The fromlist() method and extend() method are both used to extend the array by list in the array module.
There is no difference in how it is returning and handling runtime errors.
Almost both behave the same, but why there are two different methods with the same functionality?
I checked in Python docs, Source code but didn't find anything unique.
let me know if I'm missing out on anything here.
Thanks in Advance!
#Sample code
from array import array
arr = array('i',[1,2,3,4,4])
templist = [5,6]
arr.extend(templist)
#arr.fromlist(templist)
So, I know that you can do this by doing
>>> arr[mask] = value
However, if I want to make the code shorter (and not recompute the mask and index each time), I'd like to do something like this:
>>> sub = arr[mask]
>>> sub[...] = value # This works in other cases, but not this one.
My understanding is that doing Ellipses indexing should allow you to specify that you're not reassigning a given variable, but are rather broadcasting to the actual array.
So, here's the question: why doesn't it work?
My thinking is that it's related to the fact that:
>>> arr[mask] is arr[mask]
False
But surely since the mask indexed versions are just views (not copies of the underlying structure), this shouldn't break assignment.
But surely since the mask indexed versions are just views (not copies of the underlying structure), this shouldn't break assignment.
The reason why this doesn't work is that indexing with masks will create a copy, not a view:
Advanced indexing always returns a copy of the data (contrast with basic slicing that returns a view).
arr[mask] is a copy. arr[mask]=... looks the same, but actually is a different assignment operation. Elsewhere I've explained this in terms of calls to __getitem__ and __setitem__.
For doing repeated operations in numpy/scipy, there's a lot of overhead because most operation return a new object.
For example
for i in range(100):
x = A*x
I would like to avoid this by passing a reference to the operation, like you would in C
for i in range(100):
np.dot(A,x,x_new) #x_new would now store the result of the multiplication
x,x_new = x_new,x
Is there any way to do this? I would like this not for just mutiplication but all operations that return a matrix or a vector.
See Learning to avoid unnecessary array copies in IPython Books. From there, note e.g. these guidelines:
a *= b
will not produce a copy, whereas:
a = a * b
will produce a copy. Also, flatten() will copy, while ravel() only copies if necessary and returns a view otherwise (and thus should in general be preferred). reshape() also does not produce a copy, but returns a view.
Furthermore, as #hpaulj and #ali_m noted in their comments, many numpy functions support an out parameter, so have a look at the docs. From numpy.dot() docs:
out : ndarray, optional
Output argument.
This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible.
I'm working on a project in Python requiring a lot of numerical array calculations. Unfortunately (or fortunately, depending on your POV), I'm very new to Python, but have been doing MATLAB and Octave programming (APL before that) for years. I'm very used to having every variable automatically typed to a matrix float, and still getting used to checking input types.
In many of my functions, I require the input S to be a numpy.ndarray of size (n,p), so I have to both test that type(S) is numpy.ndarray and get the values (n,p) = numpy.shape(S). One potential problem is that the input could be a list/tuple/int/etc..., another problem is that the input could be an array of shape (): S.ndim = 0. It occurred to me that I could simultaneously test the variable type, fix the S.ndim = 0problem, then get my dimensions like this:
# first simultaneously test for ndarray and get proper dimensions
try:
if (S.ndim == 0):
S = S.copy(); S.shape = (1,1);
# define dimensions p, and p2
(p,p2) = numpy.shape(S);
except AttributeError: # got here because input is not something array-like
raise AttributeError("blah blah blah");
Though it works, I'm wondering if this is a valid thing to do? The docstring for ndim says
If it is not already an ndarray, a conversion is
attempted.
and we surely know that numpy can easily convert an int/tuple/list to an array, so I'm confused why an AttributeError is being raised for these types inputs, when numpy should be doing this
numpy.array(S).ndim;
which should work.
When doing input validation for NumPy code, I always use np.asarray:
>>> np.asarray(np.array([1,2,3]))
array([1, 2, 3])
>>> np.asarray([1,2,3])
array([1, 2, 3])
>>> np.asarray((1,2,3))
array([1, 2, 3])
>>> np.asarray(1)
array(1)
>>> np.asarray(1).shape
()
This function has the nice feature that it only copies data when necessary; if the input is already an ndarray, the data is left in-place (only the type may be changed, because it also gets rid of that pesky np.matrix).
The docstring for ndim says
That's the docstring for the function np.ndim, not the ndim attribute, which non-NumPy objects don't have. You could use that function, but the effect would be that the data might be copied twice, so instead do:
S = np.asarray(S)
(p, p2) = S.shape
This will raise a ValueError if S.ndim != 2.
[Final note: you don't need ; in Python if you just follow the indentation rules. In fact, Python programmers eschew the semicolon.]
Given the comments to #larsmans answer, you could try:
if not isinstance(S, np.ndarray):
raise TypeError("Input not a ndarray")
if S.ndim == 0:
S = np.reshape(S, (1,1))
(p, p2) = S.shape
First, you check explicitly whether S is a (subclass of) ndarray. Then, you use the np.reshape to copy your data (and reshaping it, of course) if needed. At last, you get the dimension.
Note that in most cases, the np functions will first try to access the corresponding method of a ndarray, then attempt to convert the input to a ndarray (sometimes keeping it a subclass, as in np.asanyarray, sometimes not (as in np.asarray(...)). In other terms, it's always more efficient to use the method rather than the function: that's why we're using S.shape and not np.shape(S).
Another point: the np.asarray, np.asanyarray, np.atleast_1D... are all particular cases of the more generic function np.array. For example, asarray sets the optional copy argument of array to False, asanyarray does the same and sets subok=True, atleast_1D sets ndmin=1, atleast_2d sets ndmin=2... In other terms, it's always easier to use np.array with the appropriate arguments. But as mentioned in some comments, it's a matter of style. Shortcuts can often improve readability, which is always an objective to keep in mind.
In any case, when you use np.array(..., copy=True), you're explicitly asking for a copy of your initial data, a bit like doing a list([....]). Even if nothing else changed, your data will be copied. That has the advantages of its drawbacks (as we say in French), you could for example change the order from row-first C to column-first F. But anyway, you get the copy you wanted.
With np.array(input, copy=False), a new array is always created. It will either point to the same block of memory as input if this latter was already a ndarray (that is, no waste of memory), or will create a new one "from scratch" if input wasn't. The interesting case is of course if input was a ndarray.
Using this new array in a function may or may not change the original input, depending on the function. You have to check the documentation of the function you want to use to see whether it returns a copy or not. The NumPy developers try hard to limit unnecessary copies (following the Python example), but sometimes it can't be avoided. The documentation should tell explicitly what happens, if it doesn't or it's unclear, please mention it.
np.array(...) may raise some exceptions if something goes awry. For example, trying to use a dtype=float with an input like ["STRING", 1] will raise a ValueError. However, I must admit I can't remember which exceptions in all the cases, please edit this post accordingly.
Welcome to stack-overflow. This comes down to almost a style choice, but the most common way I've seen to deal with this kind of situation is to convert the input to an array. Numpy provides some useful tools for this. numpy.asarray has already been mentioned, but here are a few more. numpy.at_least1d is similar to asarray, but reshapes () arrays to be (1,) numpy.at_least2d is the same as above but reshapes 0d and 1d arrays to be 2d, ie (3,) to (1, 3). The reason we convert "array_like" inputs to arrays is partly just because we're lazy, for example sometimes it can be easier to write foo([1, 2, 3]) than foo(numpy.array([1, 2, 3])), but this is also the design choice made within numpy itself. Notice that the following works:
>>> numpy.mean([1., 2., 3.])
>>> 2.0
In the docs for numpy.mean we can see that x should be "array_like".
Parameters
----------
a : array_like
Array containing numbers whose mean is desired. If `a` is not an
array, a conversion is attempted.
That being said, there are situations when you want to only accept arrays as arguments and not all "array_like" types.
Is there a simple way to create an immutable NumPy array?
If one has to derive a class from ndarray to do this, what's the minimum set of methods that one has to override to achieve immutability?
You can make a numpy array unwriteable:
a = np.arange(10)
a.flags.writeable = False
a[0] = 1
# Gives: ValueError: assignment destination is read-only
Also see the discussion in this thread:
http://mail.scipy.org/pipermail/numpy-discussion/2008-December/039274.html
and the documentation:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flags.html
I have a subclass of Array at this gist: https://gist.github.com/sfaleron/9791418d7023a9985bb803170c5d93d8
It makes a copy of its argument and marks that as read-only, so you should only be able to shoot yourself in the foot if you are very deliberate about it. My immediate need was for it to be hashable, so I could use them in sets, so that works too. It isn't a lot of code, but about 70% of the lines are for testing, so I won't post it directly.
Note that it's not a drop-in replacement; it won't accept any keyword args like a normal Array constructor. Instances will behave like Arrays, though.
Setting the flag directly didn't work for me, but using ndarray.setflags did work:
a = np.arange(10)
a.setflags(write=False)
a[0] = 1 # ValueError