python 用3σ法则检验x变量是否存在异常值,将异常值剔除 代码
时间: 2024-05-09 19:18:14 浏览: 17
可以使用以下代码:
```python
import numpy as np
def remove_outliers(data):
data_mean = np.mean(data)
data_std = np.std(data)
cut_off = data_std * 3
lower, upper = data_mean - cut_off, data_mean + cut_off
new_data = [x for x in data if lower < x < upper]
return new_data
```
其中,`data`是需要检测异常值并剔除的数据,函数返回剔除异常值后的数据。
相关问题
python 用3σ法则检验x变量是否存在异常值,记录异常值的索引,然后剔除x,y值的索引对应的元素函数代码
好的,请输入以下代码:
```python
import numpy as np
def detect_outliers(data):
mean = np.mean(data)
std = np.std(data)
threshold = 3 * std
outlier_indices = []
for i in range(len(data)):
if abs(data[i] - mean) > threshold:
outlier_indices.append(i)
return outlier_indices
def remove_outliers(x, y, outlier_indices):
x_cleaned = np.delete(x, outlier_indices, axis=0)
y_cleaned = np.delete(y, outlier_indices, axis=0)
return x_cleaned, y_cleaned
# example usage
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100])
y = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 1000])
outlier_indices = detect_outliers(x)
print("Outlier indices:", outlier_indices)
x_cleaned, y_cleaned = remove_outliers(x, y, outlier_indices)
print("x_cleaned:", x_cleaned)
print("y_cleaned:", y_cleaned)
```
这段代码可以使用3σ法则检验变量x是否存在异常值,并记录异常值的索引。然后利用该索引对应的元素剔除x,y的索引对应的元素。这样可以得到去除异常值的新x和y。
python 用3σ法则检验x变量是否存在异常值,记录异常值的索引,然后剔除x,y值的索引对应的元素 代码
可以使用numpy库中的percentile函数进行3σ法则检验,代码如下:
import numpy as np
def remove_outliers(x, y):
# 计算x的平均值与标准差
mean = np.mean(x)
std = np.std(x)
# 计算下限与上限
lower_limit = mean - 3 * std
upper_limit = mean + 3 * std
# 记录异常值的索引
outliers_index = np.where(np.logical_or(x < lower_limit, x > upper_limit))[0]
# 剔除x,y值的索引对应的元素
x_clean = np.delete(x, outliers_index)
y_clean = np.delete(y, outliers_index)
return x_clean, y_clean, outliers_index
# 测试代码
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
y = np.array([2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30])
x_clean, y_clean, outliers_index = remove_outliers(x, y)
print(x_clean)
print(y_clean)
print(outliers_index)