vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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npx skills add https://github.com/vamseeachanta/workspace-hub --skill autoviz-6-outlier-detection-and-highlighting命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
正在显示 SKILL.md
基于 SOC 职业分类
| name | autoviz-6-outlier-detection-and-highlighting |
| description | Sub-skill of autoviz: 6. Outlier Detection and Highlighting. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Automatic Outlier Identification:
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Create dataset with outliers
np.random.seed(42)
n = 1000
# Normal data with injected outliers
revenue = np.concatenate([
np.random.normal(1000, 200, n - 20), # Normal values
np.random.uniform(3000, 5000, 10), # High outliers
np.random.uniform(-500, 0, 10) # Low outliers
])
units = np.concatenate([
np.random.normal(50, 10, n - 15),
np.random.uniform(150, 200, 15) # Outliers
])
df = pd.DataFrame({
"revenue": revenue,
"units": units,
"cost": np.abs(revenue * 0.6 + np.random.randn(n) * 100),
"category": np.random.choice(["A", "B", "C"], n),
"region": np.random.choice(["North", "South", "East", "West"], n)
})
AV = AutoViz_Class()
# AutoViz automatically:
# 1. Detects outliers using IQR method
# 2. Highlights them in box plots
# 3. Shows them in scatter plots
# 4. Reports outlier counts
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
verbose=2,
chart_format="svg"
)
Custom Outlier Analysis Wrapper:
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
def analyze_with_outlier_report(df: pd.DataFrame, target: str = "") -> dict:
"""
Run AutoViz and provide detailed outlier report.
Args:
df: Input DataFrame
target: Target variable name (optional)
Returns:
Dictionary with analysis results and outlier info
"""
# Calculate outliers before visualization
outlier_info = {}
numeric_cols = df.select_dtypes(include=[np.number]).columns
for col in numeric_cols:
Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]
outlier_info[col] = {
"count": len(outliers),
"percentage": len(outliers) / len(df) * 100,
"lower_bound": lower_bound,
"upper_bound": upper_bound,
"min_outlier": outliers[col].min() if len(outliers) > 0 else None,
"max_outlier": outliers[col].max() if len(outliers) > 0 else None
}
AV = AutoViz_Class()
df_analyzed = AV.AutoViz(
filename=,
dfte=df,
depVar=target,
verbose=,
chart_format=
)
{
: df_analyzed,
: outlier_info,
: (info[] info outlier_info.values())
}