Return the type that results from applying the NumPy type promotion rules to the arguments in Python

The numpy.result_type() method returns the data type that results from applying NumPy's type promotion rules to the given arguments. This is useful for determining the output type of operations between different NumPy data types without actually performing the operation.

Syntax

numpy.result_type(*arrays_and_dtypes)

Parameters

The function accepts multiple arguments representing operands whose result type is needed. These can be:

• arrays_and_dtypes − Arrays, scalars, or data type strings/objects

How Type Promotion Works

NumPy follows specific rules for type promotion:

• When combining arrays and scalars, the array's type takes precedence
• The actual value of scalars is considered during promotion
• Integer types promote to float types when necessary
• Complex types have the highest precedence

Basic Examples

import numpy as np

# Scalar with array
result1 = np.result_type(2, np.arange(4, dtype='i1'))
print("Scalar + int8 array:", result1)

# Two scalars
result2 = np.result_type(5, 8)
print("Two integers:", result2)

# Data type strings
result3 = np.result_type('i4', 'c8')
print("int32 + complex64:", result3)

Scalar + int8 array: int8
Two integers: int64
int32 + complex64: complex128

Mixed Type Examples

import numpy as np

# Float and integer
result1 = np.result_type(3.8, 8)
print("Float + integer:", result1)

# Integer and float
result2 = np.result_type(5, 20.7)
print("Integer + float:", result2)

# Negative values
result3 = np.result_type(-8, 20.7)
print("Negative int + float:", result3)

# Float and negative integer
result4 = np.result_type(10.0, -4)
print("Float + negative int:", result4)

Float + integer: float64
Integer + float: float64
Negative int + float: float64
Float + negative int: float64

Practical Use Case

import numpy as np

# Check result type before creating arrays
arr1 = np.array([1, 2, 3], dtype='int32')
arr2 = np.array([1.5, 2.5, 3.5], dtype='float32')

result_type = np.result_type(arr1.dtype, arr2.dtype)
print("Result type for operation:", result_type)

# This helps in memory planning
result_array = np.empty(3, dtype=result_type)
print("Pre-allocated array dtype:", result_array.dtype)

Result type for operation: float64
Pre-allocated array dtype: float64

Conclusion

The numpy.result_type() function is essential for predicting data types in NumPy operations. It follows consistent promotion rules where floats take precedence over integers, and complex types rank highest in the hierarchy.