Plotly Interactive Visualization

What is Plotly?

Plotly is a powerful library for building interactive visualizations in Python. It works well in Jupyter notebooks, web apps (Dash), and static HTML exports.

Installation & Basic Import

# Install Plotly (terminal)
pip install plotly

import plotly.express as px

Example 1: Interactive Scatter Plot

Scatter plots can show relationships between two variables with interactive tooltips.

import plotly.express as px
import pandas as pd

# Sample data (could be the full Iris dataset)
data = {
    "sepal_length": [5.1, 4.9, 6.7, 5.9],
    "sepal_width":  [3.5, 3.0, 3.1, 3.0],
    "species":      ["setosa", "setosa", "versicolor", "virginica"]
}

df = pd.DataFrame(data)

# Create an interactive scatter plot
fig = px.scatter(
    df,
    x="sepal_length",
    y="sepal_width",
    color="species",               # color points by species
    title="Iris Sepal Length vs Width",
    hover_data=["species"],        # show extra info on hover
    size_max=10
)

fig.show()  # opens in notebook or browser

Example 2: Interactive Bar Chart

import plotly.express as px
import pandas as pd

data = {
    "fruit": ["Apple", "Banana", "Orange"],
    "count": [10, 15, 7]
}

df = pd.DataFrame(data)

fig = px.bar(
    df,
    x="fruit",            # categories
    y="count",            # values
    title="Fruit Count",
    color="fruit",        # each bar with different color
    text="count"          # show value on top of bar
)

# Make labels easier to read
fig.update_traces(textposition="outside")

fig.show()

Example 3: Line Chart

import plotly.express as px
import pandas as pd

data = {
    "day":   [1, 2, 3, 4, 5],
    "sales": [100, 120, 90, 150, 130]
}

df = pd.DataFrame(data)

fig = px.line(
    df,
    x="day",
    y="sales",
    title="Sales Over 5 Days",
    markers=True   # show markers on the line
)

# Customize axes labels
fig.update_layout(
    xaxis_title="Day",
    yaxis_title="Sales ($)"
)

fig.show()