Histogram Maker
Created:November 10, 2024
Last Updated:March 21, 2025
Create beautiful histogram from your data. Upload your own data or try our sample datasets.
Try it out!
- Click Sample Data and select Restaurant Tips
- For Value column, select total_bill
- For Group/Color By column, select sex or leave it as None
- For Number of Bins, select 30 (default)
- For Density, check the box to show density line
- Click Generate Histogram to visualize the data
Calculator
1. Load Your Data
2. Select Column & Options
Learn More
Histograms: Understanding Data Distributions
What is a Histogram?
A histogram is a graphical representation that organizes a group of numerical data points into bins, displaying the frequency of data points that fall into each bin. Unlike bar charts, histograms are used for continuous data where bins represent ranges of values. The height of each bar shows how many observations fall into that range, helping visualize the distribution shape, central tendency, and variability of the data.
When to Use Histograms
- Visualizing the distribution of continuous numerical data
- Identifying patterns, skewness, and potential outliers in data
- Comparing distributions across different groups or categories
- Understanding the shape and spread of your data
- Checking assumptions of normality in statistical analyses
Key Features
- Bins: Consecutive, non-overlapping intervals of your data
- Frequency: Height of bars showing count or proportion in each bin
- Distribution Shape: Can be normal, skewed, bimodal, or uniform
- Density Lines: Smooth curves showing the approximate shape of the distribution
- Color Groups: Optional categorical splitting for comparison
Best Practices
- Choose an appropriate number of bins to balance detail and smoothness
- Consider adding density lines for smoother distribution visualization
- Use consistent bin widths unless there's a specific reason not to
- Include clear labels for axes and legend when using groups
- Consider the scale of your y-axis (count vs. proportion)
- Use transparency when comparing multiple distributions
Common Applications
- Population demographics (age, income, etc.)
- Scientific measurements and experimental results
- Quality control in manufacturing
- Financial data analysis
- Educational test scores and performance metrics
- Environmental and weather data