CSV Processor Skill

Parse, transform, and analyze CSV files with advanced data manipulation capabilities.

Instructions

You are a CSV processing expert. When invoked:

1. Parse CSV Files:

   • Auto-detect delimiters (comma, tab, semicolon, pipe)
   • Handle different encodings (UTF-8, Latin-1, Windows-1252)
   • Process quoted fields and escaped characters
   • Handle multi-line fields correctly
   • Detect and use header rows

2. Transform Data:

   • Filter rows based on conditions
   • Select specific columns
   • Sort and group data
   • Merge multiple CSV files
   • Split large files into smaller chunks
   • Pivot and unpivot data

3. Clean Data:

   • Remove duplicates
   • Handle missing values
   • Trim whitespace
   • Normalize data formats
   • Fix encoding issues
   • Validate data types

4. Analyze Data:

   • Generate statistics (sum, average, min, max, count)
   • Identify data quality issues
   • Detect outliers
   • Profile column data types
   • Calculate distributions

Usage Examples

@csv-processor data.csv
@csv-processor --filter "age > 30"
@csv-processor --select "name,email,age"
@csv-processor --merge file1.csv file2.csv
@csv-processor --stats
@csv-processor --clean --remove-duplicates

Basic CSV Operations

Reading CSV Files

Python (pandas)

import pandas as pd

# Basic read
df = pd.read_csv('data.csv')

# Custom delimiter
df = pd.read_csv('data.tsv', delimiter='\t')

# Specify encoding
df = pd.read_csv('data.csv', encoding='latin-1')

# Skip rows
df = pd.read_csv('data.csv', skiprows=2)

# Select specific columns
df = pd.read_csv('data.csv', usecols=['name', 'email', 'age'])

# Parse dates
df = pd.read_csv('data.csv', parse_dates=['created_at', 'updated_at'])

# Handle missing values
df = pd.read_csv('data.csv', na_values=['NA', 'N/A', 'null', ''])

# Specify data types
df = pd.read_csv('data.csv', dtype={
    'user_id': int,
    'age': int,
    'score': float,
    'active': bool
})

JavaScript (csv-parser)

const fs = require('fs');
const csv = require('csv-parser');

// Basic parsing
const results = [];
fs.createReadStream('data.csv')
  .pipe(csv())
  .on('data', (row) => {
    results.push(row);
  })
  .on('end', () => {
    console.log(`Processed ${results.length} rows`);
  });

// With custom options
const Papa = require('papaparse');

Papa.parse(fs.createReadStream('data.csv'), {
  header: true,
  delimiter: ',',
  skipEmptyLines: true,
  transformHeader: (header) => header.trim().toLowerCase(),
  complete: (results) => {
    console.log('Parsed:', results.data);
  }
});

Python (csv module)

import csv

# Basic reading
with open('data.csv', 'r', encoding='utf-8') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row['name'], row['age'])

# Custom delimiter
with open('data.csv', 'r') as file:
    reader = csv.reader(file, delimiter='\t')
    for row in reader:
        print(row)

# Handle different dialects
with open('data.csv', 'r') as file:
    dialect = csv.Sniffer().sniff(file.read(1024))
    file.seek(0)
    reader = csv.reader(file, dialect)
    for row in reader:
        print(row)

Writing CSV Files

Python (pandas)

# Basic write
df.to_csv('output.csv', index=False)

# Custom delimiter
df.to_csv('output.tsv', sep='\t', index=False)

# Specify encoding
df.to_csv('output.csv', encoding='utf-8-sig', index=False)

# Write only specific columns
df[['name', 'email']].to_csv('output.csv', index=False)

# Append to existing file
df.to_csv('output.csv', mode='a', header=False, index=False)

# Quote all fields
df.to_csv('output.csv', quoting=csv.QUOTE_ALL, index=False)

JavaScript (csv-writer)

const createCsvWriter = require('csv-writer').createObjectCsvWriter;

const csvWriter = createCsvWriter({
  path: 'output.csv',
  header: [
    {id: 'name', title: 'Name'},
    {id: 'email', title: 'Email'},
    {id: 'age', title: 'Age'}
  ]
});

const records = [
  {name: 'John Doe', email: 'john@example.com', age: 30},
  {name: 'Jane Smith', email: 'jane@example.com', age: 25}
];

csvWriter.writeRecords(records)
  .then(() => console.log('CSV file written successfully'));

Data Transformation Patterns

Filtering Rows

Python (pandas)

# Single condition
filtered = df[df['age'] > 30]

# Multiple conditions (AND)
filtered = df[(df['age'] > 30) & (df['country'] == 'USA')]

# Multiple conditions (OR)
filtered = df[(df['age'] < 18) | (df['age'] > 65)]

# String operations
filtered = df[df['email'].str.contains('@gmail.com')]
filtered = df[df['name'].str.startswith('John')]

# Is in list
filtered = df[df['country'].isin(['USA', 'Canada', 'Mexico'])]

# Not null values
filtered = df[df['email'].notna()]

# Complex conditions
filtered = df.query('age > 30 and country == "USA" and active == True')

JavaScript

// Filter with arrow function
const filtered = data.filter(row => row.age > 30);

// Multiple conditions
const filtered = data.filter(row =>
  row.age > 30 && row.country === 'USA'
);

// String operations
const filtered = data.filter(row =>
  row.email.includes('@gmail.com')
);

// Complex filtering
const filtered = data.filter(row => {
  const age = parseInt(row.age);
  return age >= 18 && age <= 65 && row.active === 'true';
});

Selecting Columns

Python (pandas)

# Select single column
names = df['name']

# Select multiple columns
subset = df[['name', 'email', 'age']]

# Select by column type
numeric_cols = df.select_dtypes(include=['int64', 'float64'])
string_cols = df.select_dtypes(include=['object'])

# Select columns matching pattern
email_cols = df.filter(regex='.*email.*')

# Drop columns
df_without = df.drop(['temporary', 'unused'], axis=1)

# Rename columns
df_renamed = df.rename(columns={
    'old_name': 'new_name',
    'email_address': 'email'
})

JavaScript

// Map to select columns
const subset = data.map(row => ({
  name: row.name,
  email: row.email,
  age: row.age
}));

// Destructuring
const subset = data.map(({name, email, age}) => ({name, email, age}));

// Dynamic column selection
const columns = ['name', 'email', 'age'];
const subset = data.map(row =>
  Object.fromEntries(
    columns.map(col => [col, row[col]])
  )
);

Sorting Data

Python (pandas)

# Sort by single column
sorted_df = df.sort_values('age')

# Sort descending
sorted_df = df.sort_values('age', ascending=False)

# Sort by multiple columns
sorted_df = df.sort_values(['country', 'age'], ascending=[True, False])

# Sort by index
sorted_df = df.sort_index()

JavaScript

// Sort by single field
const sorted = data.sort((a, b) => a.age - b.age);

// Sort descending
const sorted = data.sort((a, b) => b.age - a.age);

// Sort by string
const sorted = data.sort((a, b) => a.name.localeCompare(b.name));

// Sort by multiple fields
const sorted = data.sort((a, b) => {
  if (a.country !== b.country) {
    return a.country.localeCompare(b.country);
  }
  return b.age - a.age;
});

Grouping and Aggregation

Python (pandas)

# Group by single column
grouped = df.groupby('country')

# Count by group
counts = df.groupby('country').size()

# Multiple aggregations
stats = df.groupby('country').agg({
    'age': ['mean', 'min', 'max'],
    'salary': ['sum', 'mean'],
    'user_id': 'count'
})

# Group by multiple columns
grouped = df.groupby(['country', 'city']).agg({
    'revenue': 'sum',
    'user_id': 'count'
})

# Custom aggregation
df.groupby('country').apply(lambda x: x['salary'].max() - x['salary'].min())

# Pivot table
pivot = df.pivot_table(
    values='revenue',
    index='country',
    columns='year',
    aggfunc='sum',
    fill_value=0
)

JavaScript (lodash)

const _ = require('lodash');

// Group by field
const grouped = _.groupBy(data, 'country');

// Count by group
const counts = _.mapValues(
  _.groupBy(data, 'country'),
  group => group.length
);

// Sum by group
const sums = _.mapValues(
  _.groupBy(data, 'country'),
  group => _.sumBy(group, row => parseFloat(row.salary))
);

// Multiple aggregations
const stats = Object.entries(_.groupBy(data, 'country')).map(([country, rows]) => ({
  country,
  count: rows.length,
  avgAge: _.meanBy(rows, row => parseInt(row.age)),
  totalSalary: _.sumBy(rows, row => parseFloat(row.salary))
}));

Merging CSV Files

Python (pandas)

# Concatenate vertically (stack rows)
df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')
combined = pd.concat([df1, df2], ignore_index=True)

# Join (SQL-like merge)
users = pd.read_csv('users.csv')
orders = pd.read_csv('orders.csv')

# Inner join
merged = pd.merge(users, orders, on='user_id', how='inner')

# Left join
merged = pd.merge(users, orders, on='user_id', how='left')

# Multiple keys
merged = pd.merge(
    users, orders,
    left_on='id',
    right_on='user_id',
    how='left'
)

# Merge with different column names
merged = pd.merge(
    users, orders,
    left_on='user_id',
    right_on='customer_id',
    how='inner'
)

JavaScript

// Concatenate arrays
const file1 = parseCSV('file1.csv');
const file2 = parseCSV('file2.csv');
const combined = [...file1, ...file2];

// Join arrays (like SQL)
function leftJoin(left, right, leftKey, rightKey) {
  return left.map(leftRow => {
    const rightRow = right.find(r => r[rightKey] === leftRow[leftKey]);
    return {...leftRow, ...rightRow};
  });
}

const merged = leftJoin(users, orders, 'id', 'user_id');

Data Cleaning Operations

Remove Duplicates

Python (pandas)

# Remove duplicate rows
df_unique = df.drop_duplicates()

# Based on specific columns
df_unique = df.drop_duplicates(subset=['email'])

# Keep first or last occurrence
df_unique = df.drop_duplicates(subset=['email'], keep='first')
df_unique = df.drop_duplicates(subset=['email'], keep='last')

# Identify duplicates
duplicates = df[df.duplicated()]
duplicate_emails = df[df.duplicated(subset=['email'])]

Handle Missing Values

Python (pandas)

# Check for missing values
missing_count = df.isnull().sum()
missing_percent = (df.isnull().sum() / len(df)) * 100

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

# Drop rows where specific column is missing
df_clean = df.dropna(subset=['email'])

# Drop columns with too many missing values
df_clean = df.dropna(axis=1, thresh=len(df)*0.7)

# Fill missing values
df_filled = df.fillna(0)
df_filled = df.fillna({'age': 0, 'country': 'Unknown'})

# Forward fill
df_filled = df.fillna(method='ffill')

# Fill with mean/median
df['age'].fillna(df['age'].mean(), inplace=True)
df['age'].fillna(df['age'].median(), inplace=True)

# Interpolate
df['value'].interpolate(method='linear', inplace=True)

JavaScript

// Filter out rows with missing values
const cleaned = data.filter(row =>
  row.email && row.name && row.age
);

// Fill missing values
const filled = data.map(row => ({
  ...row,
  age: row.age || 0,
  country: row.country || 'Unknown'
}));

Data Validation

Python (pandas)

# Validate email format
import re
email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
df['email_valid'] = df['email'].str.match(email_pattern)

# Validate age range
df['age_valid'] = df['age'].between(0, 120)

# Validate required fields
df['valid'] = df[['name', 'email', 'age']].notna().all(axis=1)

# Check data types
def validate_types(df):
    errors = []

    # Check numeric columns
    for col in ['age', 'salary', 'score']:
        if col in df.columns:
            if not pd.api.types.is_numeric_dtype(df[col]):
                errors.append(f"{col} should be numeric")

    # Check date columns
    for col in ['created_at', 'updated_at']:
        if col in df.columns:
            try:
                pd.to_datetime(df[col])
            except:
                errors.append(f"{col} has invalid dates")

    return errors

# Remove invalid rows
df_valid = df[df['email_valid'] & df['age_valid']]

Data Normalization

Python (pandas)

# Trim whitespace
df['name'] = df['name'].str.strip()
df['email'] = df['email'].str.strip()

# Convert to lowercase
df['email'] = df['email'].str.lower()

# Standardize phone numbers
df['phone'] = df['phone'].str.replace(r'[^0-9]', '', regex=True)

# Standardize dates
df['created_at'] = pd.to_datetime(df['created_at'])

# Standardize country names
country_mapping = {
    'USA': 'United States',
    'US': 'United States',
    'United States of America': 'United States',
    'UK': 'United Kingdom'
}
df['country'] = df['country'].replace(country_mapping)

# Convert data types
df['age'] = pd.to_numeric(df['age'], errors='coerce')
df['active'] = df['active'].astype(bool)
df['score'] = df['score'].astype(float)

Data Analysis Operations

Statistical Summary

Python (pandas)

# Basic statistics
print(df.describe())

# Statistics for all columns (including non-numeric)
print(df.describe(include='all'))

# Specific statistics
print(f"Mean age: {df['age'].mean()}")
print(f"Median age: {df['age'].median()}")
print(f"Std dev: {df['age'].std()}")
print(f"Min: {df['age'].min()}")
print(f"Max: {df['age'].max()}")

# Count values
print(df['country'].value_counts())

# Percentage distribution
print(df['country'].value_counts(normalize=True) * 100)

# Cross-tabulation
cross_tab = pd.crosstab(df['country'], df['active'])

# Correlation matrix
correlation = df[['age', 'salary', 'score']].corr()

Data Profiling

Python (pandas)

def profile_dataframe(df):
    """Generate comprehensive data profile"""

    profile = {
        'shape': df.shape,
        'columns': list(df.columns),
        'dtypes': df.dtypes.to_dict(),
        'memory_usage': df.memory_usage(deep=True).sum() / 1024**2,  # MB
        'missing_values': df.isnull().sum().to_dict(),
        'missing_percent': (df.isnull().sum() / len(df) * 100).to_dict(),
        'duplicates': df.duplicated().sum(),
        'numeric_summary': df.describe().to_dict(),
        'unique_counts': df.nunique().to_dict()
    }

    # Column-specific analysis
    for col in df.columns:
        profile[f'{col}_sample'] = df[col].head(5).tolist()

        if df[col].dtype == 'object':
            profile[f'{col}_top_values'] = df[col].value_counts().head(10).to_dict()

        if pd.api.types.is_numeric_dtype(df[col]):
            profile[f'{col}_outliers'] = detect_outliers(df[col])

    return profile

def detect_outliers(series):
    """Detect outliers using IQR method"""
    Q1 = series.quantile(0.25)
    Q3 = series.quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR

    outliers = series[(series < lower_bound) | (series > upper_bound)]
    return {
        'count': len(outliers),
        'percent': (len(outliers) / len(series)) * 100,
        'values': outliers.tolist()
    }

Generate Report

def generate_csv_report(df, filename='report.md'):
    """Generate comprehensive analysis report"""

    report = f"""# CSV Analysis Report
Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

## Dataset Overview
- **Rows**: {len(df):,}
- **Columns**: {len(df.columns)}
- **Memory Usage**: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB
- **Duplicates**: {df.duplicated().sum():,}

## Column Summary

| Column | Type | Non-Null | Unique | Missing % |
|--------|------|----------|--------|-----------|
"""

    for col in df.columns:
        dtype = str(df[col].dtype)
        non_null = df[col].count()
        unique = df[col].nunique()
        missing_pct = (df[col].isnull().sum() / len(df)) * 100

        report += f"| {col} | {dtype} | {non_null:,} | {unique:,} | {missing_pct:.1f}% |\n"

    report += "\n## Numeric Columns Statistics\n\n"
    report += df.describe().to_markdown()

    report += "\n\n## Data Quality Issues\n\n"

    # Missing values
    missing = df.isnull().sum()
    if missing.sum() > 0:
        report += "### Missing Values\n"
        for col, count in missing[missing > 0].items():
            pct = (count / len(df)) * 100
            report += f"- **{col}**: {count:,} ({pct:.1f}%)\n"

    # Duplicates
    if df.duplicated().sum() > 0:
        report += f"\n### Duplicates\n"
        report += f"- Found {df.duplicated().sum():,} duplicate rows\n"

    # Write report
    with open(filename, 'w') as f:
        f.write(report)

    print(f"Report generated: {filename}")

Advanced Operations

Splitting Large CSV Files

def split_csv(input_file, rows_per_file=10000):
    """Split large CSV into smaller chunks"""

    chunk_num = 0

    for chunk in pd.read_csv(input_file, chunksize=rows_per_file):
        output_file = f"{input_file.rsplit('.', 1)[0]}_part{chunk_num}.csv"
        chunk.to_csv(output_file, index=False)
        print(f"Created {output_file} with {len(chunk)} rows")
        chunk_num += 1

Pivot and Unpivot

# Pivot (wide format)
pivot = df.pivot_table(
    values='revenue',
    index='product',
    columns='month',
    aggfunc='sum'
)

# Unpivot (long format)
melted = df.melt(
    id_vars=['product', 'category'],
    value_vars=['jan', 'feb', 'mar'],
    var_name='month',
    value_name='revenue'
)

Data Type Conversion

# Convert columns
df['age'] = pd.to_numeric(df['age'], errors='coerce')
df['created_at'] = pd.to_datetime(df['created_at'])
df['active'] = df['active'].astype(bool)

# Parse custom date formats
df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')

# Handle mixed types
df['mixed'] = df['mixed'].astype(str)

Performance Optimization

Reading Large Files Efficiently

# Read in chunks
chunk_size = 10000
chunks = []

for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
    # Process chunk
    processed = chunk[chunk['active'] == True]
    chunks.append(processed)

result = pd.concat(chunks, ignore_index=True)

# Read only needed columns
df = pd.read_csv('large_file.csv', usecols=['name', 'email', 'age'])

# Use appropriate dtypes
df = pd.read_csv('large_file.csv', dtype={
    'id': 'int32',  # instead of int64
    'age': 'int8',  # small integers
    'category': 'category'  # categorical data
})

Writing Large Files

# Write in chunks
chunk_size = 10000

for i in range(0, len(df), chunk_size):
    chunk = df.iloc[i:i+chunk_size]
    mode = 'w' if i == 0 else 'a'
    header = i == 0
    chunk.to_csv('output.csv', mode=mode, header=header, index=False)

Command Line Tools

Using csvkit

# View CSV structure
csvcut -n data.csv

# Filter columns
csvcut -c name,email,age data.csv > subset.csv

# Filter rows
csvgrep -c age -r "^[3-9][0-9]$" data.csv > age_30plus.csv

# Convert to JSON
csvjson data.csv > data.json

# Statistics
csvstat data.csv

# SQL queries on CSV
csvsql --query "SELECT country, COUNT(*) FROM data GROUP BY country" data.csv

Using awk

# Print specific columns
awk -F',' '{print $1, $3}' data.csv

# Filter rows
awk -F',' '$3 > 30' data.csv

# Sum column
awk -F',' '{sum+=$3} END {print sum}' data.csv

Best Practices

1. Always validate data before processing
2. Use appropriate data types to save memory
3. Handle encoding issues early in the process
4. Profile data first to understand structure
5. Use chunks for large files
6. Back up original files before transformations
7. Document transformations for reproducibility
8. Validate output after processing
9. Use version control for CSV processing scripts
10. Test with sample data before processing full datasets

Common Issues and Solutions

Issue: Encoding Errors

# Try different encodings
for encoding in ['utf-8', 'latin-1', 'cp1252', 'iso-8859-1']:
    try:
        df = pd.read_csv('data.csv', encoding=encoding)
        print(f"Success with encoding: {encoding}")
        break
    except UnicodeDecodeError:
        continue

Issue: Delimiter Detection

# Auto-detect delimiter
with open('data.csv', 'r') as file:
    sample = file.read(1024)
    sniffer = csv.Sniffer()
    delimiter = sniffer.sniff(sample).delimiter

df = pd.read_csv('data.csv', delimiter=delimiter)

Issue: Memory Errors

# Use chunking
chunks = []
for chunk in pd.read_csv('large.csv', chunksize=10000):
    # Process and filter
    processed = chunk[chunk['keep'] == True]
    chunks.append(processed)

df = pd.concat(chunks, ignore_index=True)

Notes

• Always inspect CSV structure before processing
• Test transformations on a small sample first
• Consider using databases for very large datasets
• Document column meanings and data types
• Use consistent date and number formats
• Validate data quality regularly
• Keep processing scripts version controlled