#4 Plotly Interactive Visualizations for Data Scientists

📈 Plotly Interactive Visualizations for Data Scientists

Plotly makes your charts interactive, dynamic, and stunningly modern — all with just a few lines of Python. Zoom, hover, and explore your data like never before! This post shows the most useful examples using plotly.express.

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1️⃣ Install & Import Plotly

# Install Plotly (if not already installed)
!pip install plotly

import plotly.express as px
import pandas as pd

2️⃣ Line Chart — Trend Over Time

# Sample DataFrame
df = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'Sales': [200, 250, 300, 400, 450, 500]
})

# Interactive Line Chart
fig = px.line(df, x='Month', y='Sales', title='Monthly Sales Trend', markers=True)
fig.show()

3️⃣ Bar Chart — Comparing Categories

df = pd.DataFrame({
    'Product': ['A', 'B', 'C', 'D'],
    'Revenue': [1200, 900, 1500, 1100]
})

fig = px.bar(df, x='Product', y='Revenue', color='Product',
             title='Revenue by Product', text='Revenue')
fig.update_traces(textposition='outside')
fig.show()

4️⃣ Scatter Plot — Data Relationship

# Built-in dataset
df = px.data.iris()

# Interactive scatter with hover info
fig = px.scatter(df, x='sepal_width', y='sepal_length',
                 color='species', size='petal_length',
                 hover_data=['petal_width'],
                 title='Iris Flower Data')
fig.show()

5️⃣ Pie Chart — Category Proportions

fig = px.pie(df, names='species', title='Iris Species Distribution', hole=0.3)
fig.show()

6️⃣ Box Plot — Distribution Analysis

fig = px.box(df, x='species', y='sepal_length', color='species',
             title='Sepal Length Distribution by Species')
fig.show()

7️⃣ Histogram — Frequency Distribution

fig = px.histogram(df, x='petal_length', nbins=20, color='species',
                   title='Petal Length Distribution')
fig.show()

8️⃣ Heatmap — Correlation Matrix

corr = df.corr(numeric_only=True)
fig = px.imshow(corr, text_auto=True, color_continuous_scale='viridis',
                title='Correlation Heatmap')
fig.show()

9️⃣ Geo Visualization — World Map

geo_df = px.data.gapminder().query("year == 2007")

fig = px.choropleth(
    geo_df,
    locations="iso_alpha",
    color="lifeExp",
    hover_name="country",
    title="🌍 Life Expectancy Around the World (2007)",
    color_continuous_scale=px.colors.sequential.Plasma
)
fig.show()

🔟 Animated Chart — Population Growth Over Time

gapminder = px.data.gapminder()

fig = px.scatter(
    gapminder,
    x="gdpPercap",
    y="lifeExp",
    animation_frame="year",
    animation_group="country",
    size="pop",
    color="continent",
    hover_name="country",
    log_x=True,
    size_max=55,
    range_x=[100,100000],
    range_y=[25,90],
    title="🌎 Population Growth (1952–2007)"
)
fig.show()

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💡 Pro Tip: Use fig.write_html("chart.html") to save any Plotly chart as an interactive web page! Perfect for dashboards, portfolios, or blog embeds. 🚀