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- 仓库
- ffsshhttiikk/opencode-agents-skills
- 最近来源活动
- 2026年2月28日 22:55
- 检测到的 SKILL.md 语言
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SKILL.md
来源说明 · 只读预览- name
- wrangling
- description
- Data wrangling fundamentals
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"data-analysts","category":"data-science"}
## What I do
- Transform and clean raw data
- Reshape data structures
- Handle data quality issues
- Parse and extract from unstructured formats
- Merge and join datasets
- Create derived features
## When to use me
Use me when:
- Raw data needs preparation
- Data from multiple sources needs combining
- Complex transformations required
- Preparing data for analysis or ML
## Key Concepts
### Common Transformations
```python
import pandas as pd
# Pivot/Unpivot
pivot_df = df.pivot(index="date", columns="product", values="sales")
melted = pd.melt(df, id_vars=["id"], value_vars=["q1","q2","q3","q4"])
# String operations
df["email_domain"] = df["email"].str.split("@").str[1]
df["name_clean"] = df["name"].str.strip().str.title()
# Conditional logic
df["segment"] = np.where(df["income"] > 100000, "Premium",
np.where(df["income"] > 50000, "Standard", "Basic"))
# Apply custom functions
def categorize(age):
if age < 18: return "minor"
elif age < 65: return "adult"
return "senior"
df["age_category"] = df["age"].apply(categorize)
# Rolling calculations
df["rolling_avg"] = df["sales"].rolling(window=7).mean()
df["pct_change"] = df["sales"].pct_change()
```
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