will give you an output instantly
Hi Alex I don't know if its just me, but I can't access the material via the link in the description. All I get is an XML file with an error tag at the end.
Please see the updated 2019 tutorial at ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-ZB7BZMhfPgk.html. The materials may be found at github.com/enthought/Numpy-Tutorial-SciPyConf-2019
You might like these Pandas Cheat Sheets that Alex helped author, you can download the set of 8 here: www.enthought.com/cheat-sheets-pandas-python-for-data-analysis/.