# Data Transform

Transform, clean, reshape, and preprocess data using pandas and numpy.

- Repository: https://github.com/beita6969/ScienceClaw (1024 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add beita6969/ScienceClaw --skill data-transform -g -a claude-code -y`
Codex: `npx skills add beita6969/ScienceClaw --skill data-transform -g -a codex -y`
Cursor: `npx skills add beita6969/ScienceClaw --skill data-transform -g -a cursor -y`
Gemini CLI: `npx skills add beita6969/ScienceClaw --skill data-transform -g -a gemini-cli -y`

## Description

Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Data Transformation (Universal)

## Overview
This skill enables you to perform comprehensive data transformations including cleaning, normalization, reshaping, filtering, and feature engineering. Unlike cloud-hosted solutions, this skill uses standard Python data manipulation libraries (**pandas**, **numpy**, **sklearn**) and executes **locally** in your environment, making it compatible with **ALL LLM providers** including GPT, Gemini, Claude, DeepSeek, and Qwen.

## When to Use This Skill
- Clean and preprocess raw data
- Normalize or scale numeric features
- Reshape data between wide and long formats
- Handle missing values
- Filter and subset datasets
- Merge multiple datasets
- Create new features from existing ones
- Convert data types and formats

## How to Use

### Step 1: Import Required Libraries
```python
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import warnings
warnings.filterwarnings('ignore')
```

### Step 2: Data Cleaning
```python
# Load data
df = pd.read_csv('data.csv')

# Check for missing values
print("Missing values per column:")
print(df.isnull().sum())

# Remove duplicates
df_clean = df.drop_duplicates()
print(f"Removed {len(df) - len(df_clean)} duplicate rows")

# Remove rows with any missing values
df_clean = df_clean.dropna()

# Or fill missing values
df_clean = df.copy()
df_clean['numeric_col'] = df_clean['numeric_col'].fillna(df_clean['numeric_col'].median())
df_clean['categorical_col'] = df_clean['categorical_col'].fillna('Unknown')

# Remove outliers using IQR method
def remove_outliers(df, column, multiplier=1.5):
    Q1 = df[column].quantile(0.25)
    Q3 = df[column].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - multiplier * IQR
    upper_bound = Q3 + multiplier * IQR
    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]

The rest: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/data-transform/SKILL.md