# My method for fast mental conversion between ℃ and ℉

- Anchor points
- Steps to perform the conversion
- Code
- Proof of error bound
- Final thoughts and when not to use it

As a speaker of multiple languages, I’m often aware of how inherent
habits in our speech can greatly influence the extent to which other
people make sense of it. But even when you speak the same language,
even a topic as simple as the weather can already bring unnecessary
friction to the conversation if the speakers are using incompatible
units (*cough* *cough*). Or maybe I’m just coming up with an
arbitrary reason to justify this party trick. In any case, I describe
a mental heuristic that gets you within 0.25℃ for any temperature in
Fahrenheit, and prove the error bound. For the other direction, the
error in converting a temperature in Celsius to Fahrenheit is at most
0.5℉.

With this method, you get an immediate sense of the rough temperature in Celsius for a given temperature in Fahrenheit, and if you calculate a bit more, then the error is 0.25℃.

## Anchor points

I memorize the following table. I recommend remembering that 50℉ corresponds to 10℃. Since Fahrenheit and Celsius have a linear relationship, a difference of 9℉ corresponds to a difference of 5℃. You can get the other numbers by adding as needed.

Fahrenheit | Celsius |
---|---|

32 | 0 |

41 | 5 |

50 |
10 |

59 | 15 |

68 | 20 |

77 | 25 |

86 | 30 |

## Steps to perform the conversion

Given a temperature \(T_F\) and the table,

- Look up the nearest Farenheit value \(v\) in the table. If it exists then you are done and the answer is \(T[v]\).
- Otherwise, compute \(\frac{T_F-T[v]}{2}\), let the result be \(d\)
- The approximation is given by \(T[v]+d\)

Here’s an example.

- Suppose we are given 72℉. The nearest value in the table is 68℉, corresponding to 20℃.
- Now we compute \(\frac{72-68}{2}=2\)
- Now we add the two results to get 22℃.

## Code

I can render the above steps into code so it’s unambiguous what I actually mean. Note that in the code I didn’t use a lookup table but instead some arithmetic to find the closest anchor point. Obviously in practice it’ll be memorized.

```
def convert_approx(given):
# Nearest memorized temperature
close = round((given - 5) / 9) * 9 + 5
# Convert to Celsius
rough = (close - 32) // 9
# Half of the difference
diff = (given - close) / 2
return rough + diff
```

## Proof of error bound

First observe since the memorized intervals occur every 9℉, the difference between the given temperature and nearest interval is at most 9/2 ℉. Then the conversion is approximated to 1/2 ℃/℉, so we calculate:

\[9/2(5/9-1/2) = 0.25℃\]## Final thoughts and when not to use it

That’s pretty much it. In summary the conversion is:

- Accurate to within 0.25℃ for any temperature in Fahrenheit, or 0.5℉ for any temperature in Celsius.
- Simply calculable; you never need to divide by more than 2.
- Gives immediate feedback; at every step you get a temperature which is roughly the temperature in Celsius.

If you’re converting temperature in the thousands of degrees and higher, you’re better off approximating it by multiplying by 2 to go from ℃ to ℉. It’s unlikely you want super precise conversions in that temperature range, and the temperatures essentially have a direct linear relationship in that range anyway.