Plotly Express Basics

What is Plotly Express?

Plotly Express (px) is the easy way to use Plotly. One line = one chart.

Import

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import plotly.express as px
import pandas as pd

Line Chart

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df = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    'Sales': [100, 120, 115, 140, 160]
})

fig = px.line(df, x='Month', y='Sales', title='Monthly Sales')
fig.show()

Bar Chart

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fig = px.bar(df, x='Month', y='Sales', title='Sales by Month')
fig.show()

Scatter Plot

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df = pd.DataFrame({
    'Age': [25, 30, 35, 40, 45],
    'Salary': [40000, 50000, 60000, 70000, 80000]
})

fig = px.scatter(df, x='Age', y='Salary', title='Age vs Salary')
fig.show()

Color by Category

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df = pd.DataFrame({
    'Age': [25, 30, 35, 40, 45, 50],
    'Salary': [40000, 50000, 60000, 70000, 80000, 90000],
    'Department': ['Sales', 'IT', 'Sales', 'IT', 'HR', 'HR']
})

fig = px.scatter(df, x='Age', y='Salary', color='Department')
fig.show()

Each department gets a different color!

Size by Value

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df['Experience'] = [2, 5, 8, 12, 15, 20]

fig = px.scatter(df, x='Age', y='Salary',
                 color='Department', size='Experience')
fig.show()

Histogram

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import numpy as np

data = np.random.normal(50, 10, 500)
fig = px.histogram(x=data, nbins=30, title='Distribution')
fig.show()

Box Plot

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df = pd.DataFrame({
    'Department': ['Sales']*20 + ['IT']*20,
    'Salary': list(range(40000, 60000, 1000)) + list(range(60000, 80000, 1000))
})

fig = px.box(df, x='Department', y='Salary')
fig.show()

Pie Chart

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df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'D'],
    'Value': [30, 25, 25, 20]
})

fig = px.pie(df, values='Value', names='Category', title='Distribution')
fig.show()

Common Parameters

Parameter	What it does
x	X-axis column
y	Y-axis column
color	Color by column
size	Size by column
title	Chart title
labels	Rename axes

Rename Axis Labels

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fig = px.scatter(df, x='Age', y='Salary',
                 labels={'Age': 'Employee Age', 'Salary': 'Annual Salary'})
fig.show()

Key Points

px.line() for trends
px.bar() for comparisons
px.scatter() for relationships
px.histogram() for distributions
px.box() for spread
px.pie() for proportions
Use color and size for extra info

What's Next?

Learn to create interactive scatter plots with more features.