What is a Histogram in Tableau?

A histogram is one of the most fundamental charts for understanding your data, but it’s often confused with its simpler cousin, the bar chart. While they look similar, a histogram reveals the underlying shape and distribution of your continuous data, helping you see patterns you might otherwise miss. This article will walk you through exactly what a histogram is, why it's so useful in Tableau, and how to create and customize one step-by-step.

What is a Histogram, Really? A Simple Explanation

At its core, a histogram is a graphical representation of the frequency distribution of a set of continuous, numerical data. That sounds a bit academic, so let’s break it down with a simple example.

Imagine you run an e-commerce store and you want to understand the prices of all the items purchased last month. You have a list of 1,000 transaction values ranging from $5 to $500. A histogram organizes this data into groups, or "bins," to see how many transactions fall into each price range.

For example, you might create bins for every $50 increment:

• $0 - $50
• $51 - $100
• $101 - $150
• ...and so on.

The histogram would then display a bar for each bin. The height of each bar represents the number of transactions (the frequency) that fall within that price range. You might discover that the tallest bar is the $0 - $50 range, showing most of your sales are lower-priced items. This simple visualization instantly gives you a feel for the shape of your sales data.

Histogram vs. Bar Chart: What’s the Difference?

This is an extremely common point of confusion for anyone new to data visualization. Though they both use bars, their purpose is completely different:

• Bar charts compare categorical data. You would use a bar chart to compare sales across different product categories (like "T-Shirts," "Hoodies," "Hats") or sales across different regions ("North," "South," "East," "West"). The categories are distinct and don't have a numerical order.
• Histograms show the distribution of numerical data. You use a histogram to understand the spread of a single continuous variable, like sales value, customer age, or website session duration. The bars in a histogram touch each other to show that the variable on the x-axis is a continuous range.

Why Should You Use a Histogram in Tableau?

Histograms are essential for initial data exploration because they help you quickly grasp the characteristics of a dataset. Instead of looking at a raw table of thousands of numbers, a histogram gives you a bird's-eye view of your data's personality.

1. To Understand the Data's Distribution

The primary job of a histogram is to show the shape of your data distribution. You can instantly answer questions like:

• Is the data symmetric? A "normal distribution" (or bell curve) is symmetric, with most values clustered around the central peak and fewer values at either end. For instance, if you were measuring the height of all players in the NBA, it would likely form a bell curve around an average height.
• Is the data skewed? If the chart has a long "tail" to one side, it's skewed.
• Does the data have multiple peaks (bimodal or multimodal)? Two distinct peaks might indicate that your dataset is composed of two different groups. For example, a histogram of restaurant "rush hour" sales might have one peak around lunchtime and another around dinnertime. Knowing this helps you analyze those groups separately.

2. To Identify the Center, Spread, and Outliers

A histogram makes it easy to estimate the key statistical properties of your data at a glance:

• Central Tendency: You can see where most of the data is clustered. Is it centered around the average? Or is the most frequent value (the mode) somewhere else?
• Spread (Variability): You can see how spread out the data is. Is it all tightly packed into a narrow range, or is it widely dispersed? Wider histograms mean more variability.
• Outliers: Outliers are data points that are significantly different from the rest. On a histogram, these appear as isolated bars far from the main cluster of data. Spotting these can be crucial for cleaning your data or investigating anomalies.

How to Create a Histogram in Tableau: A Step-by-Step Guide

Creating a histogram in Tableau is surprisingly simple, thanks to its "Show Me" feature. Let's walk through it using the Sample - Superstore dataset that comes with Tableau.

Step 1: Connect to Your Data

First, open Tableau and connect to the Sample - Superstore data source.

Step 2: Select Your Numerical Field (Measure)

Look at the Data pane on the left side. A histogram can only be made from a continuous numerical variable, which Tableau calls a Measure (these are fields with a green pound sign # icon).

For this example, let's analyze the distribution of Sales. Find the Sales measure in the Data pane.

Step 3: Just Drag it to the View

Simple as it sounds, just drag the Sales measure and drop it onto the main sheet area. By default, Tableau will create a vertical bar chart showing the sum of all sales - not what we want yet, but we're one click away.

Step 4: Use the "Show Me" Menu to Create the Histogram

In the top right corner of the Tableau window, you'll see a panel called "Show Me." Click it to open a selection of chart types.

Tableau highlights the viable chart types based on the data you've selected. Hover over the histogram icon (it looks like a set of vertical bars representing a distribution) and click it.

That's it! Tableau instantly transforms your basic bar chart into a histogram.

Here’s what Tableau automatically does in the background:

1. It creates a "bin" field from your measure, in this case called Sales (bin). You'll now see this new field in your Data pane under Dimensions.
2. It places Sales (bin) on the Columns shelf.
3. It places the original Sales measure on the Rows shelf and changes its aggregation from SUM to CNT(Sales) (Count of Sales).

The result is a chart showing the count of sales transactions for each sales bin (e.g., how many sales were between $0 and $1,000).

Customizing and Refining Your Tableau Histogram

While the default histogram is a great start, a few small tweaks can make it much more insightful.

1. How to Adjust the Bin Size

The size of your bins is the most critical setting on a histogram. It determines how your data is grouped and can dramatically change the story your chart tells.

• Bins that are too wide can oversimplify the data, hiding important details like multiple peaks.
• Bins that are too narrow can create too much "noise," making it hard to see the underlying shape of the distribution.

To change the bin size:

1. In the Data pane, find your newly created bin field (e.g., Sales (bin)).
2. Right-click on it and select Edit...
3. A dialog box will appear. Here you can set the Size of bins to a specific numerical value. For our Superstore sales data, Tableau's default might be quite large. Try changing it to 100 or 50 to see how the shape of the distribution becomes more detailed. Click OK to apply the change.

Play around with this setting, there's no single "perfect" bin size. The goal is to find a size that clearly reveals the patterns in your data without being too noisy or too simple.

2. Adding Visual Enhancements

Once you've set your bin size, you can add further details to make your histogram easier to read.

• Add Color: You can drag a field to the Color tile on the Marks card to color the bars. For example, dragging CNT(Sales) to Color will create a color gradient, making taller bars darker. Or, you could drag a dimension like Region to Color to see how each region's sales distribution contributes to the overall total.
• Add Labels: To show the exact count on each bar, drag the CNT(Sales) field from the Rows shelf to the Label tile on the Marks card. Hold down Ctrl (or Cmd on Mac) while dragging to duplicate the field instead of moving it.
• Add a Reference Line: Reference lines are great for adding context. Go to the Analytics pane (next to the Data pane), and drag Average Line onto your chart. You can add it to the Table, Pane, or Cell level. This will draw a line at the average sales value, allowing you to instantly see how the distribution relates to the average.

Common Mistakes to Avoid When Making Histograms

• Using it for Categorical Data: Remember, histograms are for continuous numbers, not categories like "Product Names". Use a bar chart for categorical data.
• Forgetting to Experiment with Bin Size: Accepting Tableau's default bin size is a common beginner mistake. It's often not the optimal choice for your specific dataset. Always test a few different sizes to see which one tells the clearest story.
• Over-Interpreting Small Datasets: Histograms are most effective with a reasonably large number of data points. If you only have 20-30 data points, a histogram might not show a meaningful distribution. In such cases, a simple bar chart or just looking at the raw numbers might be better.

Final Thoughts

Creating a histogram in Tableau is a simple but powerful way to look beneath the surface of your data and understand its distribution. By familiarizing yourself with bins and basic customizations, you can use histograms to quickly identify patterns, spot outliers, and start asking deeper questions about what your data is trying to tell you.

While mastering the clicks and drags in a tool like Tableau is effective, sometimes you just want an answer without the setup. We built Graphed because we believe getting insights shouldn't require learning complex software. Instead of manually creating reports, you just connect your sales or marketing data sources - like Shopify or Google Analytics - and ask questions in plain English. You can simply say, "show me the distribution of my Shopify order values this quarter," and we instantly generate a live, interactive histogram for you. It's all about getting back your time so you can focus on making decisions, not building reports.