Plotly Cheatsheet

Installation

Platform/Method	Command
Python (pip)	`pip install plotly`
Python with all dependencies	`pip install plotly[all]`
Conda (recommended)	`conda install -c conda-forge plotly`
Jupyter Notebook support	`pip install "notebook>=5.3" "ipywidgets>=7.5"`
JupyterLab 3.x	`pip install jupyterlab "ipywidgets>=7.6"`
Dash framework	`pip install dash`
Static image export	`pip install -U kaleido`
Development version	`pip install git+https://github.com/plotly/plotly.py.git`
Upgrade to latest	`pip install --upgrade plotly`
Verify installation	`python -c "import plotly; print(plotly.__version__)"`

Basic Commands - Plotly Express (High-Level API)

Command	Description
`import plotly.express as px`	Import Plotly Express for quick visualizations
`px.scatter(df, x='col1', y='col2')`	Create scatter plot from DataFrame
`px.line(df, x='date', y='value')`	Create line chart for time series
`px.bar(df, x='category', y='value')`	Create vertical bar chart
`px.bar(df, x='value', y='category', orientation='h')`	Create horizontal bar chart
`px.histogram(df, x='values', nbins=20)`	Create histogram with specified bins
`px.box(df, x='category', y='values')`	Create box plot for distribution analysis
`px.violin(df, x='category', y='values')`	Create violin plot showing distribution
`px.pie(df, values='amount', names='category')`	Create pie chart
`px.pie(df, values='amount', names='category', hole=0.4)`	Create donut chart
`px.scatter_3d(df, x='x', y='y', z='z')`	Create 3D scatter plot
`px.imshow(data_matrix)`	Create heatmap from 2D array
`px.density_heatmap(df, x='col1', y='col2')`	Create density heatmap from data points
`px.area(df, x='date', y='value')`	Create area chart
`px.funnel(df, x='value', y='stage')`	Create funnel chart for conversion analysis
`fig.show()`	Display figure in default renderer
`fig.show(renderer='browser')`	Display figure in web browser
`fig.write_html('output.html')`	Save figure as HTML file
`fig.write_image('output.png')`	Save figure as PNG image
`fig.write_image('output.pdf')`	Save figure as PDF

Basic Commands - Graph Objects (Low-Level API)

Command	Description
`import plotly.graph_objects as go`	Import Graph Objects for detailed control
`go.Figure()`	Create empty figure object
`go.Scatter(x=data_x, y=data_y)`	Create scatter trace
`go.Scatter(mode='lines')`	Create line trace
`go.Scatter(mode='markers')`	Create markers-only scatter
`go.Scatter(mode='lines+markers')`	Create line with markers
`go.Bar(x=categories, y=values)`	Create bar trace
`go.Histogram(x=values)`	Create histogram trace
`go.Box(y=values)`	Create box plot trace
`go.Heatmap(z=matrix)`	Create heatmap trace
`go.Scatter3d(x=x, y=y, z=z)`	Create 3D scatter trace
`go.Surface(z=matrix)`	Create 3D surface plot
`go.Contour(z=matrix)`	Create contour plot
`fig.add_trace(trace)`	Add trace to existing figure
`fig.data`	Access all traces in figure
`fig.layout`	Access figure layout properties

Advanced Usage - Customization

Command	Description
`fig.update_layout(title='Title')`	Update figure title
`fig.update_layout(template='plotly_dark')`	Apply dark theme template
`fig.update_layout(width=800, height=600)`	Set figure dimensions
`fig.update_xaxes(title='X Label')`	Update x-axis properties
`fig.update_yaxes(title='Y Label', type='log')`	Update y-axis with log scale
`fig.update_traces(marker_size=10)`	Update all trace properties
`fig.update_traces(line_color='red', selector=dict(name='trace1'))`	Update specific trace by name
`px.scatter(df, color='category')`	Color points by categorical variable
`px.scatter(df, size='value')`	Size points by numerical variable
`px.scatter(df, hover_data=['col1', 'col2'])`	Add custom hover information
`px.scatter(df, facet_col='category')`	Create small multiples by column
`px.scatter(df, facet_row='cat1', facet_col='cat2')`	Create 2D grid of subplots
`px.scatter(df, trendline='ols')`	Add ordinary least squares trendline
`px.scatter(df, marginal_x='histogram')`	Add marginal histogram on x-axis
`px.scatter(df, color_continuous_scale='Viridis')`	Set continuous color scale

Advanced Usage - Subplots and Multiple Axes

Command	Description
`from plotly.subplots import make_subplots`	Import subplot functionality
`make_subplots(rows=2, cols=2)`	Create 2x2 subplot grid
`make_subplots(specs=[[{"secondary_y": True}]])`	Create figure with secondary y-axis
`fig.add_trace(trace, row=1, col=2)`	Add trace to specific subplot
`make_subplots(subplot_titles=('Plot 1', 'Plot 2'))`	Create subplots with titles
`make_subplots(shared_xaxes=True)`	Create subplots sharing x-axis
`make_subplots(vertical_spacing=0.1)`	Set vertical spacing between subplots
`fig.update_xaxes(matches='x')`	Synchronize x-axes across subplots
`fig.update_yaxes(title_text='Y1', secondary_y=False)`	Update primary y-axis
`fig.update_yaxes(title_text='Y2', secondary_y=True)`	Update secondary y-axis

Advanced Usage - Annotations and Shapes

Command	Description
`fig.add_annotation(x=2, y=5, text='Note')`	Add text annotation at coordinates
`fig.add_annotation(showarrow=True, arrowhead=2)`	Add annotation with arrow
`fig.add_shape(type='rect', x0=1, y0=2, x1=3, y1=4)`	Add rectangle shape
`fig.add_shape(type='line', x0=0, y0=0, x1=5, y1=5)`	Add line shape
`fig.add_shape(type='circle', x0=1, y0=1, x1=3, y1=3)`	Add circle shape
`fig.add_hline(y=5, line_dash='dash')`	Add horizontal reference line
`fig.add_vline(x=3, line_color='red')`	Add vertical reference line
`fig.add_hrect(y0=2, y1=4, fillcolor='blue', opacity=0.2)`	Add horizontal rectangle region
`fig.add_vrect(x0=1, x1=2, fillcolor='green', opacity=0.2)`	Add vertical rectangle region

Configuration - Layout Options

# Complete layout configuration
fig.update_layout(
    title=dict(
        text='Chart Title',
        font=dict(size=24, color='navy'),
        x=0.5,  # Center title
        xanchor='center'
    ),
    xaxis=dict(
        title='X Axis Label',
        showgrid=True,
        gridcolor='lightgray',
        zeroline=True,
        showline=True,
        linecolor='black'
    ),
    yaxis=dict(
        title='Y Axis Label',
        showgrid=True,
        gridcolor='lightgray',
        range=[0, 100]  # Fixed range
    ),
    plot_bgcolor='rgba(0,0,0,0)',  # Transparent background
    paper_bgcolor='white',
    font=dict(family='Arial', size=12, color='black'),
    showlegend=True,
    legend=dict(
        x=1,
        y=1,
        xanchor='right',
        yanchor='top',
        bgcolor='rgba(255,255,255,0.5)'
    ),
    hovermode='closest',  # or 'x', 'y', 'x unified'
    width=800,
    height=600,
    margin=dict(l=50, r=50, t=50, b=50)
)

Configuration - Trace Styling

# Scatter plot styling
fig.update_traces(
    marker=dict(
        size=10,
        color='blue',
        opacity=0.7,
        line=dict(width=2, color='darkblue'),
        symbol='circle'  # or 'square', 'diamond', 'cross', etc.
    ),
    line=dict(
        color='red',
        width=3,
        dash='dash'  # or 'dot', 'dashdot', 'solid'
    ),
    textposition='top center',
    textfont=dict(size=12, color='black')
)

# Bar chart styling
fig.update_traces(
    marker=dict(
        color='lightblue',
        line=dict(color='darkblue', width=1.5)
    ),
    texttemplate='%{y:.2f}',
    textposition='outside'
)

Configuration - Renderer Settings

import plotly.io as pio

# Set default renderer
pio.renderers.default = 'browser'  # or 'notebook', 'jupyterlab', 'vscode'

# Check available renderers
print(pio.renderers)

# Set default template
pio.templates.default = 'plotly_dark'

# Available templates: plotly, plotly_white, plotly_dark, ggplot2, 
# seaborn, simple_white, none

Common Use Cases

Use Case 1: Time Series Analysis with Range Slider

import plotly.express as px
import pandas as pd

# Create time series data
df = pd.DataFrame({
    'date': pd.date_range('2023-01-01', periods=365),
    'value': np.random.randn(365).cumsum()
})

# Create interactive time series
fig = px.line(df, x='date', y='value', title='Time Series with Range Slider')

# Add range slider and buttons
fig.update_xaxes(
    rangeslider_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(count=1, label="YTD", step="year", stepmode="todate"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)

fig.show()

Use Case 2: Multi-Metric Dashboard with Subplots

from plotly.subplots import make_subplots
import plotly.graph_objects as go

# Create subplot grid
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Sales Trend', 'Category Distribution', 
                    'Regional Performance', 'Monthly Comparison'),
    specs=[[{'type': 'scatter'}, {'type': 'bar'}],
           [{'type': 'pie'}, {'type': 'box'}]]
)

# Add line chart
fig.add_trace(
    go.Scatter(x=[1,2,3,4], y=[10,15,13,17], mode='lines+markers', name='Sales'),
    row=1, col=1
)

# Add bar chart
fig.add_trace(
    go.Bar(x=['A','B','C'], y=[20,30,25], name='Categories'),
    row=1, col=2
)

# Add pie chart
fig.add_trace(
    go.Pie(labels=['East','West','North','South'], values=[30,25,20,25]),
    row=2, col=1
)

# Add box plot
fig.add_trace(
    go.Box(y=[10,12,15,18,20,22,25,28,30], name='Distribution'),
    row=2, col=2
)

fig.update_layout(height=800, showlegend=False, title_text='Business Dashboard')
fig.show()

Use Case 3: Correlation Heatmap with Annotations

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

# Create sample correlation matrix
df = pd.DataFrame(np.random.randn(100, 5), columns=['A', 'B', 'C', 'D', 'E'])
corr_matrix = df.corr()

# Create annotated heatmap
fig = px.imshow(
    corr_matrix,
    text_auto='.2f',  # Show values with 2 decimal places
    aspect='auto',
    color_continuous_scale='RdBu_r',  # Red-Blue reversed
    zmin=-1, zmax=1  # Correlation range
)

fig.update_layout(
    title='Correlation Matrix',
    xaxis_title='Variables',
    yaxis_title='Variables'
)

fig.show()

Use Case 4: Interactive 3D Scatter with Custom Hover

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

# Create 3D dataset
df = pd.DataFrame({
    'x': np.random.randn(200),
    'y': np.random.randn(200),
    'z': np.random.randn(200),
    'category': np.random.choice(['A', 'B', 'C'], 200),
    'size': np.random.randint(5, 30, 200),
    'label': [f'Point {i}' for i in range(200)]
})

# Create 3D scatter
fig = px.scatter_3d(
    df, x='x', y='y', z='z',
    color='category',
    size='size',
    hover_data=['label', 'size'],
    title='3D Scatter Plot with Categories'
)

# Customize 3D scene
fig.update_layout(
    scene=dict(
        xaxis_title='X Axis',
        yaxis_title='Y Axis',
        zaxis_title='Z Axis',
        camera=dict(eye=dict(x=1.5, y=1.5, z=1.5))
    )
)

fig.show()

Use Case 5: Animated Scatter Plot

import plotly.express as px

# Create data with time dimension
df = pd.DataFrame({
    'year': [2020]*5 + [2021]*5 + [2022]*5,
    'country': ['A', 'B', 'C', 'D', 'E'] * 3,
    'gdp': np.random.randint(1000, 5000, 15),
    'population': np.random.randint(10, 100, 15)
})

# Create animated scatter
fig = px.scatter(
    df, x='gdp', y='population',
    animation_frame='year',
    animation_group='country',
    size='population',
    color='country',
    hover_name='country',
    size_max=55,
    range_x=[0, 6000],
    range_y=[0, 120],
    title='GDP vs Population Over Time'
)

fig.update_layout(transition_duration=500)
fig.show()

Best Practices

Use Plotly Express for rapid prototyping: Start with px for quick visualizations, then switch to go (Graph Objects) when you need fine-grained control over every element
Leverage templates for consistency: Set a default template with pio.templates.default to maintain consistent styling across all visualizations in your project
Optimize for large datasets: For datasets with >10k points, consider using scattergl instead of scatter, or use datashader for pre-aggregation before plotting
Make visualizations accessible: Always include axis labels, titles, and legends. Use colorblind-friendly palettes like plotly.colors.qualitative.Safe
Export considerations: Use kaleido for high-quality static exports. For web embedding, use fig.write_html(include_plotlyjs='cdn') to reduce file size
Interactive features: Enable hovermode='x unified' for time series to compare multiple traces at the same x-value simultaneously
Memory management: When creating multiple figures in loops, explicitly call fig = None or use context managers to free memory
Version control: Save figure configurations as JSON with fig.to_json() for reproducibility and version control of visualizations

Troubleshooting

Issue	Solution
Figure not displaying in Jupyter	Install widgets: `pip install "ipywidgets>=7.5"` and restart kernel. Check renderer: `pio.renderers.default = 'notebook'`
”No module named ‘plotly’” error	Install plotly: `pip install plotly`. If using conda, try: `conda install -c conda-forge plotly`
Static image export fails	Install kaleido: `pip install -U kaleido`. For older systems, try: `conda install -c plotly plotly-orca`
Slow rendering with large datasets	Switch to WebGL: use `go.Scattergl()` instead of `go.Scatter()`, or downsample data before plotting
Figures too small in output	Set dimensions explicitly: `fig.update_layout(width=1200, height=800)` or use `fig.show(config={'responsive': True})`
Legend overlapping plot area	Reposition legend: `fig.update_layout(legend=dict(x=1.05, y=1, xanchor='left'))` or set `showlegend=False`
Hover labels cut off	Increase margins: `fig.update_layout(margin=dict(l=100, r=100, t=100, b=100))`
Colors not showing correctly	Ensure color column is correct type. For categorical: convert to string. For continuous: ensure numeric values
Animation not working	Verify `animation_frame` column exists and has multiple unique values. Check data is sorted by animation frame
PDF export has missing elements	Use SVG as intermediate: `fig.write_image('file.svg')` then convert to PDF, or use `fig.write_image('file.pdf', engine='kaleido')`
Memory error with subplots	Create subplots incrementally, clearing unused figures. Use `del fig` and garbage collection: `import gc; gc.collect()`