Python numpy.nanpercentile()

numpy.nanpercentile()用于计算给定数据（数组元素）沿指定轴线的第n个百分位数的函数，并忽略nan值。

语法 :

numpy.nanpercentile(arr, q, axis=None, out=None) 

参数 :

• arr : 输入阵列。
• q : 百分数值。
• axis：我们要计算百分位数的轴。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， axis=1意味着沿着行工作。
• out :不同的数组，我们要把结果放在其中。该数组必须具有与预期输出相同的尺寸。

返回 :数组的百分位数（如果坐标轴为零，则为标量值），或者数组中沿指定坐标轴的数值的百分位数。

**代码 #1 : **

# Python Program illustrating
# numpy.nanpercentile() method
   
import numpy as np
   
# 1D array
arr = [20, 2, 7, np.nan, 34]
print("arr : ", arr)
print("50th percentile of arr : ",
       np.percentile(arr, 50))
print("25th percentile of arr : ",
       np.percentile(arr, 25))
print("75th percentile of arr : ",
       np.percentile(arr, 75))
 
print("\n50th percentile of arr : ",
      np.nanpercentile(arr, 50))
print("25th percentile of arr : ",
       np.nanpercentile(arr, 25))
print("75th percentile of arr : ",
      np.nanpercentile(arr, 75))

输出 :

arr :  [20, 2, 7, nan, 34]
50th percentile of arr :  nan
25th percentile of arr :  nan
75th percentile of arr :  nan

50th percentile of arr :  13.5
25th percentile of arr :  5.75
75th percentile of arr :  23.5

代码 #2 :

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

输出 :

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

50th Percentile of arr, axis = None :  nan

50th Percentile of arr, axis = None :  14.5
0th Percentile of arr, axis = None :  1.0

50th Percentile of arr, axis = 0 :  [15.   2.  19.5  8.  19. ]
0th Percentile of arr, axis = 0 :  [14.  2. 12.  1.  4.]

50th Percentile of arr, axis = 1 :  [23.5 17.   3. ]
0th Percentile of arr, axis = 1 :  [12.  8.  1.]

0th Percentile of arr, axis = 1 : 
 [[23.5]
 [17. ]
 [ 3. ]]

0th Percentile of arr, axis = 1 : 
 [[12.]
 [ 8.]
 [ 1.]]

代码 #3:

# Python Program illustrating
# numpy.nanpercentile() method
 
import numpy as np
 
# 2D array
arr = [[14, np.nan, 12, 33, 44],
       [15, np.nan, 27, 8, 19],
       [23, np.nan, np.nan, 1, 4, ]]
print(& quot
       \narr: \n"
       , arr)
# Percentile along the axis = 1
print(& quot
       \n50th Percentile of arr, axis = 1 : & quot
       ,
       np.nanpercentile(arr, 50, axis=1))
print(& quot
       \n50th Percentile of arr, axis = 0 : & quot
       ,
       np.nanpercentile(arr, 50, axis=0))

输出 :

arr : 
 [[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, nan, nan, 1, 4]]

50th Percentile of arr, axis = 1 :  [23.5 17.   4. ]

50th Percentile of arr, axis = 0 :  [15.   nan 19.5  8.  19. ]
RuntimeWarning: All-NaN slice encountered
  overwrite_input, interpolation)