csv-pipeline

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• SKILL.md
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SKILL.md (first lines)

# CSV Data Pipeline

Process tabular data (CSV, TSV, JSON, JSON Lines) using standard command-line tools and Python. No external dependencies required beyond Python 3.

## When to Use

- User provides a CSV/TSV/JSON file and asks to analyze, transform, or report on it
- Joining, filtering, grouping, or aggregating tabular data
- Converting between formats (CSV to JSON, JSON to CSV, etc.)
- Deduplicating, sorting, or cleaning messy data
- Generating summary statistics or reports
- ETL workflows: extract from one format, transform, load into another

## Quick Operations with Standard Tools

### Inspect

```bash
# Preview first rows
head -5 data.csv

# Count rows (excluding header)
tail -n +2 data.csv | wc -l

# Show column headers
head -1 data.csv

# Count unique values in a column (column 3)
tail -n +2 data.csv | cut -d',' -f3 | sort -u | wc -l
```

### Filter with `awk`

```bash
# Filter rows where column 3 > 100
awk -F',' 'NR==1 || $3 > 100' data.csv > filtered.csv

# Filter rows matching a pattern in column 2
awk -F',' 'NR==1 || $2 ~ /pattern/' data.csv > matched.csv

# Sum column 4
awk -F',' 'NR>1 {sum += $4} END {print sum}' data.csv
```

### Sort and Deduplicate

```bash
# Sort by column 2 (numeric)
head -1 data.csv > sorted.csv && tail -n +2 data.csv | sort -t',' -k2 -n >> sorted.csv

# Deduplicate by all columns
head -1 data.csv > deduped.csv && tail -n +2 data.csv | sort -u >> deduped.csv

# Deduplicate by specific column (keep first occurrence)
awk -F',' '!seen[$2]++' data.csv > deduped.csv
```

## Python Operations (for complex transforms)

### Read and Inspect

```python
import csv, json, sys
from collections import Counter

def read_csv(path, delimiter=','):
    """Read CSV/TSV into list of dicts."""
    with open(path, newline='', encoding='utf-8') as f:
        return list(csv.DictReader(f, delimiter=delimiter))

def write_csv(rows, path, delimiter=','):
    """Write list of dicts to CSV."""
    if not rows:
        return
    with open(path, 'w', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=rows[0].keys(), delimiter=delimiter)
        writer.writeheader()
        writer.writerows(rows)

# Quick stats
data = read_csv('data.csv')
print(f"Rows: {len(data)}")
print(f"Columns: {list(data[0].keys())}")
for col in data[0]:
    non_empty = sum(1 for r in data if r[col].strip())
    print(f"  {col}: {non_empty}/{len(data)} non-empty")
```

### Filter and Transform

```python
# Filter rows
filtered = [r for r in data if float(r['amount']) > 100]

# Add computed column
for r in data:
    r['total'] = str(float(r['price']) * int(r['quantity']))

# Rename columns
renamed = [{('new_name' if k == 'old_name' else k): v for k, v in r.items()} for r in data]

# Type conversion
for r in data:
    r['amount'] = float(r['amount'])
    r['date'] = r['date'].strip()
```

### Group and Aggregate

```python
from collections import defaultdict

def group_by(rows, key):
    """Group rows by a column value."""
    groups = defaultdict(list)
    for r in rows:
        groups[r[key]].append(r)
    return dict(groups)

From aAAaqwq/AGI-Super-Team, MIT.

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