How to check if a list of numpy arrays contains a given test array?

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I have a list of numpy arrays, say,

a = [np.random.rand(3, 3), np.random.rand(3, 3), np.random.rand(3, 3)]

and I have a test array, say

b = np.random.rand(3, 3)

I want to check whether a contains b or not. However

b in a 

throws the following error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

What is the proper way for what I want?

You can just make one array of shape (3, 3, 3) out of a:

a = np.asarray(a)

And then compare it with b (we're comparing floats here, so we should use isclose())

np.all(np.isclose(a, b), axis=(1, 2))

For example:

a = [np.random.rand(3,3),np.random.rand(3,3),np.random.rand(3,3)]
a = np.asarray(a)
b = a[1, ...]       # set b to some value we know will yield True

np.all(np.isclose(a, b), axis=(1, 2))
# array([False,  True, False])

numpy.isin, Returns a boolean array of the same shape as element that is True where an The values against which to test each value of element. If True, the input arrays are both assumed to be unique, which can speed up the Has the same shape as element. Converting the set to a list usually gives the desired behavior. Write a NumPy program to test whether any of the elements of a given array is non-zero. Resetting will undo all of your current changes. pro tip You can save a copy for yourself with the Copy or Remix button. Recaptcha requires verification. pro tip You can save a copy for yourself with the Copy or Remix button. Publish Your Trinket!

Ok so in doesn't work because it's effectively doing

def in_(obj, iterable):
    for elem in iterable:
        if obj == elem:
            return True
    return False

Now, the problem is that for two ndarrays a and b, a == b is an array (try it), not a boolean, so if a == b fails. The solution is do define a new function

def array_in(arr, list_of_arr):
     for elem in list_of_arr:
        if (arr == elem).all():
            return True
     return False

a = [np.arange(5)] * 3
b = np.ones(5)

array_in(b, a) # --> False

numpy.any, Test whether any array element along a given axis evaluates to True. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single o (​array(True), array(True)) >>> # Check now that z is a reference to o >>> z is o  import numpy as np def is_numeric_array(array): """Checks if the dtype of the array is numeric. Booleans, unsigned integer, signed integer, floats and complex are considered numeric. Parameters ----- array : `numpy.ndarray`-like The array to check.

How to check if a value exists in NumPy Array, Numpy arrays are data structures for efficiently storing and using data. Checking if a value exists in an array tests if the array or any elements of the array contain  Check if NumPy array is empty. We can use the size method which returns the total number of elements in the array. In the following example, we have an if statement that checks if there are elements in the array by using ndarray.size where ndarray is any given NumPy array:

As pointed out in this answer, the documentation states that:

For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y).

a[0]==b is an array, though, containing an element-wise comparison of a[0] and b. The overall truth value of this array is obviously ambiguous. Are they the same if all elements match, or if most match of if at least one matches? Therefore, numpy forces you to be explicit in what you mean. What you want to know, is to test whether all elements are the same. You can do that by using numpy's all method:

any((b is e) or (b == e).all() for e in a)

or put in a function:

def numpy_in(arrayToTest, listOfArrays):
    return any((arrayToTest is e) or (arrayToTest == e).all()
               for e in listOfArrays)

(Tutorial) Python NUMPY Array TUTORIAL, Don't forget that, in order to work with the np.array() function, you If you would like to know more about how to make lists, go here. When you multiply a matrix with an identity matrix, the given matrix is left unchanged. that NumPy has to offer to get to know more instantly! To make a numpy array, you can just use the np.array () function. All you need to do is pass a list to it, and optionally, you can also specify the data type of the data. If you want to know more about the possible data types that you can pick, go here or consider taking a brief look at DataCamp’s NumPy cheat sheet.

Use array_equal from numpy

    import numpy as np
    a = [np.random.rand(3,3),np.random.rand(3,3),np.random.rand(3,3)]
    b = np.random.rand(3,3)

    for i in a:
        if np.array_equal(b,i):
            print("yes")

Python, Given two lists a, b. Using traversal in two lists, we can check if there exists one common element at least After complete traversal and checking, if no elements are same, then we a positive integer, if it does not contains any positive integer, then it returns 0. first_page Python | Reverse an array upto a given position. Some key differences between lists include, numpy arrays are of fixed sizes, they are homogenous I,e you can only contain, floats or strings, you can easily convert a list to a numpy array, For example, if you would like to perform vector operations you can cast a list to a numpy array.

numpy.isin, Returns a boolean array of the same shape as element that is True where an element The values against which to test each value of element. Has the same shape as element. in1d: Flattened version of this function. numpy.lib.​arraysetops: Module with a Converting the set to a list usually gives the desired behavior. Given an array of n integers. The task is to check whether an arithmetic progression can be formed using all the given elements. If possible print “Yes”, else print “No”. Examples: Input : arr[] = {0, 12, 4, 8} Output : Yes Rearrange given array as {0, 4, 8, 12} which forms an arithmetic progression.

1.4.1. The NumPy array object, high-level number objects: integers, floating point; containers: lists (costless insertion extension package to Python for multi-dimensional arrays; closer to hardware You can use np.may_share_memory() to check if two arrays share the same array of integers, the new array has the same shape as the array of integers:. Write a NumPy program to convert a list and tuple into arrays. There was a problem connecting to the server. Please check your connection and try running the trinket again. Run your code first! It looks like you haven't tried running your new code. Try clicking Run and if you like the result, try sharing again.

4. NumPy Basics: Arrays and Vectorized Computation, This feature has made Python a language of choice for wrapping legacy Unless explicitly specified (more on this later), np.array tries to infer a good data type for the array that See Table 4-1 for a short list of standard array creation functions. large data sets, it is good to know that you have control over the storage type. Similar to arithmetic operations when we apply any comparison operator to Numpy Array, then it will be applied to each element in the array and a new bool Numpy Array will be created with values True or False. Suppose we have a Numpy Array i.e. #Create an Numpy Array containing elements from 5 to 30 but at equal interval of 2 arr = np.arange (5

Comments
  • Did you try list comprehension?
  • what do you mean by list comprehension? In my understanding list comprehension means something like
  • [ a for a in some_iterable]
  • what is the point of list comprehension in this task?
  • Why not make a a 3x3x3 array?
  • Actually, (a==b).all() is not slower than np.array_equal(a, b). The main difference is, that np.array_equal tests the shape of the array first.