Python Integer | Generated by AI
In Python, the int type has no fixed range or size limit. Unlike languages like C or Java, where integers are constrained by specific bit sizes (e.g., 32-bit or 64-bit), Python’s int is arbitrary-precision, meaning it can grow as large as available memory allows. This applies to both Python 2 (since Python 2.2, where long and int were unified) and Python 3.
Key Points:
- No Upper or Lower Bound: Python integers can represent any whole number, positive or negative, limited only by the system’s memory. For example, you can have extremely large numbers like
10**100or very negative numbers like-10**100. - Memory Usage: The memory required for an
intgrows dynamically as the number’s magnitude increases. Python uses a variable-length encoding to store large integers. - Performance: Operations on very large integers (e.g.,
2**1000000) may be slower due to the increased memory and computational overhead, but they are still supported.
Example:
# Small integer
small = 42
print(small, type(small)) # 42 <class 'int'>
# Large integer
large = 2**1000
print(large, type(large)) # 107150860718626732... <class 'int'>
# Negative integer
negative = -10**100
print(negative, type(negative)) # -10000000000... <class 'int'>
Relation to Your Previous Query:
In your TigerOpen API issue, the order_id (359248847525015552) is an integer well within Python’s int range, so there’s no concern about overflow or truncation in Python itself. The biz param error is likely due to the API’s expectations (e.g., requiring a string order_id) rather than a Python integer limitation. The suggestion to try str(order_id) in the get_order call addresses this potential mismatch.
Additional Notes:
- System Limitations: While Python’s
inthas no theoretical limit, practical limits arise from available RAM. Extremely large integers (e.g.,2**1000000) may consume significant memory. - Type Checking: Use
isinstance(x, int)to check if a variable is an integer. - Conversion: You can convert strings or floats to integers using
int(), but be cautious with floats to avoid precision loss (e.g.,int(3.9)yields3).
If you have further questions about integers in Python or need help with the TigerOpen API issue, let me know!