Understanding `numpy.issubdtype()` for Data Type Hierarchy Checks in NumPy

Purpose

• It determines if the first argument (arg1) is a sub-type of the second argument (arg2) within the NumPy data type hierarchy.

Arguments

• arg2 (dtype_like)
  Similar to arg1, it can be a dtype object or a typecode string.
• arg1 (dtype_like)
  This can be either a NumPy data type object (dtype) or a string representing a typecode (e.g., "int32", "float64").

Return Value

• The function returns True if arg1 is a sub-type of arg2 in the data type hierarchy. Otherwise, it returns False.

Analogy

Think of the data type hierarchy like a family tree. Numeric data types (integers, floats) have a parent type like number. numpy.issubdtype() checks if one type is a descendant (sub-type) of another within this hierarchy.

Example

import numpy as np

# Check if int8 is a sub-type of int64
result = np.issubdtype(np.int8, np.int64)
print(result)  # Output: True

# Check if float32 is a sub-type of object (generic type)
result = np.issubdtype(np.float32, np.object_)
print(result)  # Output: False

• It's useful for verifying data type compatibility before performing operations on NumPy arrays.
• numpy.issubdtype() is similar to the built-in issubclass function in Python, but it works specifically for NumPy data types.

---

Checking String Subtypes

import numpy as np

# Check if string type (S1) is a sub-type of string_ (generic string)
result = np.issubdtype('S1', np.string_)
print(result)  # Output: True

# Check if unicode string (U) is a sub-type of bytes (b)
result = np.issubdtype(np.unicode_, np.byte)
print(result)  # Output: False (different data kind)

Checking User-defined Types (not recommended)

# This is not recommended practice
class MyType:
  pass

# This will raise an error because issubdtype is not designed for custom types
try:
  result = np.issubdtype(MyType, np.int32)
except TypeError as e:
  print(e)  # Output: "issubdtype not supported for this type of arguments"

import numpy as np

def add_arrays(arr1, arr2):
  # Check if data types are compatible before addition
  if not np.issubdtype(arr1.dtype, arr2.dtype):
    raise TypeError("Incompatible data types for addition")
  # Perform addition if types are compatible
  return arr1 + arr2

arr1 = np.array([1, 2, 3], dtype=np.int16)
arr2 = np.array([1.5, 2.5, 3.5], dtype=np.float32)

try:
  result = add_arrays(arr1, arr2)
  print(result)
except TypeError as e:
  print(e)  # Output: TypeError("Incompatible data types for addition")

---

isinstance for Built-in Types

If you're dealing with built-in NumPy data types like int, float, bool, etc., you can use the isinstance function for a more general approach.

import numpy as np

arr1 = np.array([1, 2, 3])

# Check if the data type of arr1 is a built-in numeric type
result = isinstance(arr1.dtype, (np.number, np.bool_))
print(result)  # Output: True (int is a numeric type)

Manual Type Checking

For simpler scenarios, you can perform manual type checks using comparisons.

import numpy as np

arr1 = np.array([1, 2, 3], dtype=np.float32)
arr2 = np.float64

# Check if the data type of arr1 is the same as arr2 (float)
result = arr1.dtype == arr2
print(result)  # Output: False (different bit widths)

# Check if arr1's dtype is a specific type (float32 here)
result = arr1.dtype == np.float32
print(result)  # Output: True

Custom Function for Complex Logic

If you have more complex requirements for data type relationships, you can define your own function.

import numpy as np

def is_compatible_dtype(arr1, arr2):
  # Example: Check if both are numeric types and have the same kind (integer, float)
  return (np.issubdtype(arr1.dtype, np.number) and 
          np.issubdtype(arr2.dtype, np.number) and
          arr1.dtype.kind == arr2.dtype.kind)

arr1 = np.array([1, 2, 3], dtype=np.int16)
arr2 = np.float32

# Check if data types of arr1 and arr2 are compatible for your logic
result = is_compatible_dtype(arr1, arr2)
print(result)  # Output: False (int and float have different kinds)

• Note that isinstance and custom functions won't handle the full NumPy data type hierarchy like numpy.issubdtype().
• Define a custom function for intricate data type logic.
• Use manual type checking for simple comparisons.
• Use isinstance for general checks with built-in numeric types.