Advanced Charts, Graph Objects & Export in Plotly: Complete Guide

3D plots, Geographic maps, Sunbursts, Treemaps, Funnels — aur Graph Objects se pixel-level control. Plus complete styling, layout customization aur export guide — sab ek final masterclass mein.

📑 Is Guide Mein 3 Sections Cover Honge:

• Section A — Advanced PX Charts: 3D Scatter, 3D Line, Sunburst, Treemap, Funnel, Choropleth, Scatter Geo
• Section B — Graph Objects: go.Figure, go.Scatter, go.Bar, go.Heatmap, make_subplots
• Section C — Styling & Export: update_layout, update_traces, Templates, HTML/Image export

Section A — Advanced Plotly Express Charts

🔍 Kya Hai: 3D scatter plot mein mouse se rotate, zoom, pan kar sakte hain — 3 continuous variables ka relationship ek interactive 3D space mein dikhta hai. Color aur size se 5 dimensions tak visualize ho sakte hain.

fig = px.scatter_3d(employees, 
    x="Experience",                # x-axis
    y="Age",                       # y-axis
    z="Salary",                    # z-axis (vertical)
    color="Department",             # 4th dimension — color
    size="Rating",                  # 5th dimension — bubble size
    symbol="Gender",                # 6th dimension — marker shape
    hover_name="Name",
    hover_data={"Salary": "₹,.0f"},
    size_max=15,
    opacity=0.7,
    labels={
        "Experience": "Exp (Years)",
        "Salary": "Salary (₹)",
        "Age": "Age (Years)"
    },
    title="3D: Experience × Age × Salary",
    template="plotly_white",
    color_discrete_sequence=px.colors.qualitative.Set2,
    width=900, height=700
)

fig.update_layout(
    scene=dict(                      # 3D scene settings
        xaxis_title="Experience",
        yaxis_title="Age",
        zaxis_title="Salary",
        camera=dict(eye=dict(x=1.5, y=1.5, z=0.8))  # initial camera angle
    )
)

fig.write_html("scatter_3d.html")
fig.show()

🔍 Kya Hai: 3D line chart trajectories dikhata hai — time series ko 3D space mein trace karna, spiral paths, aur multi-variable trends ke liye.

# Spiral 3D Line
t = np.linspace(0, 10, 200)
spiral = pd.DataFrame({
    "X": np.cos(t * 2) * t,
    "Y": np.sin(t * 2) * t,
    "Z": t,
    "Time": t
})

fig = px.line_3d(spiral, x="X", y="Y", z="Z",
    color="Time",                   # color gradient over time
    title="3D Spiral Trajectory",
    template="plotly_dark",
    width=800, height=600
)

fig.write_html("line_3d.html")
fig.show()

🔍 Kya Hai: Sunburst chart nested pie chart jaisa hai — center se bahar ki taraf hierarchy dikhti hai. Company → Department → Gender → Rating jaisi multi-level breakdowns ke liye perfect. Click karke drill-down ho jaata hai!

fig = px.sunburst(employees,
path=["Department", "Gender", "Rating"],  # hierarchy levels (inside → outside)
values="Salary",                # size of each segment
color="Department",             # color by top level
hover_data={"Salary": "₹,.0f"},
title="Company Hierarchy — Sunburst",
template="plotly_white",
color_discrete_sequence=px.colors.qualitative.Set2,
width=750, height=750
)

fig.update_traces(
textinfo="label+percent parent",  # show label + % of parent
insidetextorientation="radial"  # "horizontal","radial","tangential","auto"
)

fig.write_html("sunburst.html")
fig.show()

🔍 Kya Hai: Treemap nested rectangles se hierarchy dikhata hai — bada rectangle = badi value. Sunburst jaisa hi hai lekin rectangular layout mein. Space utilization better hai aur large hierarchies mein zyada readable hai.

fig = px.treemap(employees,
path=[px.Constant("Company"), "Department", "Gender"],  # px.Constant = root label
values="Salary",
color="Salary",                 # color by salary (continuous)
color_continuous_scale="RdYlGn",  # gradient colorscale
color_continuous_midpoint=employees["Salary"].mean(),
hover_data={"Salary": "₹,.0f"},
title="Company Salary Structure — Treemap",
template="plotly_white",
width=900, height=600
)

fig.update_traces(
textinfo="label+value+percent parent",
textfont_size=12
)

fig.write_html("treemap.html")
fig.show()

🔍 Kya Hai: Funnel chart step-by-step conversion/drop-off dikhata hai — website visitors → sign-ups → purchases jaisi pipeline. Har stage par kitne log drop hue clearly dikhta hai.

funnel_data = pd.DataFrame({
    "Stage": ["Website Visits", "Product Views", "Add to Cart", 
             "Checkout", "Payment", "Delivered"],
    "Count": [15000, 8500, 4200, 2800, 2100, 1950]
})

fig = px.funnel(funnel_data, x="Count", y="Stage",
    text="Count",                   # data labels
    title="E-Commerce Sales Funnel",
    template="plotly_white",
    color_discrete_sequence=px.colors.sequential.Blues_r,
    width=800, height=500
)

fig.update_traces(textinfo="value+percent initial")
fig.write_html("funnel.html")
fig.show()

# ===== FUNNEL AREA (Smooth) =====
fig2 = px.funnel_area(funnel_data, names="Stage", values="Count",
    title="Funnel Area — Smooth Version",
    color_discrete_sequence=px.colors.sequential.Sunset
)
fig2.write_html("funnel_area.html")
fig2.show()

🔍 Kya Hai: Choropleth map countries/regions ko values ke basis par color-code karta hai — GDP, population, sales by region jaise geographic data visualization ke liye. Hover se country-wise details dikhte hain, zoom aur pan support hai.

# Using built-in Plotly dataset
gapminder = px.data.gapminder()
latest = gapminder[gapminder["year"] == 2007]

fig = px.choropleth(latest,
    locations="iso_alpha",          # country ISO codes
    color="gdpPercap",              # color by GDP per capita
    hover_name="country",           # country name on hover
    hover_data={"pop": ",.0f", "lifeExp": ":.1f"},
    color_continuous_scale="Viridis",  # colorscale
    projection="natural earth",     # map projection
    title="World GDP Per Capita (2007)",
    labels={"gdpPercap": "GDP/Capita ($)"},
    template="plotly_white",
    width=1000, height=550
)

fig.write_html("choropleth.html")
fig.show()

# ===== ANIMATED MAP (Over Years!) =====
fig2 = px.choropleth(gapminder,
    locations="iso_alpha",
    color="gdpPercap",
    hover_name="country",
    animation_frame="year",        # ANIMATED over years! 🎬
    color_continuous_scale="Viridis",
    projection="natural earth",
    title="GDP Growth Animation (1952-2007)",
    range_color=[0, 50000]
)
fig2.write_html("choropleth_animated.html")
fig2.show()

🔍 Kya Hai: Scatter geo map par dots plot karta hai — har dot ek city/location represent karta hai. Dot ka size aur color variables se map hota hai. Office locations, customer distribution, event locations visualize karne ke liye.

fig = px.scatter_geo(latest,
locations="iso_alpha",
color="continent",
size="pop", # bubble size = population
hover_name="country",
hover_data={"pop": ",.0f", "gdpPercap": "$,.0f"},
size_max=40,
projection="natural earth",
title="World Population by Country (Bubble Map)",
template="plotly_white",
color_discrete_sequence=px.colors.qualitative.Set2,
width=1000, height=550
)

fig.write_html("scatter_geo.html")
fig.show()

Section B — Graph Objects (Advanced Control)

🔍 Kya Hai: Graph Objects (go) Plotly ka low-level API hai — px se zyada verbose lekin pixel-level control milta hai. Multiple traces (lines/bars/areas) ek figure mein manually add kar sakte hain. Complex dashboards aur custom charts ke liye essential.

import plotly.graph_objects as go

months = ["Jan","Feb","Mar","Apr","May","Jun"]
revenue_2023 = [45,52,48,61,55,67]
revenue_2024 = [50,58,53,69,62,75]

fig = go.Figure()

# Trace 1: 2023 Line
fig.add_trace(go.Scatter(
    x=months, y=revenue_2023,
    name="2023",
    mode="lines+markers",          # "lines","markers","lines+markers","text"
    line=dict(color="#3498db", width=3, dash="solid"),  # "solid","dash","dot","dashdot"
    marker=dict(size=10, symbol="circle", 
                line=dict(width=2, color="white")),
    hovertemplate="Month: %{x}<br>Revenue: ₹%{y}K<extra></extra>",
    fill="none"                     # "none","tozeroy","tonexty","toself"
))

# Trace 2: 2024 Line with fill
fig.add_trace(go.Scatter(
    x=months, y=revenue_2024,
    name="2024",
    mode="lines+markers",
    line=dict(color="#e74c3c", width=3, dash="dash"),
    marker=dict(size=10, symbol="diamond"),
    fill="tonexty",                # fill area between this and previous trace
    fillcolor="rgba(231,76,60,0.1)"
))

# Reference line
fig.add_hline(y=60, line_dash="dot", line_color="gray",
    annotation_text="Target: ₹60K", annotation_position="top right")

fig.update_layout(
    title="Revenue Comparison: 2023 vs 2024",
    template="plotly_white",
    hovermode="x unified",
    width=900, height=500
)

fig.write_html("go_scatter.html")
fig.show()

departments = ["IT","HR","Finance","Marketing"]
salary_2023 = [78,52,70,58]
salary_2024 = [85,58,76,64]

fig = go.Figure()

fig.add_trace(go.Bar(
    name="2023", x=departments, y=salary_2023,
    marker_color="#3498db",
    marker_line=dict(width=1.5, color="white"),
    text=[f"₹{v}K" for v in salary_2023],
    textposition="outside",
    textfont_size=12,
    opacity=0.85,
    hovertemplate="%{x}: ₹%{y}K<extra>2023</extra>"
))

fig.add_trace(go.Bar(
    name="2024", x=departments, y=salary_2024,
    marker_color="#2ecc71",
    marker_line=dict(width=1.5, color="white"),
    text=[f"₹{v}K" for v in salary_2024],
    textposition="outside",
    textfont_size=12,
    opacity=0.85
))

fig.update_layout(
    barmode="group",
    title="Salary Comparison: 2023 vs 2024",
    template="plotly_white",
    bargap=0.2,                    # gap between groups
    bargroupgap=0.1,               # gap within group
    width=800, height=500
)

fig.write_html("go_bar.html")
fig.show()

# Correlation heatmap
corr = employees.select_dtypes(include=["number"]).corr().round(2)

fig = go.Figure(go.Heatmap(
z=corr.values,
x=corr.columns,
y=corr.columns,
colorscale="RdBu_r",            # diverging colorscale
zmid=0,                         # center value
zmin=-1, zmax=1,
text=corr.values,               # show values in cells
texttemplate="%{text:.2f}",     # format
textfont_size=12,
hovertemplate="%{x} vs %{y}: %{z:.3f}<extra></extra>",
colorbar=dict(title="Correlation", thickness=15)
))

fig.update_layout(
title="Interactive Correlation Heatmap",
template="plotly_white",
width=700, height=600
)

fig.write_html("heatmap_plotly.html")
fig.show()

🔍 Kya Hai: make_subplots() se multiple interactive charts ek figure mein arrange hote hain — har chart independently hover, zoom, pan support karta hai. Production dashboards ka foundation.

from plotly.subplots import make_subplots

fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=("Revenue Trend", "Department Split",
                    "Salary Distribution", "Experience vs Salary"),
    specs=[[{"type": "scatter"}, {"type": "pie"}],
           [{"type": "histogram"}, {"type": "scatter"}]],
    vertical_spacing=0.12,
    horizontal_spacing=0.1
)

# Chart 1: Line
fig.add_trace(go.Scatter(
    x=months, y=revenue_2024, mode="lines+markers",
    line=dict(color="#3498db", width=3),
    marker=dict(size=8), name="Revenue"
), row=1, col=1)

# Chart 2: Pie
dept_counts = employees["Department"].value_counts()
fig.add_trace(go.Pie(
    labels=dept_counts.index, values=dept_counts.values,
    hole=0.4, marker_colors=px.colors.qualitative.Set2
), row=1, col=2)

# Chart 3: Histogram
fig.add_trace(go.Histogram(
    x=employees["Salary"], nbinsx=25,
    marker_color="#9b59b6", opacity=0.7, name="Salary"
), row=2, col=1)

# Chart 4: Scatter
fig.add_trace(go.Scatter(
    x=employees["Experience"], y=employees["Salary"],
    mode="markers", marker=dict(size=4, color="#e74c3c", opacity=0.4),
    name="Exp vs Sal"
), row=2, col=2)

fig.update_layout(
    title_text="🏢 Company Analytics Dashboard",
    title_font_size=22,
    showlegend=False,
    template="plotly_white",
    height=800, width=1100
)

fig.write_html("dashboard_plotly.html")
fig.show()

Section C — Styling, Layout & Export

fig.update_layout(
    # ===== TITLE =====
    title=dict(
        text="Revenue Analysis",
        font=dict(size=22, family="Arial", color="#2c3e50"),
        x=0.5,                      # center title (0=left, 0.5=center, 1=right)
        y=0.95
    ),
    
    # ===== AXES =====
    xaxis=dict(
        title="Month",
        title_font_size=14,
        tickfont_size=12,
        showgrid=True,
        gridcolor="#ecf0f1",
        gridwidth=0.5,
        showline=True,
        linecolor="#bdc3c7",
        tickangle=-45,               # rotate tick labels
        range=[0, 100]               # axis limits
    ),
    yaxis=dict(
        title="Revenue (₹)",
        title_font_size=14,
        showgrid=True,
        gridcolor="#ecf0f1",
        zeroline=True,
        zerolinecolor="#bdc3c7"
    ),
    
    # ===== LEGEND =====
    legend=dict(
        title="Year",
        font_size=12,
        bgcolor="rgba(255,255,255,0.8)",
        bordercolor="#bdc3c7",
        borderwidth=1,
        x=1.02, y=1,                # position
        orientation="v"              # "v"=vertical, "h"=horizontal
    ),
    
    # ===== BACKGROUND =====
    plot_bgcolor="white",          # plot area background
    paper_bgcolor="#f8fafc",       # entire figure background
    
    # ===== HOVER =====
    hovermode="x unified",         # "x","y","closest","x unified","y unified"
    hoverlabel=dict(
        bgcolor="white",
        font_size=12,
        font_family="Arial"
    ),
    
    # ===== MARGINS =====
    margin=dict(l=60, r=30, t=80, b=60),
    
    # ===== SIZE =====
    width=900, height=500,
    
    # ===== TEMPLATE =====
    template="plotly_white"
)

# ===== LINE TRACES =====
fig.update_traces(
    line_width=3,
    line_color="#3498db",
    line_dash="solid",              # "solid","dash","dot","dashdot","longdash"
    marker_size=10,
    marker_symbol="circle",        # "circle","square","diamond","cross","star","triangle-up"
    opacity=0.85,
    selector=dict(type="scatter")  # apply only to scatter traces
)

# ===== BAR TRACES =====
fig.update_traces(
    marker_color="#2ecc71",
    marker_line_width=1.5,
    marker_line_color="white",
    textposition="outside",
    textfont_size=12,
    selector=dict(type="bar")
)

# ===== PIE TRACES =====
fig.update_traces(
    textinfo="percent+label",
    textfont_size=13,
    pull=[0.05, 0, 0, 0],
    marker_line_width=2,
    marker_line_color="white",
    selector=dict(type="pie")
)

# ===== CUSTOM HOVERTEMPLATE =====
fig.update_traces(
    hovertemplate=(
        "<b>%{x}</b><br>"
        "Revenue: ₹%{y:,.0f}<br>"
        "<extra></extra>"              # removes trace name box
    )
)

# ===== REFERENCE LINES (Shortcut) =====
fig.add_hline(y=50, line_dash="dash", line_color="red", line_width=1.5,
    annotation_text="Target", annotation_position="top left")
fig.add_vline(x="Mar", line_dash="dot", line_color="gray")
fig.add_hrect(y0=45, y1=55, fillcolor="red", opacity=0.1, line_width=0)  # shaded region

# ===== ANNOTATION (Arrow + Text) =====
fig.add_annotation(
    x="Jun", y=75,                 # arrow points here
    text="Peak Month! 🎉",
    showarrow=True,
    arrowhead=2,                   # arrowhead style (0-8)
    arrowsize=1.5,
    arrowwidth=2,
    arrowcolor="#e74c3c",
    ax=-60, ay=-40,                # arrow offset (text position relative)
    font=dict(size=14, color="#e74c3c"),
    bgcolor="#fadbd8",
    bordercolor="#e74c3c",
    borderwidth=1,
    borderpad=4
)

# ===== SHAPE (Rectangle, Circle, Line) =====
fig.add_shape(
    type="rect",                   # "rect","circle","line","path"
    x0="Apr", x1="Jun", y0=60, y1=80,
    fillcolor="rgba(52,152,219,0.1)",
    line=dict(color="#3498db", dash="dash")
)

# ===== INTERACTIVE HTML (Best for web/sharing) =====
fig.write_html("chart.html",
    include_plotlyjs=True,        # embed plotly.js (standalone file)
    full_html=True,                # complete HTML document
    auto_open=False                # don't auto-open in browser
)

# Smaller file — CDN link instead of embedding
fig.write_html("chart_cdn.html", include_plotlyjs="cdn")

# ===== STATIC IMAGES (Requires kaleido) =====
# pip install kaleido

fig.write_image("chart.png", scale=2)       # PNG (scale=2 for retina)
fig.write_image("chart.jpg", quality=95)    # JPG with quality
fig.write_image("chart.svg")               # SVG (vector — scalable)
fig.write_image("chart.pdf")               # PDF (print quality)

# Custom size for export
fig.write_image("chart_large.png", width=1920, height=1080, scale=2)

# ===== DISPLAY OPTIONS =====
fig.show()                                 # Jupyter notebook
fig.show(renderer="browser")               # open in browser
fig.show(renderer="png")                   # static PNG in notebook
fig.show(renderer="svg")                   # static SVG in notebook

# Set default renderer
import plotly.io as pio
pio.renderers.default = "notebook"         # Jupyter
pio.renderers.default = "browser"          # External browser

📋 Export Format Comparison:

🎉 Plotly Masterclass Complete!

Congratulations! Aapne 2 comprehensive parts mein 25+ Plotly functions cover kiye hain — Basic interactive charts se lekar 3D plots, Geographic maps, Graph Objects, Dashboards aur Export tak. Ab aap production-grade interactive visualizations confidently create kar sakte hain.

Happy Interactive Visualizing! 📊🚀