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version: "1.0.0" 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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