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Comment #1 posted on 2021-05-05 19:49:39 by b-yeezi

Another great show

Thanks for another great show. I look forward to your next one.

As to your use of `pd.apply` in lieu of `np.select`, here's my 2 cents:

Apply is more readable in most cases, but select is more performant. When performance matters, or when the dataset is very large, you might want to use `np.select`. For instance, when using `np.select` on your example here, the output was 10x faster on my PC.

```
%timeit df.apply(Scorelevel, axis=1)

448 µs ± 2.88 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

```
%timeit np.select(cond_list, choice_list, default='Require Activation')

55.6 µs ± 440 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```

In many cases, the readability can trump the need for speed, but just wanted to give a counter-point.

Comment #2 posted on 2021-05-05 19:58:07 by b-yeezi

One more speed gain

If you really want to fly, you can turn the pandas series to numpy arrays first. For you example, it got twice as 2x faster than regular `np.select`.

Example:
```
cond_list = [df['Score'].values >= 9,
((df['Score'].values >= 8) & (df['Score'].values < 9)),
((df['Score'].values >= 7) & (df['Score'].values < 8)),
((df['Score'].values >= 6) & (df['Score'].values < 7)),
((df['Score'].values >= 5) & (df['Score'].values < 6)),
((df['Score'].values >= 4) & (df['Score'].values < 5))]

%timeit np.select(cond_list, choice_list, default='Require Activation')
23.5 µs ± 1.74 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```

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