Providing outlier detection suggestions
I’m proposing to share a CSV or screenshot and offer a quick Python script snippet to detect outliers using the IQR method. Here’s how I’d outline it:
- First, confirm if it’s a unit or sampling frequency issue; check device calibration.
- Visualize data using boxplots or time series graphs.
- Remove outliers using IQR or Z-scores.
- Use robust statistics like median or P95 instead of the mean.
- Share a concise version for the coach: three conclusions, one graph, and next steps.
- If I can help further, please send the data or structure with the project name and meaning.
Here’s a small Python snippet as well:
import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
cols = ['speed','heart_rate','power'] # TODO
q1 = df[cols].quantile(0.25)
q3 = df[cols].quantile(0.75)
iqr = q3 - q1
mask = ((df[cols] < (q1 - 1.5 * iqr)) | (df[cols] > (q3 + 1.5 * iqr))).any(axis=1)
clean = df[mask]
summary = clean[cols].agg(['median', 'mean', 'max', 'quantile']).T
Remember to adjust the quantile parameter to 0.95!Suggesting metrics and drafting a message
I’m thinking about including power metrics for sports like cycling, focusing on variables such as power, velocity, and heart rate. I want to keep my code comments minimal while providing clear suggestions. Given the instruction to avoid heavy formatting, I will include one concise code snippet. I’ll also draft a message for the coach explaining the data, suggesting that there seem to be outliers, potentially due to device or unit issues, and that we've recalculated using the median and P95.
Here’s how I might phrase it: "Initial observation shows outliers, possibly due to device or unit issues. We have recalculated based on the median and P95, and I recommend monitoring the range..."
听起来像是数据里有异常/夸大的点。要把教练“看不下去”的感觉变成可用结论，可以这样处理：
- 先排查源头：单位是否混了（km/h vs m/s）、采样频率变了、设备是否校准过、电量低/信号丢包。
- 画两张快速图：时序图看尖峰，箱线图看离群；再看中位数和P95，少用“均值”。
- 清洗离群值：用 IQR 或 Z-score 先做一版“稳健”汇总；保留原始版本以便对照。
- 对外呈现：一页总结＝3条结论 + 1张图 + 下一步（如“重测/校准某段”）。
要我帮你跑一遍，给我数据文件或字段说明就行（列名、单位）。先给你一个最小可用的 Python 清洗/汇总示例：
import pandas as pd
import numpy as np
# 读入数据
df = pd.read_csv('data.csv')
# 需要分析的数值列：按你的实际列名改
cols = ['speed', 'heart_rate', 'power']
# IQR 去离群：对任一列超出阈值的行标为离群
q1 = df[cols].quantile(0.25)
q3 = df[cols].quantile(0.75)
iqr = q3 - q1
is_inlier = ~((df[cols] < (q1 - 1.5 * iqr)) | (df[cols] > (q3 + 1.5 * iqr))).any(axis=1)
clean = df[is_inlier].copy()
# 稳健汇总（更适合给教练看）
summary = pd.DataFrame({
'median': clean[cols].median(),
'p95': clean[cols].quantile(0.95),
'max': clean[cols].max(),
'count': clean[cols].count(),
})
print('原始行数:', len(df), '清洗后:', len(clean))
print(summary)
需要的话我也可以：
- 帮你出图（时序/箱线图）和一页汇报稿；
- 写个可复用的小脚本/Notebook，自动导入 CSV、清洗、出图、导出结论。