Introduction to
Excel Pivot Tables
Kevin Yili Hong
Management Information Systems
Temple University
Objectives
• Understand data organization and sources
that are appropriate for use with
PivotTables
• Be able to use basic PivotTable techniques
for data exploration
• Create custom reports using PivotTables
with appropriate formatting
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Scenario
• You just delivered a report of student
credit hours by department
• “Can I see these numbers summarized by
college also?”
• Scenario #1: “Sure, when I get back to
my office, I will insert rows for each
college, write summing formulas, save file
and email it back to you.”
• Scenario #2: “Lets open the file and dragand-drop it in right now.”
3
What is an Excel Pivot Table?
• An interactive worksheet table
– Provides a powerful tool for summarizing large
amounts of tabular data
• Similar to a cross-tabulation table
– A pivot table classifies numeric data in a list
based on other fields in the list
• General purpose:
– Quickly summarize data from a worksheet or
from an external source
– Calculate totals, averages, counts, etc. based on
any numeric fields in your table
– Generate charts from your pivot tables
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Pivot Table Advantages
• Interactive: easily rearrange them by
moving, adding, or deleting fields
• Dynamic: results are automatically
recalculated whenever fields are added or
dropped, or whenever categories are
hidden or displayed
• Easy to update: “refreshable” if the
original worksheet data changes
5
Appropriate Data
• Data arranged in a list:
– Columns represent fields
– Rows represent a record of related data
• First row = column label
• Columns contain one sort of data
– For example, text in one column and numeric
values in a separate column
• Remove subtotals
– You CAN work with subtotals, but use caution
• De-normalized database extracts are great
for pivoting!
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Appropriate Data Example
• Incomplete records:
First
Sandra
Last
Archer
Hamilton Paws
First
Sandra
Sandra
Hamilton
Hamilton
October 2006
Last
Archer
Archer
Paws
Paws
Spelled
the
same
Gender
Semester
F
Fall 1999
Fall 2005
M
Spring 2003
Summer 2005
Gender
F
F
M
M
Semester
Fall 1999
Fall 2005
Spring 2003
Summer 2005
Major
Credit Hours
Statistics
15
Industrial Engineering
6
Philosophy
12
Chemistry
12
Major
Credit Hours
Statistics
15
Industrial Engineering
6
Philosophy
12
Chemistry
12
"Introduction to Excel PivotTables",
Presented by: S.Archer & R.Armacost
University of Central Florida
7
Appropriate Data Example
• Mixed use columns :
Credit
Hours
16
College
Science
Department
Faculty
Statistics
Ima Faculty
YTD Expenses: $4,000
Humanities
History
Hesa Prof
YTD Expenses: $3,500
12
Humanities
Art
Salvador Dali
YTD Expenses: $2,000
24
College
Science
Humanities
Humanities
October 2006
Department
Statistics
History
Art
Faculty
Ima Faculty
Hesa Prof
Salvador Dali
Column
Label
Credit
YTD
Hours
Expenses
16
$4,000
12
$3,500
24
$2,000
"Introduction to Excel PivotTables",
Presented by: S.Archer & R.Armacost
University of Central Florida
8
Appropriate Data Example
• Column label issues:
College
Department
Faculty
Science
Statistics
Ima Faculty
Humanities History
Hesa Prof
Humanities Art
Salvador Dali
College
Department
Faculty
Science
Statistics
Ima Faculty
Humanities History
Hesa Prof
Humanities Art
Salvador Dali
YTD Expenses
2004
2005
$4,000
$5,000
$3,500
$4,500
$2,000
$3,000
YTD
Expense
2004
$4,000
$3,500
$2,000
YTD
Expense
2005
$5,000
$4,500
$3,000
2006
$6,000
$5,500
$0
YTD
Expense
2006
$6,000
$5,500
Student Credit Hours
2004
2005
2006
16
19
22
12
15
18
24
21
0
Student Student Student
Credit
Credit
Credit
Hours
Hours
Hours
2004
2005
2006
16
19
22
12
15
18
24
21
Zero or
Blank?
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Potential Uses
• Ad hoc reporting with “refreshable”
summary table reports
• Data validation and checking
• Web reporting
• Data exploration
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