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The data explorer provides an interactive way to quickly visualize and understand your datasets with automatically generated charts.

​
Overview

mo.ui.data_explorer() creates an interactive visualization tool that:
  • Suggests appropriate chart types based on your data
  • Allows drag-and-drop encoding of visual channels
  • Updates visualizations in real-time
  • Works with pandas, polars, and other dataframe libraries

​
Basic Usage

import marimo as mo
import pandas as pd

df = pd.DataFrame({
    "category": ["A", "B", "C", "A", "B"],
    "value": [10, 20, 15, 25, 30],
    "date": pd.date_range("2024-01-01", periods=5),
    "size": [100, 200, 150, 175, 225]
})

explorer = mo.ui.data_explorer(df)
explorer
The data explorer appears with:
  • Chart type selector (bar, line, scatter, etc.)
  • Encoding channels (x, y, color, size, etc.)
  • Interactive preview

​
Pre-configured Charts

Start with specific encodings: 
import marimo as mo
import pandas as pd

df = pd.DataFrame({
    "month": ["Jan", "Feb", "Mar", "Apr"],
    "revenue": [100, 150, 120, 180],
    "expenses": [80, 90, 85, 95],
    "region": ["North", "North", "South", "South"]
})

# Start with a pre-configured chart
explorer = mo.ui.data_explorer(
    df,
    x="month",
    y="revenue",
    color="region"
)

​
Available Encodings

The data explorer supports multiple visual encodings:

Position

  • x - X-axis position
  • y - Y-axis position
  • row - Facet by row
  • column - Facet by column

Marks

  • color - Color encoding
  • size - Size of marks
  • shape - Shape of points

​
Example: Multi-dimensional Visualization

import marimo as mo
import pandas as pd

df = pd.DataFrame({
    "x_val": range(20),
    "y_val": [i**2 for i in range(20)],
    "category": ["A", "B"] * 10,
    "size_val": [i*10 for i in range(20)],
    "shape_val": ["circle", "square", "triangle"] * 6 + ["circle", "square"]
})

explorer = mo.ui.data_explorer(
    df,
    x="x_val",
    y="y_val",
    color="category",
    size="size_val",
    shape="shape_val"
)

​
Accessing the Chart Specification

Retrieve the chart configuration: 
import marimo as mo

df = pd.DataFrame({
    "x": [1, 2, 3],
    "y": [4, 5, 6]
})

explorer = mo.ui.data_explorer(df, x="x", y="y")
In another cell: 
# Get the current encoding
encoding = explorer.value

mo.md(f"""
## Current Chart Configuration

- X-axis: {encoding.get('x', 'None')}
- Y-axis: {encoding.get('y', 'None')}
- Color: {encoding.get('color', 'None')}
""")

​
Supported Data Types

The data explorer works with any dataframe library supported by narwhals: 
import pandas as pd
import marimo as mo

df = pd.DataFrame({
    "a": [1, 2, 3],
    "b": [4, 5, 6]
})

mo.ui.data_explorer(df)

​
Chart Types

The data explorer automatically suggests appropriate chart types based on your data and selected encodings:
  • Bar charts - For categorical comparisons
  • Line charts - For temporal data
  • Scatter plots - For correlations
  • Histograms - For distributions
  • Box plots - For statistical summaries
  • Heatmaps - For matrix data
Users can override the suggested type using the chart type selector in the UI.

​
Interactive Features

1
Drag and Drop
2
Drag column names from the data panel to encoding channels (x, y, color, etc.)
3
Change Chart Type
4
Click the chart type selector to switch between visualization types.
5
Adjust Encodings
6
Click on encoding channels to change:
7
  • Aggregation functions (sum, mean, count, etc.)
  • Data type interpretation (quantitative, temporal, nominal)
  • Binning for continuous variables
    • 8
    Download Chart
    9
    Export visualizations as PNG or SVG.

    ​
    Reactive Updates

    The data explorer is reactive - when the input dataframe changes, the visualization automatically updates: 
    import marimo as mo
import pandas as pd

# Slider to filter data
max_value = mo.ui.slider(10, 100, value=50, label="Max Value")
    In another cell: 
    # Filtered data
df_filtered = df[df["value"] <= max_value.value]

# Explorer updates when slider changes
mo.ui.data_explorer(df_filtered, x="category", y="value")

    ​
    Combining with Other UI Elements

    Create dashboards by combining the data explorer with other components: 
    import marimo as mo
import pandas as pd

df = pd.DataFrame({
    "product": ["A", "B", "C", "D"],
    "sales": [100, 200, 150, 300],
    "region": ["North", "South", "North", "South"]
})

# Region selector
region = mo.ui.dropdown(
    options=["All", "North", "South"],
    value="All",
    label="Region"
)
    In another cell: 
    # Filter data based on selection
df_filtered = df if region.value == "All" else df[df["region"] == region.value]

# Display region selector and explorer
mo.vstack([
    region,
    mo.ui.data_explorer(df_filtered, x="product", y="sales")
])

    ​
    Performance Considerations

    For large datasets (100k+ rows), the data explorer uses sampling to maintain interactivity.
    • Data is automatically sampled for preview
    • Full dataset is used for aggregations
    • Consider pre-aggregating very large datasets

    ​
    Under the Hood

    The data explorer uses:
    • Vega-Lite for declarative visualizations
    • Altair (via marimo’s chart transformer) for efficient rendering
    • CSV data transformation for browser compatibility

    ​
    Example: Sales Dashboard

    import marimo as mo
import pandas as pd
import numpy as np

# Generate sample sales data
np.random.seed(42)
dates = pd.date_range("2024-01-01", periods=100)
df = pd.DataFrame({
    "date": dates,
    "revenue": np.random.randint(1000, 5000, 100),
    "customers": np.random.randint(10, 100, 100),
    "product": np.random.choice(["Widget", "Gadget", "Tool"], 100),
    "region": np.random.choice(["North", "South", "East", "West"], 100)
})

# Interactive explorer
explorer = mo.ui.data_explorer(
    df,
    x="date",
    y="revenue",
    color="product"
)

explorer
    Users can now:
    • Switch to different chart types
    • Add faceting by region
    • Change aggregation methods
    • Explore temporal patterns
    All without writing any visualization code!

    ​
    Comparison with Other Tools

    FeatureData ExplorerAltair ChartPlotly
    No code required✓✗✗
    Automatic suggestions✓✗✗
    Drag-and-drop✓✗✗
    Full customizationLimited✓✓
    Export code✗✓✓
    Use the data explorer for quick exploration, then switch to mo.ui.altair_chart() or mo.ui.plotly() for production visualizations.

    ​
    Tips

    Fast Exploration: Use the data explorer when you first receive a dataset to understand its structure and relationships.
    Prototyping: Quickly prototype visualizations before writing custom chart code.
    Presentations: Great for interactive demos where the audience can explore data live.

    ​
    Limitations

    • Chart customization is limited compared to programmatic approaches
    • Complex visualizations may require mo.ui.altair_chart() or mo.ui.plotly()
    • No direct access to underlying Vega-Lite specification for modification
    For advanced use cases, consider combining the data explorer for initial exploration with custom charts for final presentation.

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