Python numpy.quantile()

numpy.quantile(arr, q, axis = None) :计算给定数据（数组元素）沿指定轴线的第q个四分位数。当人们处理正态分布时，量化在统计学中起着非常重要的作用。在上图中，Q2是正态分布数据的中位数。Q3-Q2代表 Interquartile Range的数据集。

参数 :

arr :[array_like]输入阵列。

q :四分位值。

axis :[int或int的tuples]轴，我们想沿着这个轴来计算量化值。否则，它将认为Arr是平坦的（在所有轴上工作）。 axis = 0意味着沿着列工作， axis = 1意味着沿着行工作。

out :[ndarray, optional]不同的数组，我们想把结果放在其中。该数组必须具有与预期输出相同的尺寸。

结果 :

数组的第q个四分位数（如果没有轴，则为标量值）或沿指定轴的四分位值的数组。

代码 #1:

# Python Program illustrating
# numpy.quantile() method
import numpy as np
 
 
# 1D array
arr = [20, 2, 7, 1, 34]
 
print("arr : ", arr)
print("Q2 quantile of arr : ", np.quantile(arr, .50))
print("Q1 quantile of arr : ", np.quantile(arr, .25))
print("Q3 quantile of arr : ", np.quantile(arr, .75))
print("100th quantile of arr : ", np.quantile(arr, .1))

输出 :

arr : [20, 2, 7, 1, 34]
Q2 quantile of arr : 7.0)
Q1 quantile of arr : 2.0)
Q3 quantile of arr : 20.0)
100th quantile of arr : 1.4)

代码 #2:

# Python Program illustrating
# numpy.quantile() method
import numpy as np
  
# 2D array
arr = [[14, 17, 12, 33, 44], 
       [15, 6, 27, 8, 19],
       [23, 2, 54, 1, 4, ]]
print("\narr : \n", arr)
    
# quantile of the flattened array
print("\n50th quantile of arr, axis = None : ", np.quantile(arr, .50))
print("0th quantile of arr, axis = None : ", np.quantile(arr, 0))
    
# quantile along the axis = 0
print("\n50th quantile of arr, axis = 0 : ", np.quantile(arr, .25, axis = 0))
print("0th quantile of arr, axis = 0 : ", np.quantile(arr, 0, axis = 0))
   
# quantile along the axis = 1
print("\n50th quantile of arr, axis = 1 : ", np.quantile(arr, .50, axis = 1))
print("0th quantile of arr, axis = 1 : ", np.quantile(arr, 0, axis = 1))
  
print("\n0th quantile of arr, axis = 1 : \n",
   np.quantile(arr, .50, axis = 1, keepdims = True))
print("\n0th quantile of arr, axis = 1 : \n",
   np.quantile(arr, 0, axis = 1, keepdims = True))

输出 :

arr : 
[[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]]

50th quantile of arr, axis = None : 15.0
0th quantile of arr, axis = None : 1)

50th quantile of arr, axis = 0 : [14.5  4.  19.5  4.5 11.5]
0th quantile of arr, axis = 0 : [14  2 12  1  4]

50th quantile of arr, axis = 1 : [17. 15.  4.]
0th quantile of arr, axis = 1 : [12  6  1]

0th quantile of arr, axis = 1 : 
[[17.]
[15.]
[ 4.]]

0th quantile of arr, axis = 1 : 
[[12]
[ 6]
[ 1]]