Efficiently using Numpy to assign function values to array
2017年8月16日 · I wish to create a function and from a given array to an array of function values. Here is a simplified code that does that using map: import numpy as np def func(x): return x**2 xRange = np.arange(0,1,0.01) arr_func = np.array(list(map(func, xRange)))
Python numpy array of functions - Stack Overflow
2020年3月10日 · I've been looking for ways to avoid long "for" loops as I'll be dealing with mesh operations and was wondering if there was a way to make an array of functions. Something like the following would be nice. x=np.array([1,2,3,4,5]) funcs=np.array([func1,func2,func3,func4],dtype=function) output=funcs(x)
Most efficient way to map function over numpy array
2016年2月5日 · Most of the functions you'd want to apply to a NumPy array elementwise will just work, though some may need changes. For example, if doesn't work elementwise. You'd want to convert those to use constructs like numpy.where :
Applying a function along a numpy array - Stack Overflow
2017年3月26日 · I've the following numpy ndarray. [ -0.54761371 17.04850603 4.86054302] I want to apply this function to all elements of the array def sigmoid(x): return 1 / (1 + math.exp(-x)) probabiliti...
"Direct" numpy functions on an array vs numpy array functions
2018年7月20日 · Backward compatibility requires the project to keep array methods like max. Ongoing development favors the function syntax np.fun(array). I suppose one reason for the latter is that it allows array_like input (the term used throughout NumPy documentation): anything that NumPy can turn into an ndarray.
Modifying a numpy array inside a function? - Stack Overflow
2013年3月4日 · np.zeros(0) gives you an empty numpy array. The reference inside your function now points to a new Python list but you haven't actually modified your empty numpy array, so that's still what you're printing out. Recommend to read this answer to clear up some concepts.
NumPy: Where in the source code are `arange` and `array` …
2018年5月8日 · numpy.array and numpy.arange are written in C. You can tell because they say "built-in" when you look at them: >>> numpy.array <built-in function array> >>> numpy.arange <built-in function arange> That means there's no def statement. Instead, we look at what module they come from:
python - Calling a lambda with a numpy array - Stack Overflow
2017年3月21日 · Its all about Overriding operators in numpy . You can learn numpy.arry here. Let us focus on your lamda function for each; 1. numpy array : arr = numpy.array([1, 2, 3]) type(arr) scale = lambda x: x * 3 scale(arr) this takes each element from array. 2. normal list: a =[1,2,3] type(a) scale = lambda x: x * 3 scale(a)
python - Array elementwise operations - Stack Overflow
2014年12月6日 · Using numpy.vectorize lets you use your element-by-element function to create your own ufunc, which works the same way as other NumPy ufuncs (like standard addition, etc.): the ufunc will accept arrays and it will apply your function to each pair of elements, it will do array shape broadcasting just like standard NumPy functions, etc.
How to apply multiple functions to numpy array? - Stack Overflow
2014年3月10日 · I want to apply two different functions for every row in array-x depending on the value for this row in array-z. if 0 apply fun0() if 1 apply fun1() I can also build an index and then apply by index, like this : ndx1 = (z == 1) ndx0 = (z == 0) and do f.e.: fun(x[:,ndx]) but this wont change the array-x.