Afflatus - 2 months ago 16

Python Question

I want a function that can take a series and a set of bins, and basically round up to the nearest bin. For example:

`my_series = [ 1, 1.5, 2, 2.3, 2.6, 3]`

def my_function(my_series, bins):

...

my_function(my_series, bins=[1,2,3])

> [1,2,2,3,3,3]

This seems to be very close to what Numpy's Digitize is intended to do, but it produces the wrong values (asterisks for wrong values):

`np.digitize(my_series, bins= [1,2,3], right=False)`

> [1, 1*, 2, 2*, 2*, 3]

The reason why it's wrong is clear from the documentation:

Each index i returned is such thatbins[i-1] <= x < bins[i]if bins is

monotonically increasing, orbins[i-1] > x >= bins[i]if bins is

monotonically decreasing. If values in x are beyond the bounds of

bins, 0 or len(bins) is returned as appropriate. If right is True,

then the right bin is closed so that the index i is such that

bins[i-1] < x <= bins[i] or bins[i-1] >= x > bins[i]`` if bins is

monotonically increasing or decreasing, respectively.

I can kind of get closer to what I want if I enter in the values decreasing and set "right" to True...

`np.digitize(my_series, bins= [3,2,1], right=True)`

> [3, 2, 2, 1, 1, 1]

but then I'll have to think of a way of basically methodically reversing the lowest number assignment (1) with the highest number assignment (3). It's simple when there are just 3 bins, but will get hairier when the number of bins get longer..there must be a more elegant way of doing all this.

Answer

We can simply use `np.digitize`

with its `right`

option set as `True`

to get the indices and then to extract the corresponding elements off `bins`

, bring in `np.take`

, like so -

```
np.take(bins,np.digitize(a,bins,right=True))
```

Source (Stackoverflow)

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