[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CXT8AD04MFYDA32VW3FKAG":3,"skill-same:01M6CXT8AD04MFYDA32VW3FKAG":72},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":22,"linkOnly":23,"markdownContent":24,"markdownBody":25,"frontMatter":26,"refName":30,"commitSha":31,"skillDirectory":5,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":35,"install":36,"resources":49},"01M6CXT8AD04MFYDA32VW3FKAG","wrangling",null,"Data wrangling 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ML\n\n## Key Concepts\n\n### Common Transformations\n```python\nimport pandas as pd\n\n# Pivot\u002FUnpivot\npivot_df = df.pivot(index=\"date\", columns=\"product\", values=\"sales\")\nmelted = pd.melt(df, id_vars=[\"id\"], value_vars=[\"q1\",\"q2\",\"q3\",\"q4\"])\n\n# String operations\ndf[\"email_domain\"] = df[\"email\"].str.split(\"@\").str[1]\ndf[\"name_clean\"] = df[\"name\"].str.strip().str.title()\n\n# Conditional logic\ndf[\"segment\"] = np.where(df[\"income\"] > 100000, \"Premium\",\n                np.where(df[\"income\"] > 50000, \"Standard\", \"Basic\"))\n\n# Apply custom functions\ndef categorize(age):\n    if age \u003C 18: return \"minor\"\n    elif age \u003C 65: return \"adult\"\n    return \"senior\"\n    \ndf[\"age_category\"] = df[\"age\"].apply(categorize)\n\n# Rolling calculations\ndf[\"rolling_avg\"] = df[\"sales\"].rolling(window=7).mean()\ndf[\"pct_change\"] = df[\"sales\"].pct_change()\n```\n","## What I do\n\n- Transform and clean raw data\n- Reshape data structures\n- Handle data quality issues\n- Parse and extract from unstructured formats\n- Merge and join datasets\n- Create derived features\n\n## When to use me\n\nUse me when:\n- Raw data needs preparation\n- Data from multiple sources needs combining\n- Complex transformations required\n- Preparing data for analysis or ML\n\n## Key Concepts\n\n### Common Transformations\n```python\nimport pandas as pd\n\n# Pivot\u002FUnpivot\npivot_df = df.pivot(index=\"date\", columns=\"product\", values=\"sales\")\nmelted = pd.melt(df, id_vars=[\"id\"], value_vars=[\"q1\",\"q2\",\"q3\",\"q4\"])\n\n# String operations\ndf[\"email_domain\"] = df[\"email\"].str.split(\"@\").str[1]\ndf[\"name_clean\"] = df[\"name\"].str.strip().str.title()\n\n# Conditional logic\ndf[\"segment\"] = np.where(df[\"income\"] > 100000, \"Premium\",\n                np.where(df[\"income\"] > 50000, \"Standard\", \"Basic\"))\n\n# Apply custom functions\ndef categorize(age):\n    if age \u003C 18: return \"minor\"\n    elif 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