Histogram

Histograms visualize the distribution of continuous numerical data by dividing values into bins and displaying the frequency of observations in each bin. They reveal the shape of your data distribution—whether it's normal, skewed, bimodal, or uniform. Histograms are essential for exploratory data analysis and help identify outliers, central tendency, and spread.

Example Visualization

Histogram showing age distribution with 20 bins and KDE overlay

Try this prompt

"Create a histogram showing the 'Age Distribution' of 500 survey respondents. Generate realistic demographic data with a mean age of 42 years and standard deviation of 15 years, slightly right-skewed (more young adults). Use 20 bins ranging from 18 to 80. Overlay a kernel density estimate (KDE) curve in red. Add vertical dashed lines for mean (blue), median (green), and mode (orange) with annotations. Fill histogram bars with a semi-transparent blue. Include X-axis label 'Age (years)', Y-axis as 'Frequency'. Add a text box showing summary statistics: mean, median, std dev, min, max. Title: 'Age Distribution of Survey Respondents (n=500)'."

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Python Code Example

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Common Use Cases

1Analyzing age demographics
2Examining test score distributions
3Quality control measurements
4Financial return distributions

Pro Tips

Experiment with different bin sizes

Add KDE overlay for smooth distribution curve

Use logarithmic scales for highly skewed data