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))