You may want to use the ndarray.item method, as in a.item(). This is also equivalent to (the now deprecated) np.asscalar(a). This has the benefit of working in situations with views and superfluous axes, while the above solutions will currently break. For example,

>>> a = np.asarray(1).view()
>>> a.item()  # correct
1

>>> a[0]  # breaks
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: too many indices for array


>>> a = np.asarray([[2]])
>>> a.item()  # correct
2

>>> a[0]  # bad result
array([2])

This also has the benefit of throwing an exception if the array is not actually a scalar, while the a[0] approach will silently proceed (which may lead to bugs sneaking through undetected).

>>> a = np.asarray([1, 2])
>>> a[0]  # silently proceeds
1
>>> a.item()  # detects incorrect size
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: can only convert an array of size 1 to a Python scalar

Your object monteCarloPerf is a one dimensional array containing elements of one dimensional arrays, or a list of lists. In order to access the value of the first element of the object you have to change your access to that element to the following:

finalValue = monteCarloPerf[arrayValue][0]

Here's one pretty simple way:

>>> [map(float, x) for x in A1]
[[1.0, 4.0], [2.0, 5.0], [3.0, 6.0], [1.0, 4.0], [2.0, 5.0], [3.0, 6.0]]

I like it's because it's short (some would say terse) and since using map() makes it not hardcode or be explicit about the expected format of each x, it just says that it assumes A1 to be a list of sequences.

I have no idea how this compares performance-wise to other solutions (such as the more explicit [(float(x), float(y) for (x, y) in A1] seen below).

You can use the item() function:

Use with AIimport numpy as np

matrix = np.array([[[[7]]]])
print(matrix.item())

Output

Use with AI7

Try numpy.ndarray.astype(float). Read more here.

NOTE: This is not an in place operation so remember to set it back to a variable

import numpy as np

arr = np.array([["5","6"],
                ["3","4.5"]])

arr = arr.astype(float)  #<--- 

print(arr)
print(type(arr[0,0])) #printing type of a single element in the matrix

array([[5. , 6. ],
       [3. , 4.5]])

<class 'numpy.float64'>

Just access the first item of the list/array, using the index access and the index 0:

>>> list_ = [4] >>> list_[0] 4 >>> array_ = np.array([4]) >>> array_[0] 4 

This will be an int since that was what you inserted in the first place. If you need it to be a float for some reason, you can call float() on it then:

>>> float(list_[0]) 4.0