Histogram in Plotly

Last Updated: 08 Nov 2025

---

Histograms show frequency distribution of continuous data — perfect for age groups, exam scores, temperature, or sales ranges.

Hinglish Tip: Histogram = "kitne log kis range mein aate hain" — jaise class mein marks ka bucket!

---

Basic Histogram

import plotly.express as px
import numpy as np

# Generate sample data
np.random.seed(42)
data = np.random.normal(100, 15, 1000)  # Mean=100, Std=15

fig = px.histogram(x=data, title="Exam Scores Distribution")
fig.show()

---

Key Parameters in px.histogram()

Parameter	Values / Examples	Purpose
data_frame	df	Use Pandas DataFrame
x	Column name or list	Data to bin
nbins	20, 50	Number of bins
color	Column name	Color by category (stacked)
barmode	'overlay', 'stack', 'relative'	How multiple groups display
histnorm	'', 'percent', 'probability', 'density'	Normalize: count, %, or density
opacity	0.7	Transparency (for overlay)
text_auto	True	Show count on top of bars
title, labels	String, dict	Title & axis labels

---

Full Example: Student Scores by Class (Overlay + Density)

import plotly.express as px
import pandas as pd
import numpy as np

# Sample data: Two classes
np.random.seed(42)
class_a = np.random.normal(75, 10, 500)
class_b = np.random.normal(82, 12, 500)

df = pd.DataFrame({
    'Score': np.concatenate([class_a, class_b]),
    'Class': ['A']*500 + ['B']*500
})

# Overlay Histogram
fig1 = px.histogram(
    df, x='Score', color='Class',
    nbins=30,
    barmode='overlay',
    opacity=0.7,
    title='Score Distribution: Class A vs B (Overlay)',
    labels={'Score': 'Marks', 'count': 'No. of Students'},
    histnorm='',
    text_auto=True
)
fig1.update_layout(barmode='overlay')
fig1.show()

# Density (Probability)
fig2 = px.histogram(
    df, x='Score', color='Class',
    nbins=30,
    barmode='overlay',
    opacity=0.8,
    title='Score Distribution (Density Curve Style)',
    histnorm='probability density',
    marginal='rug'  # Shows individual points below
)
fig2.show()

Hinglish Tip: histnorm='probability density' → area under curve = 1 — jaise normal distribution graph!

---

Stacked Histogram (Total + Breakdown)

fig = px.histogram(
    df, x='Score', color='Class',
    nbins=25,
    barmode='stack',
    title='Total Students by Score Range',
    text_auto=True
)
fig.update_traces(textposition='inside')
fig.show()

---

Horizontal Histogram (orientation='h')

fig = px.histogram(
    df, y='Score', color='Class',
    orientation='h',
    nbins=20,
    title='Horizontal Histogram (Good for long labels)'
)
fig.show()

Hinglish Tip: Horizontal → bin labels overlap nahi hote — clean for reports!

---

Add Mean Line & Stats

fig = px.histogram(df, x='Score', color='Class', nbins=30, barmode='overlay', opacity=0.7)

# Add mean lines
mean_a = class_a.mean()
mean_b = class_b.mean()

fig.add_vline(x=mean_a, line_dash="dash", line_color="blue", annotation_text=f"Class A Mean: {mean_a:.1f}")
fig.add_vline(x=mean_b, line_dash="dash", line_color="red", annotation_text=f"Class B Mean: {mean_b:.1f}")

fig.update_layout(title="Histogram with Mean Lines")
fig.show()

---

Common Mistakes to Avoid

Mistake	Fix
Wrong bin size → misleading	Test nbins=20, 30, 50 → pick best
Overlay bars hide each other	Use opacity=0.6 + barmode='overlay'
No normalization	Use histnorm='percent' for fair comparison
Too many bins → noisy	Start with nbins=30
Forget to install kaleido	pip install -U kaleido

---