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# NumPy | histogram method

NumPy
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schedule Jul 1, 2022
Last updated
local_offer PythonNumPy
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NumPy's `histogram(~)` method computes a histogram (frequency-count diagram).

# Parameters

1. `a` | `array-like`

The input array.

2. `bins`link | `array-like` | `optional`

The desired number of bins. If an array is provided, then it must contain edges. By default, we get 10 equal-width bins.

3. `range`link | `tuple` of `float` | `optional`

By default, the range is set to (`a.min()`, `a.max()`).

4. `weights`link | `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.

5. `density`link | `boolean` | `optional`

Whether to normalise to a probability density function (i.e. total area equaling one). By default, `density=False`.

# Return value

A tuple of two NumPy arrays:

1. The values of the histogram (i.e. the frequency counts)

2. The bin edges

# Examples

## Basic usage

``` my_hist, bin_edges = np.histogram([1,3,6,6,10])print("hist:", my_hist)print("bin_edges:", bin_edges) hist: [1 0 1 0 0 2 0 0 0 1]bin_edges: [ 1. 1.9 2.8 3.7 4.6 5.5 6.4 7.3 8.2 9.1 10. ] ```

Here, the `bin_edges` represent the intervals of the bins, and the `hist` represents the number of values that fall between the interval. For instance, there is a total of one item that falls between the interval `1` and `1.9`, so we get a value `1` for the spot in the histogram.

## Specifying the number of bins

``` my_hist, bin_edges = np.histogram([1,3,6,6,10], bins=5)print("hist:", my_hist)print("bin_edges:", bin_edges) hist: [1 1 2 0 1]bin_edges: [ 1. 2.8 4.6 6.4 8.2 10. ] ```

## Specifying bin edges

``` my_hist, bin_edges = np.histogram([1,3,6,6,10], bins=[1,5,10])print("hist:", my_hist)print("bin_edges:", bin_edges) hist: [2 3]bin_edges: [ 1 5 10] ```

## Specifying a range

``` my_hist, bin_edges = np.histogram([1,3,6,6,10], range=(0,20))print("hist:", my_hist)print("bin_edges:", bin_edges) hist: [1 1 0 2 0 1 0 0 0 0]bin_edges: [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20.] ```

## Specifying weights

``` my_hist, bin_edges = np.histogram([1,3,6,6,10], weights=[1,5,1,1,1])print("hist:", my_hist)print("bin_edges:", bin_edges) hist: [1 0 5 0 0 2 0 0 0 1]bin_edges: [ 1. 1.9 2.8 3.7 4.6 5.5 6.4 7.3 8.2 9.1 10. ] ```

The reason why we get a 5 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`.

## Normalising

``` my_hist, bin_edges = np.histogram([1,3,6,6,10], density=True)print("hist:", my_hist)print("bin_edges:", bin_edges) hist: [0.22222222 0. 0.22222222 0. 0. 0.44444444 0. 0. 0. 0.22222222]bin_edges: [ 1. 1.9 2.8 3.7 4.6 5.5 6.4 7.3 8.2 9.1 10. ] ```
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