np.quantile和 np.percentile区别
时间: 2024-06-07 18:12:19 浏览: 21
np.quantile和np.percentile是两个用于计算分位数的函数,但它们有一些区别。
1. 输入参数不同:np.quantile的输入参数是数组和一个百分位数,而np.percentile的输入参数是数组和一个百分位数或一组百分位数。
2. 计算方式不同:np.quantile使用的是线性插值的方法来计算分位数。它首先将给定的百分位数转换为对应的位置索引,然后在该位置索引的两个最近的数据点之间进行插值计算。这意味着分位数的计算结果可能不在原始数组中的任何一个数据点上。
3. 支持多维数组:np.quantile可以处理多维数组,可以在指定的轴上计算分位数,而np.percentile只能处理一维数组。
综上所述,np.quantile和np.percentile在输入参数和计算方式上有所区别,并且np.quantile支持多维数组的计算。
相关问题
生成一个ndarray,然后用np.percentile和np.quantile还求一下这个array的百分位数
好的,以下是生成ndarray并计算百分位数的示例代码:
```python
import numpy as np
# 生成一个形状为(100,)的随机数组
arr = np.random.rand(100)
# 计算数组的25、50、75百分位数
pct_25 = np.percentile(arr, 25)
pct_50 = np.percentile(arr, 50)
pct_75 = np.percentile(arr, 75)
# 计算数组的0.25、0.5、0.75分位数
qtl_25 = np.quantile(arr, 0.25)
qtl_50 = np.quantile(arr, 0.5)
qtl_75 = np.quantile(arr, 0.75)
print("25th percentile: {}".format(pct_25))
print("50th percentile: {}".format(pct_50))
print("75th percentile: {}".format(pct_75))
print("0.25 quantile: {}".format(qtl_25))
print("0.5 quantile: {}".format(qtl_50))
print("0.75 quantile: {}".format(qtl_75))
```
输出结果类似于:
```
25th percentile: 0.2491505511658243
50th percentile: 0.4823911076846821
75th percentile: 0.7088452628239561
0.25 quantile: 0.2491505511658243
0.5 quantile: 0.4823911076846821
0.75 quantile: 0.7088452628239561
```
np.quantile
numpy.quantile() is a function in NumPy library that returns the value of the quantile at a specified percentile of a given array. The percentile is specified as a float between 0 and 1, inclusive, where 0 represents the minimum value and 1 represents the maximum value.
Syntax: `numpy.quantile(a, q, axis=None, out=None, interpolation='linear')`
Parameters:
- a: Input array.
- q: Quantile to be computed, expressed as a float between 0 and 1 inclusive.
- axis: Axis along which to compute the quantile. The default is None, which computes the quantile over the entire array.
- out: Optional output array in which to place the result.
- interpolation: Interpolation method to use when the desired quantile lies between two data points. The default is 'linear'.
Returns:
- Quantile value(s) at the specified percentile(s).
Example:
```python
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
# find the median (50th percentile)
median = np.quantile(arr, 0.5)
print("Median of the array is:", median)
# find the 25th percentile
p25 = np.quantile(arr, 0.25)
print("25th percentile of the array is:", p25)
# find the 75th percentile
p75 = np.quantile(arr, 0.75)
print("75th percentile of the array is:", p75)
```
Output:
```
Median of the array is: 5.5
25th percentile of the array is: 3.25
75th percentile of the array is: 7.75
```
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