Get the Machine limits information for integer types in Python

To get machine limits information for integer types in Python, use the numpy.iinfo() method. This function returns an object containing the minimum and maximum values for a specified integer data type, helping you understand the range of values that can be stored.

Syntax

numpy.iinfo(int_type)

Parameters:

• int_type − The integer data type to get information about (e.g., np.int16, np.int32, np.int64)

Basic Example

Let's check the limits for different integer types ?

import numpy as np

# Get machine limits for int16
info_16 = np.iinfo(np.int16)
print("int16 minimum:", info_16.min)
print("int16 maximum:", info_16.max)

# Get machine limits for int32  
info_32 = np.iinfo(np.int32)
print("\nint32 minimum:", info_32.min)
print("int32 maximum:", info_32.max)

# Get machine limits for int64
info_64 = np.iinfo(np.int64)
print("\nint64 minimum:", info_64.min)
print("int64 maximum:", info_64.max)

int16 minimum: -32768
int16 maximum: 32767

int32 minimum: -2147483648
int32 maximum: 2147483647

int64 minimum: -9223372036854775808
int64 maximum: 9223372036854775807

Additional Information

The iinfo object provides more than just min and max values ?

import numpy as np

info = np.iinfo(np.int32)
print("Data type:", info.dtype)
print("Kind:", info.kind)
print("Bits:", info.bits)
print("Minimum:", info.min)
print("Maximum:", info.max)

Data type: int32
Kind: i
Bits: 32
Minimum: -2147483648
Maximum: 2147483647

Comparison of Integer Types

Conclusion

Use numpy.iinfo() to get machine limits for integer types. This helps you choose the appropriate data type for your arrays and avoid overflow errors when working with large numbers in NumPy.