skill-002

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.