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# sciPy stats.binned_statistic_dd () function | python

` stats.binned_statistic_dd (arr, values, statistic = & # 39; mean & # 39 ;, bins = 10, range = None) ` calculates statistic value for given 2D data.
It works in a similar way to histogram2d. Since the histogram function makes bins and counts no. points in each basket; this function calculates the sum, average, mean -median-mode-in-python-without-libraries/">median, count, or other statistics of values ​​for each bin.

Parameters:
arr: [array_like] Data to histogram passed as (N, D) array
values: [array_like] on which stats to be calculated.
statistics: Statistics to compute { mean , count, mean -median-mode-in-python-without-libraries/">median, sum, function}. Default is mean .
bin: [int or scalars] If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines the bin edges.
range: (float, float) Lower and upper range of the bins and if not provided, range is from x.max () to x.min ().

Results: Statistics value for each bin; bin edges; bin number.

Code # 1:

 ` # stats.binned_statistic_dd () method ` ` import ` ` numpy as np ` ` from ` ` scipy ` ` import ` ` stats `   ` x ` ` = ` ` np.ones (` ` 10 ` `) ` ` y ` ` = ` ` np.ones (` ` 10 ` `) `   ` print ` ` (` `" x: "` `, x) ` ` print ` ` (` ` "y:" ` `, y) `   ` print ` ` (` `" binned_statistic_2d for count: "` `, ` ` stats.binned_statistic_dd ([x, y], ` ` None ` `, ` `’ count’ ` `, bins ` ` = ` ` 3 ` `)) `

Output:

x:
[1 ... 1. 1. 1. 1. 1. 1. 1. 1. 1.]

y:
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

binned_statistic_2d for count: BinnedStatisticddResult (statistic = array ([[0., 0., 0.],
[0., 10., 0.],
[0., 0., 0.]]), bin_edges = [array ([0.5, 0.83333333, 1.16666667, 1.5]),
array ([0.5, 0.83333333, 1.16666667, 1.5])],
binnumber = array ([12, 12, 12, 12, 12, 12, 12, 12, 12, 12], dtype = int64))

Code # 2:

 ` # import libraries ` ` import ` ` numpy as np ` ` from ` ` scipy ` ` import ` ` stats `   ` # using np.ones for x and y ` ` x ` ` = ` ` np.ones (` ` 10 ` `) ` ` y ` ` = ` ` np.ones (` ` 10 ` `) `   ` # Using binned_statistic_dd ` ` print ` ` (` `" binned_statistic_2d for count: "` `, ` ` stats.binned_statistic_dd ([x, y], ` ` None ` `, ` ` ` `’ count ’` `, bins ` ` = ` ` 3 ` `, ` ` range ` ` = ` ` [[` ` 2 ` `, ` ` 3 ` `], [` ` 0 ` `, ` ` 0.5 ` `]])) `

Output:

binned_statistic_2d for count: BinnedStatisticddResult ( statistic = array ([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]), bin_edges = [array ([2., 2.33333333, 2.66666667, 3.]),
array ([0., 0.16666667, 0.33333333, 0.5])],
binnumber = array ([4, 4, 4, 4, 4, 4, 4, 4, 4, 4], dtype = int64))

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