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For CS50P’s “Einstein” exercise, convert the entered mass to an integer and multiply it by 300,000,000 twice. That matches the assignment’s integer-in, integer-out requirements and avoids an unnecessary float conversion. The multiplication is exact for those integer values in Python; the speed-of-light value used by the exercise is still an approximation, not an exact measurement of a real object’s energy.

What the CS50P Einstein exercise asks you to do

The official CS50P Einstein assignment asks you to create a file named einstein.py. Your program should prompt for mass in kilograms as an integer, then output the equivalent energy in joules as an integer, using E = mc². The assignment gives the speed of light as approximately 300,000,000 meters per second.

Python’s input() returns text, so convert the response with int(). Then multiply the mass by the speed value twice. The ** 2 operator is another way to square the value.

mass = int(input("Mass: "))
speed_of_light = 300_000_000
energy = mass * speed_of_light * speed_of_light
print(energy)

The underscores in 300_000_000 are permitted in Python numeric literals and make the groups easier to read; they do not change the value. You could instead write mass * speed_of_light ** 2.

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Why integers fit this assignment

The exercise explicitly asks for integer mass and integer energy. Python integers represent whole numbers exactly, and multiplying integer operands produces an integer result without floating-point rounding. That is a good match for this specified calculation; it is not a rule that every physics calculation should use integers.

CS50’s published examples show the expected scale of the result:

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Mass entered Energy output
1 kg 90,000,000,000,000,000 J
14 kg 1,260,000,000,000,000,000 J
50 kg 4,500,000,000,000,000,000 J

These are examples printed in the assignment, not independent measurements. The problem page also points learners to check50 for submission checking.

What “precision” means here—and what it does not

There are two separate issues. In the program, integer multiplication is exact relative to the integer operands and the chosen value 300,000,000. In the physical formula, that value is explicitly approximate. So the program can calculate its defined integer expression exactly while the result remains an approximation of the energy of a real object.

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This distinction matters: exact arithmetic does not make an approximate input constant exact. The exercise is a lesson in matching representation to the requested calculation, not a claim that its output is a perfectly precise physical measurement.

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How integer, float, and decimal representations differ

Type What it represents well Relevant trade-off
int Whole numbers and exact integer arithmetic Does not represent fractional values.
float Approximate real-number calculations, including fractions Most decimal fractions cannot be represented exactly as binary fractions. Python’s tutorial says almost all platforms map floats to IEEE 754 binary64 values with 53 bits of precision.
Decimal Decimal arithmetic where decimal behavior or strict equality rules matter, such as some accounting work It adds a different arithmetic model that this integer-only exercise does not require. The Python 3.11 documentation specifies a default precision of 28 places.

Python’s floating-point tutorial explains representation error: most decimal fractions cannot be represented exactly in binary. This does not make floats inherently bad. They are useful when a program needs fractional values and can account for rounding. For this exercise, the input and required output are whole numbers, so float representation adds no benefit.

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The Python 3.11 Decimal documentation describes adjustable precision and decimal arithmetic suited to cases where exact decimal equality invariants matter. Decimal is useful in appropriate tasks, but it is not a more correct replacement for integers here: the assignment asks for integer input and output.

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