Numpy's array objects that are PyArrayObject types, have a NPY_PRIORITY attribute that denotes the priority of the types of items for cases where the array contains items with heterogeneous data types. You can access this priority using PyArray_GetPriority API that returns the __array_priority__ attribute which according to the the documents:

Data type objects (dtype) Data type promotion in NumPy; Iterating over arrays; Standard array subclasses; Masked arrays; The array interface protocol; Datetimes and timedeltas; Universal functions (ufunc) Routines and objects by topic; Typing (numpy.typing) Packaging (numpy.distutils) NumPy C-API; Array API standard compatibility; CPU/SIMD ...

class. __array_priority__ # The value of this attribute is used to determine what type of object to return in situations where there is more than one possibility for the Python type of the returned object. Subclasses inherit a default value of 0.0 for this attribute.

Jan 31, 2013 · In NumPy, it is possible to use the __array_priority__ attribute to take control of binary operators acting on an ndarray and a user-defined type. For instance: def __radd__(self, lhs): return 0. __array_priority__ = 100. The same thing, however, doesn't appear to work for comparison operators.

Jul 30, 2015 · There isn't much mention of __array_priority__ in the documentation or numpy code. Mostly it is used in ufunc to let ndarray subclasses determine what kind of array is returned. ndarray has priority 0, matrix has priority 10.

When to Use Each Type of Array. Use Python’s array module when: You need a simple collection of numerical data of the same type; ... NumPy arrays, and Python lists. Perfect for data analysis and manipulation. Learn how to use arrays in Python with practical examples using the built-in array module, NumPy arrays, and Python lists. ...

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3 days ago · This module defines an object type which can compactly represent an array of basic values: characters, integers, floating-point numbers. Arrays are sequence types and behave very much like lists, except that the type of objects stored in them is constrained.

If you want to write error-free code, then as a Python developer, you should understand data types in Python. Each data type has its own strengths and appropriate use cases: Integers and Floats: For numerical calculations and representing quantities; Strings: For text processing and representation; Lists: For ordered, mutable collections

Mar 10, 2017 · The modules described in this chapter provide a variety of specialized data types such as dates and times, fixed-type arrays, heap queues, double-ended queues, and enumerations. Python also provides some built-in data types, in particular, dict, list, set and frozenset, and tuple.