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NumPy | bincount method

schedule Aug 10, 2023
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Numpy's `bincount(~)` method computes the bin count (i.e. the number of values that fall in an interval) given an array of values. By default, the interval size of each bin is one, and so the number of bins is dependent on the range of the input values.

Parameters

1. `a` | `array-like`

The input array.

2. `weights` | `array-like` | `optional`

An array containing the weights placed on each of the input values. If a value falls in a particular bin, instead of incrementing the count by one, we increment by the corresponding weight. The shape must be the same as that of `a`. By default, the weights are all one.

3. `minlength` | `int` | `optional`

The minimum number of bins.

Return value

A Numpy array containing the bin counts.

Examples

Basic usage

``` my_hist = np.bincount([1,3,6,6,10])my_hist array([0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 1]) ```

Specifying weights

``` my_hist = np.bincount([1,3,6,6,10], weights=[2,3,4,5,6])my_hist array([0., 2., 0., 3., 0., 0., 9., 0., 0., 0., 6.]) ```

The reason why we get a 9 there is that the two 6s we have in the input array obviously fall in the same bin, and since their respective weights are 4 and 5, we end up with a total bin count of `4+5=9`.

Specifying the min length

``` my_hist = np.bincount([1,3,6,6,10], minlength=20)my_hist array([0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]) ```

Notice bin size remained as one - the minlength just widened the global range.

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