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sciPy function stats.binned_statistic () | python

stats.binned_statistic (x, values, statistic = & # 39; mean & # 39 ;, bins = 10, range = None) calculates statistic value for given data (array elements). 
It works similarly to the mean -median-mode-in-python-without-libraries/">median, count, or other statistics of values ​​for each bin.

Parameters:
arr: [array_like] input array to be binned.
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 () method

import numpy as np

from scipy import stats

 
# 1D array

arr = [ 20 , 2 , 7 , 1 , 34 ]

print ( "arr:" , arr) 

 

 
# mean -median-mode-in-python-without-libraries/">median

print ( "binned_statistic for mean -median-mode-in-python-without-libraries/">median:" , stats.binned_statistic (

arr, np.arange ( 5 ), statistic = mean -median-mode-in-python-without-libraries/">median’ , bins = 4 )) 

Output:

 arr: [20, 2, 7, 1, 34] binned_statistic for mean -median-mode-in-python-without-libraries/">median: BinnedStatisticResult (statistic = array ([2., nan, 0., 4.]), bin_edges = array ([1 ., 9.25, 17.5, 25.75, 34.]), binnumber = arr ay ([3, 1, 1, 1, 4], dtype = int64)) 

Code # 2:

# stats.binned_statistic () method

import numpy as np

from scipy import stats

 
# greedy

arr = [ 20 , 2 , 7 , 1 , 34 ]

print ( " binned_statistic for mean : " , stats.binned_statistic (

arr, np.arange ( 5 ), statistic = mean , bins = 2 )) 

Output:

 binned_statistic for  mean : BinnedStatisticResult (statistic = array ([2., 2.]), bin_edges = array ([1., 17.5, 34.]), binnumber = array ([2, 1, 1, 1, 2], dtype = int64)) 

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