sciPy stats.binned_statistic_dd()函数| Python

stats.binned_statistic_dd(arr, values, statistic='mean', bins=10, range=None)函数计算给定二维数据的分箱统计值。
它的工作原理类似于 histogram2d。由于直方图函数制作垃圾箱并计算编号。每个 bin 中的点数；此函数计算每个 bin 的值的总和、平均值、中值、计数或其他统计信息。

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, 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.

代码#1：

# stats.binned_statistic_dd() method 
import numpy as np
from scipy import stats
  
x = np.ones(10)
y = np.ones(10)
  
print ("x : \n", x)
print ("\ny : \n", y)
  
print ("\nbinned_statistic_2d for count : ", 
       stats.binned_statistic_dd([x, y], None, 'count', bins = 3))

输出 ：

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))

代码#2：

# importing 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 ("\nbinned_statistic_2d for count : ", 
        stats.binned_statistic_dd([x, y], None,
        'count', bins=3, range=[[2,3],[0,0.5]]))

输出 ：

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))