wrangling

## 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() ```