search
Search
Unlock 100+ guides
search toc
close
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
What does this mean?
Why is this true?
Give me some examples!
search
keyboard_voice
close
Searching Tips
Search for a recipe:
"Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview
Doc Search
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Shrink
Navigate to

# NumPy | histogram method

schedule Aug 10, 2023
Last updated
local_offer
PythonNumPy
Tags
mode_heat
Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!

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. ] ```
Edited by 0 others
thumb_up
thumb_down
Comment
Citation
Ask a question or leave a feedback...
thumb_up
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!