python iqr

IQR stands for Interquartile Range. In Python, you can calculate the IQR using the statistics module. Here's an example:

import statistics

data = [10, 15, 20, 25, 30, 35, 40]
iqr = statistics.median(data[:len(data)//2]), statistics.median(data), statistics.median(data[len(data)//2+1:])
print("IQR:", iqr)

In this example, the data is a list of numbers. The IQR is calculated by finding the median of the lower half of the data (Q1), the median of the entire data set (Q2), and the median of the upper half of the data (Q3). The IQR is then represented as (Q1, Q2, Q3).

相关问题

iqr python

IQR (Interquartile Range) in Python can be calculated using the `numpy` library. Here's an example of calculating IQR:

import numpy as np

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
iqr = np.percentile(data, 75) - np.percentile(data, 25)
print("IQR:", iqr)

In this example, we have a list of numbers `data`, and we use the `percentile` function from `numpy` to calculate the 75th percentile and the 25th percentile. Subtracting the 25th percentile from the 75th percentile gives us the IQR.

IQR方法python

IQR方法是一种常用的异常检测方法，用于识别数据集中的异常值。下面是一个使用Python实现IQR方法的示例代码：

import numpy as np

def cal_iqr(arr):
    # 计算四分位距
    qr1 = np.quantile(arr, 0.25, method='averaged_inverted_cdf') # 下四分位数
    qr3 = np.quantile(arr, 0.75, method='averaged_inverted_cdf') # 上四分位数
    iqr = qr3 - qr1 # 计算四分位距
    return iqr

这段代码使用了NumPy库中的`quantile`函数来计算数据集的下四分位数和上四分位数，然后通过相减得到四分位距（IQR）。函数的输入参数`arr`是一个一维数组，表示待计算的数据集。

你可以将需要进行异常检测的数据传递给`cal_iqr`函数，它将返回数据集的四分位距。根据IQR方法的原理，可以将超过1.5倍IQR的值视为异常值。

参考资料：
- A walkthrough of Univariate Anomaly Detection in Python（很好学习资料）：\[链接\](https://www.analyticsvidhya.com/blog/2021/06/univariate-anomaly-detection-a-walkthrough-in-python/)
- 14种异常检测方法总结：\[链接\](https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/)
- PyOD库：\[链接\](https://pyod.readthedocs.io/en/latest/)
\[1\]\[2\]\[3\]
#### 引用[.reference_title]
- *1* [python：计算四分位距IQR](https://blog.csdn.net/weixin_41543800/article/details/127548569)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* *3* [删除异常值方法总结](https://blog.csdn.net/qq_32649321/article/details/123009459)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]