Only Integer Arrays With One Element Can Be Converted to an Index

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Error encountered is: "TypeError: just integer scalar arrays can be converted to a scalar index".
This mistake comes when we handle Numpy array in the wrong way using Numpy utility functions. The set up is to refer the documentation of the Numpy function and utilise the role correctly.

Error:

              TypeError: only integer scalar arrays can be converted to a scalar index                          

Table of Content:

1. Solution
2. Case 1: Error with np.concatenate
3. Case 2: Fault with np.ndindex
4. See source of error in Numpy source lawmaking

Solution

The error you have faced is this:

              TypeError: just integer scalar arrays can exist converted to a scalar index                          

This means that the index you are using to refer to an assortment element is wrong. The index should always be an integer.

Despite this, due to flexibility of Python, yous tin utilize an array with one Interger element every bit an index. Internally, the array will exist converted to a single Integer and volition exist used as an index.

This fault is defined in Numpy library. And then, yous are using one of the functions in Numpy in a incorrect way. You need to refer the Numpy documentation to ostend the API of the function which is giving an error.

You should be able to identify the issue. The most common event is that a ready of assortment needs to be passed equally a list to a Numpy function. Nosotros volition do not pass it every bit a list then, this error will exist encountered.

              # Not passing as list array1, array2, ..., arrayN  # Passing as listing (array1, array2, ..., arrayN)                          

Case 1: Fault with np.concatenate

Consider the following lawmaking snippet:

              import numpy as np a = np.zeros((1,2)) np.concatenate(a,a)                          

Using the above code snippet, nosotros tin can reproduce the error. The mistake is as follows:

              Traceback (most recent call final):   File "<stdin>", line 1, in <module>   File "<__array_function__ internals>", line 6, in concatenate TypeError: simply integer scalar arrays can exist converted to a scalar index                          

The solution is to use the array as a list:

              np.concatenate((a,a))                          

Now, consider this Python code:

              import numpy every bit np  a = [one,two] np.concatenate(a,a)                          

This lawmaking looks unproblematic and has an consequence as well. The issue is different and is that a is a list. The mistake volition exist as follows:

              Traceback (most recent telephone call final):   File "<stdin>", line ane, in <module>   File "<__array_function__ internals>", line half dozen, in concatenate TypeError: 'list' object cannot be interpreted as an integer                          

np.concatenate accepts Numpy arrays.

Case 2: Mistake with ndindex

We tin get the same error with ndindex office of Numpy library.

Consider the following Python lawmaking:

              import numpy equally np opengenus = np.ndindex(np.random.rand(128,128))                          

Error:

              Traceback (almost recent call terminal):   File "<stdin>", line 1, in <module>   File "/usr/local/lib64/python3.6/site-packages/numpy/lib/index_tricks.py", line 640, in __init__     strides=_nx.zeros_like(shape))   File "/usr/local/lib64/python3.6/site-packages/numpy/lib/stride_tricks.py", line 103, in as_strided     array = np.asarray(DummyArray(interface, base of operations=x))   File "/usr/local/lib64/python3.6/site-packages/numpy/core/_asarray.py", line 85, in asarray     render array(a, dtype, re-create=Imitation, gild=order) TypeError: only integer scalar arrays can be converted to a scalar index                          

Note the mistake is same and then the set is same:

Gear up:

              np.ndindex(np.random.rand(128,128).shape)                          

Output:

              <numpy.ndindex object at 0x7f1fe5f3cfd0>                          

Run across source of error in Numpy source code

This error comes from this function in Numpy source lawmaking (File: numpy/core/src/multiarray/number.c):

File:

              numpy/core/src/multiarray/number.c                          

Code snippet from the Numpy code file:

              static PyObject * array_index(PyArrayObject *v) {     if (!PyArray_ISINTEGER(v) || PyArray_NDIM(five) != 0) {         PyErr_SetString(PyExc_TypeError,             "simply integer scalar arrays tin be converted to a scalar index");         render NULL;     }     render PyArray_GETITEM(five, PyArray_DATA(v)); }                          

array_index is used whenever nosotros refer to array index in Numpy. The function array_index is used as follows in the aforementioned file.

              NPY_NO_EXPORT PyNumberMethods array_as_number = {     .nb_add = array_add,     .nb_subtract = array_subtract,     .nb_multiply = array_multiply,     .nb_remainder = array_remainder,     .nb_divmod = array_divmod,     .nb_power = (ternaryfunc)array_power,     .nb_negative = (unaryfunc)array_negative,     .nb_positive = (unaryfunc)array_positive,     .nb_absolute = (unaryfunc)array_absolute,     .nb_bool = (inquiry)_array_nonzero,     .nb_invert = (unaryfunc)array_invert,     .nb_lshift = array_left_shift,     .nb_rshift = array_right_shift,     .nb_and = array_bitwise_and,     .nb_xor = array_bitwise_xor,     .nb_or = array_bitwise_or,      .nb_int = (unaryfunc)array_int,     .nb_float = (unaryfunc)array_float,     .nb_index = (unaryfunc)array_index,      .nb_inplace_add = (binaryfunc)array_inplace_add,     .nb_inplace_subtract = (binaryfunc)array_inplace_subtract,                          

Decision:

This error comes when we handle Numpy array in the incorrect mode using Numpy utility functions. The ready is to refer the documentation of the Numpy role and use the office correctly.

With this article at OpenGenus, you lot must have the complete idea of solving this mistake. Happy debugging.

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Source: https://iq.opengenus.org/integer-scalar-array-index-error/

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