python 中的 num py . nanquantile()
原文:https://www.geeksforgeeks.org/numpy-nanquantile-in-python/
numpy.nanquantile(arr, q, axis = None) : 沿指定轴计算给定数据(数组元素)的第 q 个分位数，忽略 nan 值。
当处理正态分布时，分位数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median。Q3 - Q2表示给定数据集的分位数区间。
参数: arr:【array _ like】输入数组。 q : 分位数值。 轴:【int 或 int 的元组】轴，我们要沿着该轴计算分位数值。否则，它将考虑将 arr 展平(在所有轴上工作)。axis = 0 表示沿列工作，axis = 1 表示沿行工作。 out:【n 数组，可选】我们要放置结果的不同数组。数组必须具有与预期输出相同的维度。
结果: q 第个数组分位数(如果轴为无，则为标量值)或沿指定轴有分位数值的数组，忽略 nan 值。
代码#1 :
# Python Program illustrating
# numpy.nanquantile() method
import numpy as np
# 1D array
arr = [20, 2, 7, np.nan, 34]
print("arr : ", arr)
print("\n-Q1 quantile of arr : ", np.quantile(arr, .50))
print("Q2 - quantile of arr : ", np.quantile(arr, .25))
print("Q3 - quantile of arr : ", np.quantile(arr, .75))
print("\nQ1 - nanquantile of arr : ", np.nanquantile(arr, .50))
print("Q2 - nanquantile of arr : ", np.nanquantile(arr, .25))
print("Q3 - nanquantile of arr : ", np.nanquantile(arr, .75))
输出:
arr : [20, 2, 7, nan, 34]
Q1 - quantile of arr : nan
Q2 - quantile of arr : nan
Q3 - quantile of arr : nan
Q1 - nanquantile of arr : 13.5
Q2 - nanquantile of arr : 5.75
Q3 - nanquantile of arr : 23.5
代码#2:
# Python Program illustrating
# numpy.nanquantile() 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("\narr : \n", arr)
# quantile of the flattened array
print("\nQ2 quantile of arr, axis = None : ", np.quantile(arr, .50))
print("\nQ2 quantile of arr, axis = None : ", np.nanquantile(arr, .50))
print("0th quantile of arr, axis = None : ", np.nanquantile(arr, 0))
输出:
arr :
[[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]]
Q2 quantile of arr, axis = None : nan
Q2 quantile of arr, axis = None : 14.5
0th quantile of arr, axis = None : 1.0
代码#3:
# Python Program illustrating
# numpy.nanquantile() 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("\narr : \n", arr)
# quantile along the axis = 0
print("\nQ2 quantile of arr, axis = 0 : ", np.nanquantile(arr, .50, axis = 0))
print("0th quantile of arr, axis = 0 : ", np.nanquantile(arr, 0, axis = 0))
# quantile along the axis = 1
print("\nQ2 quantile of arr, axis = 1 : ", np.nanquantile(arr, .50, axis = 1))
print("0th quantile of arr, axis = 1 : ", np.nanquantile(arr, 0, axis = 1))
print("\nQ2 quantile of arr, axis = 1 : \n",
np.nanquantile(arr, .50, axis = 1, keepdims = True))
print("\n0th quantile of arr, axis = 1 : \n",
np.nanquantile(arr, 0, axis = 1, keepdims = True))
输出:
arr :
[[14, nan, 12, 33, 44], [15, nan, 27, 8, 19], [23, 2, nan, 1, 4]]
Q2 quantile of arr, axis = 0 : [15\. 2\. 19.5 8\. 19\. ]
0th quantile of arr, axis = 0 : [14\. 2\. 12\. 1\. 4.]
Q2 quantile of arr, axis = 1 : [23.5 17\. 3\. ]
0th quantile of arr, axis = 1 : [12\. 8\. 1.]
Q2 quantile of arr, axis = 1 :
[[23.5]
[17\. ]
[ 3\. ]]
0th quantile of arr, axis = 1 :
[[12.]
[ 8.]
[ 1.]]
