skill-002

name	skill-002
description	Advanced tools for creating, modifying, and analyzing pivot tables in Excel, enabling quick data summarization and insights.
license	Proprietary. LICENSE.txt has complete terms

Requirements for Outputs

General Pivot Table Standards

Data Source Integrity

• Ensure that the data source for pivot tables is complete and well-structured to avoid errors.
• Pivot tables should not reference cells that contain errors or are blank.

Naming Conventions

• Use clear and descriptive names for pivot tables and their associated fields to enhance usability.

Pivot Table Creation Techniques

Basic Creation Steps

• Pivot tables should be created directly from well-structured data ranges.
• Example code snippet:

import pandas as pd

def create_pivot_table(df):
    pivot_table = df.pivot_table(values='Sales', index='Product', columns='Region', aggfunc='sum')
    return pivot_table

Advanced Modifications

• Users should be able to modify pivot tables to include calculated fields and filters as needed.
• Example code snippet:

def add_calculated_field(pivot_table):
    pivot_table['Profit'] = pivot_table['Sales'] - pivot_table['Cost']
    return pivot_table

Documentation and Validation Requirements

Metadata Inclusion

• Each pivot table must include metadata specifying its source data and any calculations performed.
• Example: "Pivot Table based on Sales Data from 2023 Q1."

Change Tracking

• Maintain a log of changes made to pivot tables to facilitate auditing and validation.