Skill

skills

CSV & Excel Data Wrangler

Clean, filter, join, pivot, and export CSV/XLSX data reliably with reproducible steps.

JSONbored

October 15, 2025

csv

xlsx

data-cleaning

pandas

python

Skill Content

The main content for this skill.

csv-excel-data-wrangler.txt

"Clean this customer export:
1. Remove duplicate emails (keep most recent)
2. Standardize phone numbers to (XXX) XXX-XXXX format
3. Fill missing company names with 'Unknown'
4. Split full_name into first_name and last_name
5. Export as customers_clean.xlsx"

Skill Content (2)

Additional code example for this skill.

csv-excel-data-wrangler.txt

"Analyze this sales data:
1. Calculate total revenue by product category
2. Identify top 10 customers by revenue
3. Show month-over-month growth
4. Create a pivot table: rows=salesperson, columns=month, values=revenue
5. Export summary as sales_report.xlsx with formatted numbers"

Skill Content (3)

Additional code example for this skill.

csv-excel-data-wrangler.txt

"Validate this CSV:
1. Check for duplicate IDs
2. Identify rows with missing required fields (name, email, phone)
3. Flag invalid email formats
4. Report data quality issues
5. Export clean rows and error rows separately"

Skill Content (4)

Additional code example for this skill.

csv-excel-data-wrangler.txt

"Combine all CSV files I upload into one master file:
1. Ensure columns match (add missing ones)
2. Add a 'source_file' column
3. Remove duplicates across all files
4. Sort by date column
5. Export as consolidated_data.csv"

Features

Key capabilities and functionality

Import/export with explicit schema control
Deduplicate and null-safe transformations
Join/merge/pivot with predictable results
Encoding-aware IO with UTF-8/UTF-8-SIG handling
Parquet round-trips for performance

Use Cases

Common scenarios and applications

Clean messy CRM exports
Join sales and marketing datasets
Generate analyst-ready summary tables

Requirements

Prerequisites and dependencies

Python 3.11+
pandas
openpyxl
pyarrow (optional for Parquet)

Installation

Setup instructions and requirements

Claude Code Setup

Step 1: Run command

claude code setup-1.sh

bash

pip install pandas openpyxl

Verify versions: pandas >= 2.0

Claude Desktop Setup

Install Python 3.11+

Step 2: Run command

claude desktop setup-2.sh

bash

pip install pandas openpyxl pyarrow

Troubleshooting

Common issues and solutions

Weird characters or BOM appearing in first column name
Use encoding='utf-8-sig' when reading CSV to strip byte order mark, or manually rename columns after load with df.columns.
MemoryError when loading large CSV or Excel files
Use chunksize parameter in read_csv to process incrementally, or convert to Parquet format first for more efficient handling.
Excel file opens but all values are NaN or None
Install openpyxl engine with pip install openpyxl, then use pd.read_excel('file.xlsx', engine='openpyxl').
Date columns imported as strings instead of datetime objects
Use parse_dates parameter: pd.read_csv('file.csv', parse_dates=['date_column']) or convert post-load with pd.to_datetime().
DtypeWarning about mixed types in columns when reading CSV
Specify dtype explicitly with dtype={'column': str} or use dtype=str for all, then convert types after inspection.

Usage Examples

Practical code examples demonstrating common use cases and implementation patterns

Load, dedupe, and export

load-dedupe-and-export.py

python

import pandas as pd

customers = pd.read_csv('customers.csv', dtype=str)
orders = pd.read_excel('orders.xlsx')

# Normalize and dedupe
customers['email'] = customers['email'].str.strip().str.lower()
customers = customers.drop_duplicates(subset=['email'])

# Join and summarize
df = orders.merge(customers, on='customer_id', how='left')
sales_by_region = df.groupby('region', dropna=False)['total'].sum().reset_index()

sales_by_region.to_excel('sales_by_region.xlsx', index=False)

Explicit types and safe parsing

explicit-types-and-safe-parsing.py

python

import pandas as pd

dtypes = {
  'id': 'Int64',
  'price': 'float64',
  'created_at': 'string'
}
df = pd.read_csv('input.csv', dtype=dtypes, encoding='utf-8-sig')

# Coerce dates after load
df['created_at'] = pd.to_datetime(df['created_at'], errors='coerce', utc=True)

df.to_parquet('output.parquet')

Skill

skills

CSV & Excel Data Wrangler

Clean, filter, join, pivot, and export CSV/XLSX data reliably with reproducible steps.

JSONbored

October 15, 2025

25 views

1 used

csv

xlsx

data-cleaning

pandas

python

JSONbored

October 15, 2025

csv

xlsx

data-cleaning

pandas

python

Skill Content

The main content for this skill.

csv-excel-data-wrangler.txt

"Clean this customer export:
1. Remove duplicate emails (keep most recent)
2. Standardize phone numbers to (XXX) XXX-XXXX format
3. Fill missing company names with 'Unknown'
4. Split full_name into first_name and last_name
5. Export as customers_clean.xlsx"

Skill Content (2)

Additional code example for this skill.

csv-excel-data-wrangler.txt

"Analyze this sales data:
1. Calculate total revenue by product category
2. Identify top 10 customers by revenue
3. Show month-over-month growth
4. Create a pivot table: rows=salesperson, columns=month, values=revenue
5. Export summary as sales_report.xlsx with formatted numbers"

Skill Content (3)

Additional code example for this skill.

csv-excel-data-wrangler.txt

"Validate this CSV:
1. Check for duplicate IDs
2. Identify rows with missing required fields (name, email, phone)
3. Flag invalid email formats
4. Report data quality issues
5. Export clean rows and error rows separately"

Skill Content (4)

Additional code example for this skill.

csv-excel-data-wrangler.txt

"Combine all CSV files I upload into one master file:
1. Ensure columns match (add missing ones)
2. Add a 'source_file' column
3. Remove duplicates across all files
4. Sort by date column
5. Export as consolidated_data.csv"

Features

Key capabilities and functionality

Import/export with explicit schema control
Deduplicate and null-safe transformations
Join/merge/pivot with predictable results
Encoding-aware IO with UTF-8/UTF-8-SIG handling
Parquet round-trips for performance

Use Cases

Common scenarios and applications

Clean messy CRM exports
Join sales and marketing datasets
Generate analyst-ready summary tables

Requirements

Prerequisites and dependencies

Python 3.11+
pandas
openpyxl
pyarrow (optional for Parquet)

Installation

Setup instructions and requirements

Claude Code Setup

Step 1: Run command

claude code setup-1.sh

bash

pip install pandas openpyxl

Verify versions: pandas >= 2.0

Claude Desktop Setup

Install Python 3.11+

Step 2: Run command

claude desktop setup-2.sh

bash

pip install pandas openpyxl pyarrow

Troubleshooting

Common issues and solutions

Weird characters or BOM appearing in first column name
Use encoding='utf-8-sig' when reading CSV to strip byte order mark, or manually rename columns after load with df.columns.
MemoryError when loading large CSV or Excel files
Use chunksize parameter in read_csv to process incrementally, or convert to Parquet format first for more efficient handling.
Excel file opens but all values are NaN or None
Install openpyxl engine with pip install openpyxl, then use pd.read_excel('file.xlsx', engine='openpyxl').
Date columns imported as strings instead of datetime objects
Use parse_dates parameter: pd.read_csv('file.csv', parse_dates=['date_column']) or convert post-load with pd.to_datetime().
DtypeWarning about mixed types in columns when reading CSV
Specify dtype explicitly with dtype={'column': str} or use dtype=str for all, then convert types after inspection.

Usage Examples

Practical code examples demonstrating common use cases and implementation patterns

Load, dedupe, and export

load-dedupe-and-export.py

python

import pandas as pd

customers = pd.read_csv('customers.csv', dtype=str)
orders = pd.read_excel('orders.xlsx')

# Normalize and dedupe
customers['email'] = customers['email'].str.strip().str.lower()
customers = customers.drop_duplicates(subset=['email'])

# Join and summarize
df = orders.merge(customers, on='customer_id', how='left')
sales_by_region = df.groupby('region', dropna=False)['total'].sum().reset_index()

sales_by_region.to_excel('sales_by_region.xlsx', index=False)

Explicit types and safe parsing

explicit-types-and-safe-parsing.py

python

import pandas as pd

dtypes = {
  'id': 'Int64',
  'price': 'float64',
  'created_at': 'string'
}
df = pd.read_csv('input.csv', dtype=dtypes, encoding='utf-8-sig')

# Coerce dates after load
df['created_at'] = pd.to_datetime(df['created_at'], errors='coerce', utc=True)

df.to_parquet('output.parquet')

Resources

View on GitHub Documentation

Skill Details

Source

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View on GitHub Documentation

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Growing community of AI engineers actively building with Claude

Active community1,700/month

Growth rateMonth 2

Specialized AI talent pool

30-day featured visibility

Early-stage pricing advantage

View Pricing & Post Job

Live in 5 minutes • Growing community

CSV & Excel Data Wrangler

Claude Code Setup

Claude Desktop Setup

Load, dedupe, and export

Explicit types and safe parsing

Reviews (0)

CSV & Excel Data Wrangler

Claude Code Setup

Claude Desktop Setup

Load, dedupe, and export

Explicit types and safe parsing

Reviews (0)

Category

Source

DOCX Report Generator

CLI Data Viz Quickstart

FastAPI Python Backend Expert

Hire Claude Developers