Plotly: Membuat Visualisasi Interaktif yang Memukau

1. Pengenalan Plotly

Plotly adalah library visualisasi interaktif untuk Python yang menghasilkan grafik berkualitas tinggi yang bisa di-zoom, di-hover, di-pan, dan di-export. Berbeda dari Matplotlib yang menghasilkan gambar statis, Plotly membuat chart interaktif yang bisa dijelajahi oleh pembaca.

Plotly memiliki tiga level API: Plotly Express (sintaksis sederhana, cepat), Graph Objects (kontrol penuh), dan Dash (full dashboard framework). Untuk sebagian besar kasus, mulai dengan Plotly Express lalu pindah ke Graph Objects jika perlu kustomisasi detail.

Plotly vs Matplotlib

Aspek	Plotly	Matplotlib
Interaktif	✅ Zoom, hover, pan, click	❌ Statik (gambar)
Output	HTML, Web, Notebook	PNG, PDF, SVG
Kecepatan coding	🟢 Cepat (Plotly Express)	🟡 Sedang
Kustomisasi	🟡 Baik	🟢 Sangat lengkap
3D	✅ Built-in interaktif	⚠️ Terbatas
Maps	✅ Choropleth, scattergeo	⚠️ Perlu basemap
Cocok untuk	Presentasi, web, EDA	Publikasi, paper

# Instalasi
pip install plotly pandas kaleido

# Import
import plotly.express as px        # API sederhana (80% kasus)
import plotly.graph_objects as go  # API lengkap
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np

2. Scatter & Line Charts

Scatter Plot

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

# Load dataset built-in
df = px.data.gapminder()
print(df.head())

# Scatter plot sederhana
fig = px.scatter(df.query("year == 2007"),
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    hover_name="country",
    log_x=True,
    size_max=60,
    title="GDP per Capita vs Life Expectancy (2007)",
    labels={
        "gdpPercap": "GDP per Capita (USD)",
        "lifeExp": "Life Expectancy (tahun)",
        "continent": "Benua",
        "pop": "Populasi"
    }
)
fig.update_layout(
    template="plotly_dark",
    font=dict(size=14),
    width=900,
    height=600
)
fig.show()

# Export ke HTML (interaktif!)
fig.write_html("scatter_gapminder.html")

# Export ke PNG (perlu kaleido)
fig.write_image("scatter_gapminder.png", scale=2)

Line Chart

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

# Buat data time series
dates = pd.date_range("2024-01-01", periods=365, freq="D")
np.random.seed(42)

df = pd.DataFrame({
    "Tanggal": np.tile(dates, 3),
    "Harga": np.concatenate([
        100 + np.cumsum(np.random.randn(365) * 2),
        80 + np.cumsum(np.random.randn(365) * 1.5),
        120 + np.cumsum(np.random.randn(365) * 3),
    ]),
    "Saham": np.repeat(["BBCA", "BBRI", "TLKM"], 365)
})

# Line chart dengan multiple series
fig = px.line(df,
    x="Tanggal",
    y="Harga",
    color="Saham",
    title="Pergerakan Harga Saham 2024",
    labels={"Harga": "Harga (Rp)", "Saham": "Kode Saham"}
)

# Kustomisasi hover
fig.update_traces(
    hovertemplate="%{fullData.name}
Tanggal: %{x}
Harga: Rp %{y:,.0f}"
)

fig.update_layout(
    template="plotly_dark",
    hovermode="x unified",
    legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)
fig.show()

Area Chart

import plotly.express as px

df = px.data.gapminder()

# Line + area chart untuk Indonesia
df_id = df[df["country"] == "Indonesia"]

fig = px.area(df_id,
    x="year",
    y="pop",
    title="Populasi Indonesia (1952-2007)",
    labels={"pop": "Populasi", "year": "Tahun"},
    markers=True
)
fig.update_traces(
    fillcolor="rgba(0, 200, 100, 0.3)",
    line=dict(color="green", width=3)
)
fig.update_layout(template="plotly_dark")
fig.show()

3. Bar Charts & Histograms

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

# ===== Bar Chart =====
df = px.data.tips()

# Agregasi
daily_avg = df.groupby("day")["total_bill"].mean().reset_index()
daily_avg.columns = ["Hari", "Rata_rata"]

# Bar chart
fig = px.bar(daily_avg,
    x="Hari",
    y="Rata_rata",
    color="Hari",
    title="Rata-rata Total Bill per Hari",
    labels={"Rata_rata": "Rata-rata Bill ($)"},
    text_auto=".2f"
)
fig.update_layout(template="plotly_dark", showlegend=False)
fig.show()

# ===== Grouped Bar Chart =====
tips_by_day_sex = df.groupby(["day", "sex"])["total_bill"].mean().reset_index()

fig = px.bar(tips_by_day_sex,
    x="day",
    y="total_bill",
    color="sex",
    barmode="group",
    title="Rata-rata Bill per Hari & Gender",
    labels={"total_bill": "Rata-rata Bill ($)", "sex": "Gender", "day": "Hari"},
    text_auto=".1f"
)
fig.update_layout(template="plotly_dark")
fig.show()

# ===== Stacked Bar =====
fig = px.bar(tips_by_day_sex,
    x="day",
    y="total_bill",
    color="sex",
    barmode="stack",
    title="Stacked: Total Bill per Hari & Gender"
)
fig.update_layout(template="plotly_dark")
fig.show()

# ===== Horizontal Bar =====
df_sorted = daily_avg.sort_values("Rata_rata")
fig = px.bar(df_sorted,
    x="Rata_rata",
    y="Hari",
    orientation="h",
    title="Rata-rata Bill (Horizontal)",
    text_auto=".2f",
    color="Rata_rata",
    color_continuous_scale="Viridis"
)
fig.update_layout(template="plotly_dark")
fig.show()

Histogram & Distribution

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

# Data
np.random.seed(42)
df = pd.DataFrame({
    "Nilai": np.concatenate([
        np.random.normal(70, 10, 500),  # Kelas A
        np.random.normal(60, 15, 500),  # Kelas B
    ]),
    "Kelas": np.repeat(["Kelas A", "Kelas B"], 500)
})

# Histogram
fig = px.histogram(df,
    x="Nilai",
    color="Kelas",
    barmode="overlay",
    opacity=0.7,
    nbins=30,
    title="Distribusi Nilai Siswa",
    marginal="box",  # Box plot di atas
    labels={"Nilai": "Nilai Ujian"}
)
fig.update_layout(template="plotly_dark")
fig.show()

# Pie Chart
fig = px.pie(df,
    names="Kelas",
    title="Proporsi Kelas",
    hole=0.4,  # Donut chart
    color_discrete_sequence=["#00CC96", "#EF553B"]
)
fig.update_traces(textinfo="percent+label")
fig.update_layout(template="plotly_dark")
fig.show()

4. Heatmaps & Contour

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

# ===== Correlation Heatmap =====
df = px.data.iris()
corr = df.select_dtypes(include=[np.number]).corr()

fig = px.imshow(corr,
    text_auto=".2f",
    color_continuous_scale="RdBu_r",
    title="Correlation Heatmap - Iris Dataset",
    aspect="auto"
)
fig.update_layout(template="plotly_dark", width=700, height=600)
fig.show()

# ===== Heatmap dengan data grid =====
# Data suhu per jam
np.random.seed(42)
jam = [f"{h:02d}:00" for h in range(24)]
hari = ["Senin", "Selasa", "Rabu", "Kamis", "Jumat", "Sabtu", "Minggu"]

# Suhu lebih tinggi siang hari, lebih rendah malam
suhu = np.array([
    25 + 10 * np.sin(np.pi * (h - 6) / 12) + np.random.randn() * 2
    for h in range(24)
    for _ in range(7)
]).reshape(24, 7)

fig = go.Figure(data=go.Heatmap(
    z=suhu,
    x=hari,
    y=jam,
    colorscale="Hot",
    colorbar=dict(title="°C"),
    hovertemplate="Hari: %{x}
Jam: %{y}
Suhu: %{z:.1f}°C"
))
fig.update_layout(
    title="Pola Suhu Harian (°C)",
    yaxis=dict(autorange="reversed"),
    template="plotly_dark",
    width=700,
    height=700
)
fig.show()

# ===== Contour Plot =====
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y) * np.exp(-(X**2 + Y**2) / 10)

fig = go.Figure(data=go.Contour(
    z=Z,
    x=x,
    y=y,
    colorscale="Viridis",
    contours=dict(showlabels=True, labelfont=dict(size=12)),
    colorbar=dict(title="Z")
))
fig.update_layout(
    title="Contour Plot: sin(x)·cos(y)·exp(-(x²+y²)/10)",
    template="plotly_dark"
)
fig.show()

5. 3D Plots

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

# ===== 3D Scatter =====
df = px.data.iris()
fig = px.scatter_3d(df,
    x="sepal_length",
    y="sepal_width",
    z="petal_width",
    color="species",
    size="petal_length",
    title="Iris Dataset - 3D Scatter",
    labels={
        "sepal_length": "Sepal Length",
        "sepal_width": "Sepal Width",
        "petal_width": "Petal Width"
    },
    opacity=0.8
)
fig.update_layout(template="plotly_dark", width=900, height=700)
fig.show()

# ===== 3D Surface =====
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

fig = go.Figure(data=[go.Surface(
    z=Z,
    x=X,
    y=Y,
    colorscale="Plasma",
    contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen")
)])
fig.update_layout(
    title="3D Surface: sin(√(x²+y²))",
    scene=dict(
        xaxis_title="X",
        yaxis_title="Y",
        zaxis_title="Z",
        camera=dict(eye=dict(x=1.5, y=1.5, z=1.2))
    ),
    template="plotly_dark",
    width=900,
    height=700
)
fig.show()

# ===== 3D Line =====
t = np.linspace(0, 10, 500)
x = np.sin(t)
y = np.cos(t)
z = t

fig = go.Figure(data=[go.Scatter3d(
    x=x, y=y, z=z,
    mode='lines',
    line=dict(color=t, colorscale='Rainbow', width=5),
    name='Spiral'
)])
fig.update_layout(
    title="3D Spiral",
    scene=dict(xaxis_title="X", yaxis_title="Y", zaxis_title="Z"),
    template="plotly_dark"
)
fig.show()

6. Geographic Maps

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

# ===== Choropleth Map (Peta Indonesia) =====
# Data provinsi Indonesia (contoh)
data_indonesia = pd.DataFrame({
    "provinsi": ["DKI Jakarta", "Jawa Barat", "Jawa Tengah", "Jawa Timur",
                  "Sumatera Utara", "Sulawesi Selatan", "Bali", "Kalimantan Timur"],
    "kode": ["ID-JK", "ID-JB", "ID-JT", "ID-JI",
             "ID-SU", "ID-SN", "ID-BA", "ID-KT"],
    "penduduk_juta": [10.6, 49.9, 36.5, 40.7, 14.8, 9.1, 4.3, 3.8],
    "pdrb_triliun": [2840, 1200, 650, 900, 520, 350, 240, 450],
})

fig = px.choropleth(data_indonesia,
    geojson="https://raw.githubusercontent.com/superpikar/indonesia-geojson/master/indonesia-provinces.geojson",
    featureidkey="properties.kode",
    locations="kode",
    color="penduduk_juta",
    hover_name="provinsi",
    hover_data={"penduduk_juta": ":.1f", "pdrb_triliun": ":.0f"},
    color_continuous_scale="YlOrRd",
    title="Populasi Provinsi Indonesia (juta jiwa)",
    labels={"penduduk_juta": "Populasi (jt)"}
)
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(template="plotly_dark", width=900, height=600)
fig.show()

# ===== Scatter Geo (World) =====
df = px.data.gapminder().query("year == 2007")

fig = px.scatter_geo(df,
    locations="iso_alpha",
    size="pop",
    color="continent",
    hover_name="country",
    projection="natural earth",
    title="Populasi Dunia 2007",
    size_max=50
)
fig.update_layout(template="plotly_dark")
fig.show()

# ===== Scatter Mapbox (OpenStreetMap) =====
# Lokasi kota-kota Indonesia
kota = pd.DataFrame({
    "kota": ["Jakarta", "Surabaya", "Bandung", "Medan", "Makassar", "Semarang", "Denpasar"],
    "lat": [-6.2088, -7.2575, -6.9175, 3.5952, -5.1477, -6.9666, -8.6500],
    "lon": [106.8456, 112.7521, 107.6191, 98.6722, 119.4221, 110.4196, 115.2167],
    "populasi_jt": [10.6, 2.9, 2.5, 2.4, 1.5, 1.7, 0.9],
    "tipe": ["Ibukota", "Kota Besar", "Kota Besar", "Kota Besar", "Kota Besar", "Kota Besar", "Kota Wisata"],
})

fig = px.scatter_mapbox(kota,
    lat="lat",
    lon="lon",
    size="populasi_jt",
    color="tipe",
    hover_name="kota",
    hover_data={"populasi_juta": True},
    zoom=4,
    mapbox_style="carto-darkmatter",
    title="Kota-kota Besar Indonesia"
)
fig.update_layout(template="plotly_dark", width=900, height=600)
fig.show()

7. Subplots & Layout

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
import pandas as pd

# Membuat subplot grid
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=("Scatter Plot", "Line Chart", "Bar Chart", "Histogram"),
    specs=[
        [{"type": "scatter"}, {"type": "scatter"}],
        [{"type": "bar"}, {"type": "histogram"}]
    ],
    vertical_spacing=0.12,
    horizontal_spacing=0.1
)

# Plot 1: Scatter
np.random.seed(42)
fig.add_trace(
    go.Scatter(x=np.random.randn(100), y=np.random.randn(100),
               mode='markers', marker=dict(color='cyan', size=8), name='Scatter'),
    row=1, col=1
)

# Plot 2: Line
x = np.linspace(0, 10, 100)
fig.add_trace(
    go.Scatter(x=x, y=np.sin(x), mode='lines', name='sin(x)',
               line=dict(color='yellow', width=2)),
    row=1, col=2
)
fig.add_trace(
    go.Scatter(x=x, y=np.cos(x), mode='lines', name='cos(x)',
               line=dict(color='magenta', width=2)),
    row=1, col=2
)

# Plot 3: Bar
hari = ["Sen", "Sel", "Rab", "Kam", "Jum", "Sab", "Min"]
values = [23, 45, 56, 78, 32, 65, 43]
fig.add_trace(
    go.Bar(x=hari, y=values, marker_color='lightgreen', name='Sales'),
    row=2, col=1
)

# Plot 4: Histogram
fig.add_trace(
    go.Histogram(x=np.random.normal(50, 15, 1000), nbinsx=30,
                 marker_color='coral', name='Distribution'),
    row=2, col=2
)

fig.update_layout(
    title_text="Dashboard Multi-Chart",
    template="plotly_dark",
    height=800,
    width=1100,
    showlegend=False
)
fig.show()

Kustomisasi Layout Lanjutan

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=[1, 2, 3, 4, 5],
    y=[10, 20, 25, 30, 45],
    mode='lines+markers+text',
    text=['A', 'B', 'C', 'D', 'E'],
    textposition='top center',
    name='Penjualan',
    line=dict(color='#00CC96', width=3, dash='solid'),
    marker=dict(size=12, symbol='diamond',
                line=dict(width=2, color='white'))
))

# Annotations & shapes
fig.add_annotation(
    x=3, y=25,
    text="Titik balik!",
    showarrow=True,
    arrowhead=2,
    arrowcolor="red",
    font=dict(size=14, color="red"),
    ax=40, ay=-40
)

# Shape: shaded region
fig.add_shape(
    type="rect",
    x0=2, x1=4, y0=0, y1=50,
    fillcolor="rgba(255,255,0,0.1)",
    line=dict(color="yellow", dash="dash"),
)

# Range slider & selector
fig.update_xaxes(
    rangeslider=dict(visible=True),
    rangeselector=dict(
        buttons=list([
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(step="all", label="All")
        ])
    )
)

fig.update_layout(
    title="Kustomisasi Layout Lengkap",
    xaxis=dict(title="Bulan", showgrid=True, gridcolor="rgba(255,255,255,0.1)"),
    yaxis=dict(title="Penjualan (jt)", showgrid=True, gridcolor="rgba(255,255,255,0.1)"),
    template="plotly_dark",
    legend=dict(x=0, y=1, bgcolor="rgba(0,0,0,0.5)"),
    plot_bgcolor="rgba(0,0,0,0)",
    paper_bgcolor="rgba(30,30,30,1)",
    font=dict(family="Arial", size=14, color="white"),
    margin=dict(l=60, r=40, t=80, b=60)
)
fig.show()

8. Animations

import plotly.express as px

# ===== Animated Scatter =====
df = px.data.gapminder()

fig = px.scatter(df,
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    hover_name="country",
    log_x=True,
    size_max=55,
    animation_frame="year",        # Animasi berdasarkan tahun
    animation_group="country",     # Track per country
    range_x=[100, 100000],
    range_y=[25, 90],
    title="Gapminder: GDP vs Life Expectancy Over Time",
    labels={"gdpPercap": "GDP per Capita", "lifeExp": "Life Expectancy"}
)
fig.update_layout(template="plotly_dark", width=900, height=600)
fig.show()

# ===== Animated Bar Chart Race =====
df_top10 = df.groupby(["year", "country"])["pop"].sum().reset_index()
df_top10 = df_top10.sort_values(["year", "pop"], ascending=[True, False])
df_top10 = df_top10.groupby("year").head(10)

fig = px.bar(df_top10,
    x="pop",
    y="country",
    orientation="h",
    color="country",
    animation_frame="year",
    range_x=[0, df_top10["pop"].max() * 1.1],
    title="Top 10 Negara Berdasarkan Populasi",
    labels={"pop": "Populasi", "country": "Negara"}
)
fig.update_layout(template="plotly_dark", width=900, height=600,
                  showlegend=False, yaxis=dict(autorange="reversed"))
fig.show()

9. Financial Charts

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np

# Simulasi data OHLCV
np.random.seed(42)
dates = pd.date_range("2024-01-01", periods=120, freq="B")

harga = 5000 + np.cumsum(np.random.randn(120) * 50)
data_saham = pd.DataFrame({
    "Date": dates,
    "Open": harga + np.random.randn(120) * 20,
    "High": harga + abs(np.random.randn(120) * 40),
    "Low": harga - abs(np.random.randn(120) * 40),
    "Close": harga,
    "Volume": np.random.randint(1000000, 10000000, 120),
})

# Hitung moving averages
data_saham["MA20"] = data_saham["Close"].rolling(20).mean()
data_saham["MA50"] = data_saham["Close"].rolling(50).mean()

# Candlestick chart dengan volume
fig = make_subplots(
    rows=2, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    row_heights=[0.7, 0.3],
    subplot_titles=("Harga BBCA", "Volume")
)

# Candlestick
fig.add_trace(go.Candlestick(
    x=data_saham["Date"],
    open=data_saham["Open"],
    high=data_saham["High"],
    low=data_saham["Low"],
    close=data_saham["Close"],
    increasing_line_color="lime",
    decreasing_line_color="red",
    name="OHLC"
), row=1, col=1)

# Moving Averages
fig.add_trace(go.Scatter(
    x=data_saham["Date"], y=data_saham["MA20"],
    line=dict(color="cyan", width=1), name="MA20"
), row=1, col=1)
fig.add_trace(go.Scatter(
    x=data_saham["Date"], y=data_saham["MA50"],
    line=dict(color="orange", width=1), name="MA50"
), row=1, col=1)

# Volume bars
colors = ["lime" if c >= o else "red"
          for c, o in zip(data_saham["Close"], data_saham["Open"])]
fig.add_trace(go.Bar(
    x=data_saham["Date"], y=data_saham["Volume"],
    marker_color=colors, name="Volume", opacity=0.5
), row=2, col=1)

fig.update_layout(
    title="Candlestick Chart + Volume + MA",
    template="plotly_dark",
    xaxis_rangeslider_visible=False,
    height=800,
    width=1100
)
fig.show()

10. Dashboards dengan Dash

Dash adalah framework untuk membuat dashboard web interaktif menggunakan Python. Tidak perlu JavaScript — cukup Python dan Plotly!

# Install: pip install dash
# Simpan sebagai app.py, jalankan: python app.py

import dash
from dash import dcc, html, Input, Output
import plotly.express as px
import pandas as pd

# Load data
df = px.data.gapminder()

# Buat app
app = dash.Dash(__name__)

app.layout = html.Div([
    # Header
    html.H1("🌍 Gapminder Dashboard",
            style={"textAlign": "center", "color": "white", "padding": "20px"}),

    # Dropdown untuk memilih tahun
    html.Div([
        html.Label("Pilih Tahun:", style={"color": "white", "fontSize": "18px"}),
        dcc.Slider(
            id="year-slider",
            min=df["year"].min(),
            max=df["year"].max(),
            step=None,
            marks={str(year): str(year) for year in df["year"].unique()},
            value=df["year"].max()
        )
    ], style={"padding": "20px", "margin": "0 auto", "maxWidth": "800px"}),

    # Dropdown untuk benua
    html.Div([
        html.Label("Filter Benua:", style={"color": "white"}),
        dcc.Dropdown(
            id="continent-filter",
            options=[{"label": c, "value": c} for c in df["continent"].unique()],
            value=None,
            multi=True,
            placeholder="Semua benua..."
        )
    ], style={"padding": "10px 40px"}),

    # Charts
    html.Div([
        dcc.Graph(id="scatter-plot", style={"width": "50%", "display": "inline-block"}),
        dcc.Graph(id="bar-plot", style={"width": "50%", "display": "inline-block"}),
    ]),

    dcc.Graph(id="line-plot"),

], style={"backgroundColor": "#111", "minHeight": "100vh"})

# Callback: interaktif
@app.callback(
    [Output("scatter-plot", "figure"),
     Output("bar-plot", "figure"),
     Output("line-plot", "figure")],
    [Input("year-slider", "value"),
     Input("continent-filter", "value")]
)
def update_charts(year, continents):
    # Filter data
    dff = df[df["year"] == year]
    if continents:
        dff = dff[dff["continent"].isin(continents)]

    # Scatter
    scatter = px.scatter(dff, x="gdpPercap", y="lifeExp",
        size="pop", color="continent", hover_name="country",
        log_x=True, size_max=55,
        title=f"GDP vs Life Expectancy ({year})",
        template="plotly_dark")

    # Bar
    bar_data = dff.groupby("continent")["pop"].sum().reset_index()
    bar = px.bar(bar_data, x="continent", y="pop",
        color="continent", title=f"Populasi per Benua ({year})",
        template="plotly_dark")

    # Line (Indonesia)
    indo = df[df["country"] == "Indonesia"]
    line = px.line(indo, x="year", y="gdpPercap",
        title="GDP per Capita Indonesia Over Time",
        markers=True, template="plotly_dark")
    line.add_vline(x=year, line_dash="dash", line_color="yellow")

    return scatter, bar, line

if __name__ == "__main__":
    app.run_server(debug=True, port=8050)
    # Buka browser: http://localhost:8050

Tips Export & Integrasi

Metode	Perintah	Cocok untuk
HTML interaktif	fig.write_html("chart.html")	Share ke orang lain
Static image	fig.write_image("chart.png", scale=2)	Presentasi, paper
Notebook	fig.show()	Jupyter/Colab
Dash app	app.run_server()	Web dashboard
Orca	fig.show("svg")	SVG output

11. Quiz Pemahaman