Excel sheets with data

Tutorial: Import Data into Excel, and Create a Data Model

Abstract: This is the first tutorial in a series designed to get you acquainted and comfortable using Excel and its built-in data mash-up and analysis features. These tutorials build and refine an Excel workbook from scratch, build a data model, then create amazing interactive reports using Power View. The tutorials are designed to demonstrate Microsoft Business Intelligence features and capabilities in Excel, PivotTables, Power Pivot, and Power View.

Note: This article describes data models in Excel 2013. However, the same data modeling and Power Pivot features introduced in Excel 2013 also apply to Excel 2016.

In these tutorials you learn how to import and explore data in Excel, build and refine a data model using Power Pivot, and create interactive reports with Power View that you can publish, protect, and share.

The tutorials in this series are the following:

Import Data into Excel 2013, and Create a Data Model

In this tutorial, you start with a blank Excel workbook.

The sections in this tutorial are the following:

At the end of this tutorial is a quiz you can take to test your learning.

This tutorial series uses data describing Olympic Medals, hosting countries, and various Olympic sporting events. We suggest you go through each tutorial in order. Also, tutorials use Excel 2013 with Power Pivot enabled. For more information on Excel 2013, click here. For guidance on enabling Power Pivot, click here.

Import data from a database

We start this tutorial with a blank workbook. The goal in this section is to connect to an external data source, and import that data into Excel for further analysis.

Let’s start by downloading some data from the Internet. The data describes Olympic Medals, and is a Microsoft Access database.

Click the following links to download files we use during this tutorial series. Download each of the four files to a location that’s easily accessible, such as Downloads or My Documents, or to a new folder you create:
> OlympicMedals.accdb Access database
> OlympicSports.xlsx Excel workbook
> Population.xlsx Excel workbook
> DiscImage_table.xlsx Excel workbook

In Excel 2013, open a blank workbook.

Click DATA > Get External Data > From Access. The ribbon adjusts dynamically based on the width of your workbook, so the commands on your ribbon may look slightly different from the following screens. The first screen shows the ribbon when a workbook is wide, the second image shows a workbook that has been resized to take up only a portion of the screen.

Select the OlympicMedals.accdb file you downloaded and click Open. The following Select Table window appears, displaying the tables found in the database. Tables in a database are similar to worksheets or tables in Excel. Check the Enable selection of multiple tables box, and select all the tables. Then click OK.

The Import Data window appears.

Note: Notice the checkbox at the bottom of the window that allows you to Add this data to the Data Model, shown in the following screen. A Data Model is created automatically when you import or work with two or more tables simultaneously. A Data Model integrates the tables, enabling extensive analysis using PivotTables, Power Pivot, and Power View. When you import tables from a database, the existing database relationships between those tables is used to create the Data Model in Excel. The Data Model is transparent in Excel, but you can view and modify it directly using the Power Pivot add-in. The Data Model is discussed in more detail later in this tutorial.

Select the PivotTable Report option, which imports the tables into Excel and prepares a PivotTable for analyzing the imported tables, and click OK.

Once the data is imported, a PivotTable is created using the imported tables.

With the data imported into Excel, and the Data Model automatically created, you’re ready to explore the data.

Explore data using a PivotTable

Exploring imported data is easy using a PivotTable. In a PivotTable, you drag fields (similar to columns in Excel) from tables (like the tables you just imported from the Access database) into different areas of the PivotTable to adjust how it presents your data. A PivotTable has four areas: FILTERS, COLUMNS, ROWS, and VALUES.

It might take some experimenting to determine which area a field should be dragged to. You can drag as many or few fields from your tables as you like, until the PivotTable presents your data how you want to see it. Feel free to explore by dragging fields into different areas of the PivotTable; the underlying data is not affected when you arrange fields in a PivotTable.

Let’s explore the Olympic Medals data in the PivotTable, starting with Olympic medalists organized by discipline, medal type, and the athlete’s country or region.

In PivotTable Fields, expand the Medals table by clicking the arrow beside it. Find the NOC_CountryRegion field in the expanded Medals table, and drag it to the COLUMNS area. NOC stands for National Olympic Committees, which is the organizational unit for a country or region.

Next, from the Disciplines table, drag Discipline to the ROWS area.

Let’s filter Disciplines to display only five sports: Archery, Diving, Fencing, Figure Skating, and Speed Skating. You can do this from within the PivotTable Fields area, or from the Row Labels filter in the PivotTable itself.

Click anywhere in the PivotTable to ensure the Excel PivotTable is selected. In the PivotTable Fields list, where the Disciplines table is expanded, hover over its Discipline field and a dropdown arrow appears to the right of the field. Click the dropdown, click (Select All)to remove all selections, then scroll down and select Archery, Diving, Fencing, Figure Skating, and Speed Skating. Click OK.

Or, in the Row Labels section of the PivotTable, click the dropdown next to Row Labels in the PivotTable, click (Select All) to remove all selections, then scroll down and select Archery, Diving, Fencing, Figure Skating, and Speed Skating. Click OK.

In PivotTable Fields, from the Medals table, drag Medal to the VALUES area. Since Values must be numeric, Excel automatically changes Medal to Count of Medal.

From the Medals table, select Medal again and drag it into the FILTERS area.

Let’s filter the PivotTable to display only those countries or regions with more than 90 total medals. Here’s how.

In the PivotTable, click the dropdown to the right of Column Labels.

Select Value Filters and select Greater Than….

Type 90 in the last field (on the right). Click OK.

Your PivotTable looks like the following screen.

With little effort, you now have a basic PivotTable that includes fields from three different tables. What made this task so simple were the pre-existing relationships among the tables. Because table relationships existed in the source database, and because you imported all the tables in a single operation, Excel could recreate those table relationships in its Data Model.

But what if your data originates from different sources, or is imported at a later time? Typically, you can create relationships with new data based on matching columns. In the next step, you import additional tables, and learn how to create new relationships.

Import data from a spreadsheet

Now let’s import data from another source, this time from an existing workbook, then specify the relationships between our existing data and the new data. Relationships let you analyze collections of data in Excel, and create interesting and immersive visualizations from the data you import.

Let’s start by creating a blank worksheet, then import data from an Excel workbook.

Insert a new Excel worksheet, and name it Sports.

Browse to the folder that contains the downloaded sample data files, and open OlympicSports.xlsx.

Select and copy the data in Sheet1. If you select a cell with data, such as cell A1, you can press Ctrl + A to select all adjacent data. Close the OlympicSports.xlsx workbook.

On the Sports worksheet, place your cursor in cell A1 and paste the data.

With the data still highlighted, press Ctrl + T to format the data as a table. You can also format the data as a table from the ribbon by selecting HOME > Format as Table. Since the data has headers, select My table has headers in the Create Table window that appears, as shown here.

Formatting the data as a table has many advantages. You can assign a name to a table, which makes it easy to identify. You can also establish relationships between tables, enabling exploration and analysis in PivotTables, Power Pivot, and Power View.

Name the table. In TABLE TOOLS > DESIGN > Properties, locate the Table Name field and type Sports. The workbook looks like the following screen.

Save the workbook.

Import data using copy and paste

Now that we’ve imported data from an Excel workbook, let’s import data from a table we find on a web page, or any other source from which we can copy and paste into Excel. In the following steps, you add the Olympic host cities from a table.

Insert a new Excel worksheet, and name it Hosts.

Select and copy the following table, including the table headers.

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Download Sample Excel File with Data for Analysis

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Sample excel sheet with employee data

This excel sheet contains 500 test employee data.

Sample sales data excel

Sample excel sheet with student data

Excel sample data for pivot tables

More Sample Files for Free Download

As you all know that Microsoft Office is a product of Microsoft company but do you know that in old version 2003 of Microsoft office we can not open the file of the new version 2007 while in the new version old version We can open files easily.

Difference Between .xls file and .xlsx file

.xlsx file – This is a file extension of Microsoft excel spreadsheet 2007 and its foundation is on XML and the files saved in it are in the form of text files with the help of XML.
With this spreadsheet, we can use the .xls files of the old version excel 2003. It does not support macros.

.xls file – This Microsoft excel spreadsheet has a file extension of all the versions that have come up to 2003 and its foundation is on binary data. With the help of this spreadsheet, files with .xls can be used, but new versions of the word 2007 cannot use files with .xlsx. It supports macros.

In simple words, it can be said that when Microsoft company launched excel 2003, when we save the file, in that by default .xls extension which we can open in any version of Microsoft excel.

But Microsoft launched excel 2007 after 2003, every file we save in it is saved in by default .xlsx extension, due to which we cannot open it in older versions. If you have to open the .xlsx file in an earlier version of Excel, then first you have to download Microsoft Compatibility Software for this, then you will be able to open it.

If your computer or laptop has any version above Microsoft office excel 2007 then you can save your file in any extension for which just.

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Excel Sample Data

This page has Microsoft Excel sample datasets that you can download, to use for testing, Excel training and demos, and other learning activities. There is a table with office supply sales sample data, to copy and paste into your Excel workbook. Or download one of the many sample data files in Excel format. There are dataset examples for food sales, property insurance data, work orders, workplace safety, athletes, and other Excel datasets.

Note: You many not resell this sample data, in whole or in part, or include it in paid products.

Why Use Sample Data in Excel?

This page has Microsoft Excel sample datasets that you can use for testing, Excel training and demos, and other learning activities. The sample file contents are described in the sections below, so you can decide which dataset will be the best match for what you need.

You can see a screen shot of each dataset, and a list of the columns it contains, with a brief description. Then, to get started, you can download sample Excel file with data for your training sessions.

Why do people download my sample data workbooks, to use in Excel?

Here are a few of the reasons that people have shared with me, as to why they like using these datasets, with dummy data for Excel:

• Quick way to get interesting data for Excel training class exercises
• No privacy concerns about using real data from your employer or your client’s company
• Useful for testing macros and manual processes — you might discover problems that didn’t occur with original test data
• Handy data to share when helping with Excel forum questions.
  • Send people to this page, so they can download the sample data, and recreate their problem or error there.
  • It’s easier to help with troubleshooting when you both have the same data
• Easy to prepare class demos for sum and count formulas, lookup functions, pivot tables, summary reports, and other Excel skills training
  • Tip: Check the Key Excel Skills page, for training topic ideas.

Sample Data Files

In the sections below, there are brief descriptions of the sample data files.

Each section has a download link, or a link to an instruction page where you can get details about the data, and download the sample file.

1) Office Supply Sales Data

The first set of sample data on this page is sales data for an imaginary office supply company, and each row represents one sales order. There are formulas in one column — Total.

Each row in the Excel dummy data shows the following fields:

• Order Date: first column shows when the order was placed,
• Region: geographical area in which the sale was made
• Rep: sales representative’s name
• Item: name of the item sold
• Units: number of units sold
• UnitCost: cost of one unit
• Total: total cost of the order — Units x UnitCost

To get this sample data:

— Copy and paste the data from the Office Supply Sales Table, further down on this page

— OR, click this link to download the Excel file: Office Supply Sales sample data workbook.
——The zipped file is in xlsx format, and does not contain any macros.

2) Food Sales Data

This sample file has food sales data, from a fictional food company.

• There are 9 columns of data, including 1 column with a calculation.
• There are 244 rows of data in the food sales table.

For details on this data, and to download the zipped Excel file, go to the Food Sales Sample Data page.

3) Workplace Safety Data

This sample file has fake workplace safety report data.

• There are 14 columns of data, including 3 columns with a calculation.
• There are 514 rows of data in the workplace safety report table.

Each row shows the following fields:

• Date: Date of safety incident
• Injury Location: location on body where injury occurred
• Gender: employee gender
• Age Group: employee age group (18-24, 25-34, 35-49, 50+)
• Incident Type: Type of safety incident, e.g. Burn, Fall, Vehicle
• Days Lost: number of days employee off work
• Plant: manufacturing location were incident occurred
• Report Type: Type of safety report filed — First Aid, Lost Time, Medical Claim, Near Miss
• Shift: work shift when incident occurred — Afternoon, Day, Night
• Department: work department where incident occurred
• Incident Cost: total cost of safety incident
• WkDay: 3-letter code for day of the week (calculation, based on Date)
• Month: month number (calculation, based on Date)
• Year: year number (calculation, based on Date)

Workplace Safety: Click here to get the workplace safety data file. The zipped Excel file is in xlsx format, and does not contain any macros.

Note: This screen shot shows the first 6 columns in the Workplace Safety dataset table. There are 8 additional columns in the dataset, as described above.

4) Work Orders Data

This sample file has work orders data for an imaginary service company.

• There are 22 columns of data, including 9 columns with a calculation.
• There are 1000 rows of data in the work orders table.

Each row shows these 13 manual entry fields:

• WO: unique work order ID
• District: geographic area where work will be done
• LeadTech: head technician at the work location
• Service: type of work to be done — Assess, Deliver, Install, Repair, Replace
• Rush: is this a rush job? Yes or blank
• ReqDate: date the work order was entered in system
• WokDate: date the work was completed
• Techs: number of technicians required
• WtyLbr: is labor under warranty? Yes or blank
• WtyParts: are parts under warranty? Yes or blank
• LbrHrs: for completed work orders — number of hours labour
• PartsCost: cost of parts required to complete the work
• Payment: type of payment customer will use — Account, C.O.D, Credit, P.O., Warranty

Each row shows these 9 calculated fields:

• Wait: WorkDate — ReqDate
• LbrRate: hourly rate lookup, based on number of technicians
• LbrCost: LbrRate x LbrHrs
• LbrFee: labor cost, if not under warranty
• PartsFee: parts cost, if not under warranty
• TotalCost: LbrCost + PartsCost
• TotalFee: LbrFee +PartsFee
• ReqDay: 3-letter weekday name, based on ReqDate
• WorkDay: 3-letter weekday name, based on WorkDate

Work Orders: Click here to get the work orders data file. The zipped Excel file is in xlsx format, and does not contain any macros.

Note: This screen shot shows the first 8 columns in the Work Orders dataset table. There are 14 additional columns in the dataset, as described above.

5) Hockey Players Data

This sample file has data from the 2018 Olympic Hockey teams, from Canada and the USA.

• There are 15 columns of data, including 4 columns with calculations.
• There are 96 rows of data in the hockey player table.

For details on this data, and to download the sample file, go to the hockey player data analysis page.

5a) Hockey Player Data Analysis

This sample file has the same data as the file above — 2018 Olympic Hockey teams, from Canada and the USA. In addition to the data, this file includes several pivot tables that I added for hockey player data analysis.

For details on this data, and to download the sample file, go to the hockey player data analysis page.

6) Winter Athletes Data

This sample Excel spreadsheet file has athlete data from the 2010 Winter Olympics, in Vancouver Canada.

• There are 6 columns of data, with no calculations in the table.
• There are 2606 rows of data in the athletes table.

Each row shows these 6 manual entry fields, with information about the Olympic participants:

• Name: athlete’s full name — LAST First
• Sport: sport(s) athlete will participate in
• Nationality: country athlete competes for
• Age: athlete’s age in years
• Wt kg: athlete’s weight in kilograms
• Ht: athlete’s height in centimetres

Winter Athletes: Click here to get the winter athletes data file. The zipped Excel file is in xlsx format, and does not contain any macros.

Note: This screen shot shows all 6 columns in the Winter Athletes dataset table.

This sample file has data for professional football players in the USA, for two teams, in two different years — 2010 and 2022.

In January 2010, I watched a playoff game between the New Orleans Saints and the Arizona Cardinals. It was interesting that the players’ numbers closely correlated with their physical size. So, I found the player rosters online, and got that data into Excel, for analysis. You can read about it in my Contextures blog post, and see the game summary on the ESPN website.

Now I’ve added data for the 2022 team rosters, so you can compare years, teams, and/or player types!

• Player heights were entered as text values, in feet-inches, e.g. 6-1. In Excel, I used formulas to convert those text values to real numbers.
• Full names were listed, so I used formulas in two columns, to calculate each player’s first and last name
• For 2022 players, I used team rosters as of Aug 5, 2022
  • New Orleans team roster and Arizona team roster

In the Excel workbook, there is a player table with:

• There are 16 columns of data, including 6 columns with a calculation.
• There are 306 rows of data in the players table.

Each row shows these 10 manual entry fields:

• Year: Roster Year
• Team: Team city name
• Name: Player’s full name
• No.: Player number, worn on jersey
• Pos: Player’s position code , e.g. QB (Quarterback)
• Ht: Player’s height in text, feet-inches, e.g. 5-10
• Wt: Player’s weight in pounds
• Age: Player’s age in years
• Exp: Player’s years of experience (or R for rookie)
• College: Name of college that player attended

Each row shows the following 6 calculated fields:

• FirstName: Player’s first (given) name
• LastName: Player’s last (family) name
• Ft: Number of feet in player’s text height
• In: Number of inches in player’s text height
• Inches: Player’s height in total inches
• NumGrp: Number group for player’s jersey number (groups of 10)

Football Players: Click here to get the Football Player data file. The zipped Excel file is in xlsx format, and does not contain any macros.

Note: This screen shot shows the first 9 columns in the Football Player dataset table. There are 7 additional columns in the dataset, as described above.

Food Nutrients Data

This sample Excel spreadsheet file has food nutrition data, based on data found in a Health Canada information booklet.

• There are 8 columns of data, with no calculations in the table.
• There are 1094 rows of data in the Food Nutrients table.

Each row shows these 8 manual entry fields, with information about the food items:

• Category: main food category that food item belongs to
• Food Item: name of food item, and brief description of size
• Measure: serving measurement, such as fraction of whole item, or metric quantity
• Calories: calories based on serving size
• Protein: grams of protein in serving size
• Fat: grams of total fat in serving size
• Carbs: grams of carbohydrate in serving size
• Fibre: grams of fibre in serving size

Suggested Use

Use this data to demonstrate lookup formulas, such as VLOOKUP, XLOOKUP, INDEX and MATCH. Also, show how to create dependent drop down lists, for category and food item. See the Excel Daily Calorie Counter page for an example.

Get Food Nutrients Data File

Food Nutrients: Click here to get the food nutrients data file. The zipped Excel file is in xlsx format, and does not contain any macros.

Note: This screen shot shows all 8 columns in the Food Nutrients dataset table.

9) Insurance Policies

This sample file has fake commercial property insurance policy data.

• There are 10 columns of data, with no calculations.
• There are 500 rows of data in the insurance policy table.
• Names in the header row are one word, with no spaces
• Data is in named Excel table, with data filters in heading row

Each row shows the following fields:

• Policy: Policy number, entered as text
• Expiry: date that policy expires
• Location: location type — Urban or Rural
• State: name of state where property is located
• Region: geographic region where property is located
• InsuredValue: property value
• Construction: Construction type of property, e.g. frame or masonry
• BusinessType: Business use type for property, e.g. farming or retail
• Earth: Is earthquake coverage included? Y or N
• Flood: Is flood coverage included? Y or N

Click here to get the insurance policy data file. The zipped Excel file is in xlsx format, and does not contain any macros.

Download Sample Data

• 1) Office Supply Sales: To get the sample office supply sales data in an Excel file, click this link: Office Supply Sales sample data workbook. The zipped file is in xlsx format, and does not contain any macros.
• 1a) Office Supply Table: In the section below, there is a list with the Office Supply data, that you can copy and paste into your Excel file.

Note: The other sample file download links are in the dataset sections, above. Check each section for its sample file download link.

1a) Office Supply Sales Table

For your convenience, here are the quick steps to copy the data from the table below, and paste into your Excel worksheet:

• Click at the end of the text heading above the table
• Scroll down to the end of the sample data table
• Press Shift, and click at the end of the last number in the table, to select all the data
• On the keyboard, press Ctrl + C to copy the data
• Paste it into an Excel workbook (Ctrl+V), for use in your own tests.

Download: You can also get the sample office supply sales data in an Excel file — click this link: Office Supply Sales sample data workbook. The zipped file is in xlsx format, and does not contain any macros.

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