# Question 1: 
# The international standard for floating-point representation is 
# called the "IEEE 754 Standard for Floating-Point Arithmetic."

# Question 2: 
# 32-bit and 64-bit formats are the most widely used ones in practice.

# Question 3: 
# My computer supports all these formats
import numpy as np

supported_formats = np.finfo(np.float32).bits, np.finfo(np.float64).bits
print(f"Your computer supports the following floating-point formats: {supported_formats}")


# Question 4:
# Using the Pandas multiplication operator (df['Number1'] * df['Number2'])
# , the result is automatically converted to a floating-point data type. 
# On the other hand, when I multiply the numbers using the normal Python
# multiplication operator (num1 * num2), the result remains an integer 
# data type.

# Question 5:
# The size of a floating-point number in memory is primarily determined 
# by the data type and not the actual value it represents. So the size 
# of a Python floating-point number does not change as it gets larger.
import sys
print(sys.getsizeof(70))