Pivot Table Basics

Introduction

Are you struggling with Excel's pivot tables, or are you a seasoned pro looking for some advanced tips? Allison Gonzalez, a Microsoft certified trainer at Pragmatic Works, has got you covered. In this blog post, we'll delve into the basics of pivot tables and uncover some valuable tricks that can make your data analysis more efficient.

Understanding the Pivot Table Design Cycle

• Pivot tables are essential in Excel for summarizing, analyzing, exploring, and presenting summary data.
• The pivot table design cycle involves sourcing data, storing it in the pivot cache for quick analysis, and then designing the pivot table layout.

Criteria for a Good Data Set

Key elements for a good data set before creating a pivot table:

• Descriptive header row: Clear column headers for easy identification.

• Consistent data: No blanks, subtotals, or total lines that may disrupt the table.
• No blank rows or columns: Any gaps in data may lead to inaccuracies.
• Individual items in separate rows: Each element should have its own row for accurate counting.

Avoiding Premature Pivot Table Creation

• Prematurely creating tables to mimic the final pivot table appearance can hinder the ability to pivot the data effectively.
• Allison emphasizes the importance of starting with raw, consistent data before designing the pivot table.

Creating a Pivot Table

Allison walks us through creating a pivot table with a well-organized data set:

1. Selecting the data range.
2. Clicking on the "Pivot Table" button in the insert ribbon.
3. Choosing the location for the pivot table (new worksheet or existing worksheet).

Customizing Pivot Table Layout

• Allison demonstrates different layout options for the pivot table setup menu, providing flexibility based on user preferences.
• Moving data into different sections (rows, columns, values) controls the pivot table's appearance and summarization.

Exploring Pivot Table Features

1. Grouping Data:

• Allison groups data by moving the "Category" column into the rows section and the "Group" column into the columns section.
• This allows for efficient grouping and viewing of data.

2. Adding Date Dimension:   

• Introducing the "Date" column into the columns section enables analysis based on months.
• Users can easily observe data trends over time.

3. Hierarchical View:

• Allison creates a hierarchy by adding the "Item Name" below the "Category" in the rows section.
• Users can expand or collapse for detailed or concise views.

4. Refreshing Data:

• Allison emphasizes that pivot table data is not live and needs refreshing.
• Demonstrates two methods: right-click and refresh or use the data tab to refresh all connected data sources.

Conclusion

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