DataFrame

Interactive dataframe viewer with transformations

mo.ui.dataframe

Display and interact with dataframes from pandas, polars, pyarrow, and more.

Signature

mo.ui.dataframe(
    df: DataFrameType,
    on_change: Callable[[DataFrameType], None] | None = None,
    page_size: int = 5,
    limit: int | None = None,
    show_download: bool = True,
    *,
    format_mapping: dict | None = None,
    download_csv_encoding: str | None = None,
    download_csv_separator: str | None = None,
    download_json_ensure_ascii: bool = True,
    lazy: bool | None = None
)

Parameters

df · DataFrame · required

Pandas, Polars, PyArrow, or Ibis dataframe

on_change · Callable

Callback when dataframe is transformed

page_size · int · default: "5"

Rows per page

limit · int

Maximum rows to display

show_download · bool · default: "True"

Show download button

format_mapping · dict

Custom column formatters

lazy · bool

Use lazy evaluation for large dataframes

Features

• Filter: Filter rows by column values
• Sort: Sort by one or more columns
• Search: Full-text search across all columns
• Select columns: Choose which columns to display
• Aggregate: Group by and aggregate data
• Download: Export as CSV, JSON, or Parquet
• Code generation: Generate Python code for transformations

Examples

import marimo as mo
import pandas as pd

# Basic dataframe
df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [30, 25, 35],
    "salary": [70000, 50000, 90000]
})

df_viewer = mo.ui.dataframe(df)
df_viewer

# Access transformed dataframe
transformed = df_viewer.value
mo.md(f"Rows: {len(transformed)}")

# With custom formatting
from datetime import date

df = pd.DataFrame({
    "date": [date(2024, 1, 1), date(2024, 1, 2)],
    "value": [1234.56, 7890.12]
})

df_viewer = mo.ui.dataframe(
    df,
    format_mapping={
        "value": "${:,.2f}"
    }
)

For very large dataframes, set lazy=True to use lazy evaluation and improve performance.