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@ -4,54 +4,6 @@ This file contains errata for the
["Julia for Data Analysis"](https://www.manning.com/books/julia-for-data-analysis?utm_source=bkamins&utm_medium=affiliate&utm_campaign=book_kaminski2_julia_3_17_22)
book that has been written by Bogumił Kamiński and has been published by [Manning Publications Co.](https://www.manning.com/)
### Chapter 1, section 1.2.1, page 7
I show the following example of code execution:
```
julia> function sum_n(n)
s = 0
for i in 1:n
s += i
end
return s
end
sum_n (generic function with 1 method)
julia> @time sum_n(1_000_000_000)
0.000001 seconds
500000000500000000
```
This timing is very fast (and the reason is explained in the book).
The issue is that this is the situation under Julia 1.7.
Under Julia 1.8 and Julia 1.9 running the same code takes longer (tested under Julia 1.9-beta4):
```
julia> @time sum_n(1_000_000_000)
2.265569 seconds
500000000500000000
```
The reason for this inconsistency is a bug in the `@time` macro introduced in Julia 1.8 release.
The `sum_n(1_000_000_000)` call (without `@time`) is executed fast.
Here is a simplified benchmark (run under Julia 1.9-beta4):
```
julia> let
start = time_ns()
v = sum_n(1_000_000_000)
stop=time_ns()
v, Int(stop - start)
end
(500000000500000000, 1000)
```
Unfortunately there is an issue with the `@time`
macro used in global scope, that needs to be resolved in Base Julia.
See [this issue](https://github.com/JuliaLang/julia/issues/47561).
### Chapter 2, introduction, page 20
* middle of page 20: the provided link http://mng.bz/5mWD explaining *k-times winsorized mean* definition no longer works.