> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/marimo-team/marimo/llms.txt
> Use this file to discover all available pages before exploring further.

# DataFrame

> Interactive dataframe viewer with transformations

# mo.ui.dataframe

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

## Signature

```python theme={null}
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

<ParamField path="df" type="DataFrame" required>
  Pandas, Polars, PyArrow, or Ibis dataframe
</ParamField>

<ParamField path="on_change" type="Callable">
  Callback when dataframe is transformed
</ParamField>

<ParamField path="page_size" type="int" default="5">
  Rows per page
</ParamField>

<ParamField path="limit" type="int">
  Maximum rows to display
</ParamField>

<ParamField path="show_download" type="bool" default="True">
  Show download button
</ParamField>

<ParamField path="format_mapping" type="dict">
  Custom column formatters
</ParamField>

<ParamField path="lazy" type="bool">
  Use lazy evaluation for large dataframes
</ParamField>

## 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

```python theme={null}
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
```

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

```python theme={null}
# 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}"
    }
)
```

<Tip>
  For very large dataframes, set `lazy=True` to use lazy evaluation and improve performance.
</Tip>