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Interactive dataframes

marimo makes you more productive when working with dataframes.

marimo integrates with Pandas and Polars dataframes natively.

Displaying dataframes

marimo lets you page through, search, sort, and filter dataframes, making it extremely easy to get a feel for your data.

marimo brings dataframes to life.

Display dataframes by including them in the last expression of the cell, just like any other object.

import pandas as pd

df = pd.read_json(
    "https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json"
)
df

import polars as pl

df = pl.read_json(
    "https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json"
)
df

To opt out of the rich dataframe viewer, use mo.plain:

df = pd.read_json(
"https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json"
)
mo.plain(df)

df = pl.read_json(
"https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json"
)
mo.plain(df)

Transforming dataframes

No-code transformations

Use mo.ui.dataframe to interactively transform a dataframe with a GUI, no coding required. When you're done, you can copy the code that the GUI generated for you and paste it into your notebook.

Build transformations using a GUI
# Cell 1
import marimo as mo
import pandas as pd

df = pd.DataFrame({"person": ["Alice", "Bob", "Charlie"], "age": [20, 30, 40]})
transformed_df = mo.ui.dataframe(df)
transformed_df

# Cell 2
# transformed_df.value holds the transformed dataframe
transformed_df.value

# Cell 1
import marimo as mo
import polars as pl

df = pl.DataFrame({"person": ["Alice", "Bob", "Charlie"], "age": [20, 30, 40]})
transformed_df = mo.ui.dataframe(df)
transformed_df

# Cell 2
# transformed_df.value holds the transformed dataframe
transformed_df.value
Copy the code of the transformation

Custom filters

Create custom filters with marimo UI elements, like sliders and dropdowns.

# Cell 1 - create a dataframe
df = pd.DataFrame({"person": ["Alice", "Bob", "Charlie"], "age": [20, 30, 40]})

# Cell 2 - create a filter
age_filter = mo.ui.slider(start=0, stop=100, value=50, label="Max age")
age_filter

# Cell 3 - display the transformed dataframe
filtered_df = df[df["age"] < age_filter.value]
mo.ui.table(filtered_df)

import marimo as mo
import polars as pl

df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "David"],
    "age": [25, 30, 35, 40],
    "city": ["New York", "London", "Paris", "Tokyo"]
})

age_filter = mo.ui.slider.from_series(df["age"], label="Max age")
city_filter = mo.ui.dropdown.from_series(df["city"], label="City")

mo.hstack([age_filter, city_filter])

# Cell 2
filtered_df = df.filter((pl.col("age") <= age_filter.value) & (pl.col("city") == city_filter.value))
mo.ui.table(filtered_df)

Select dataframe rows

Display dataframes as interactive, selectable charts using mo.ui.altair_chart or mo.ui.plotly, or as a row-selectable table with mo.ui.table. Select points in the chart, or select a table row, and your selection is automatically sent to Python as a subset of the original dataframe.

Select rows in a table, get them back as a dataframe
# Cell 1 - display a dataframe
import marimo as mo
import pandas as pd

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

# Cell 2 - display the selection
table.value

# Cell 1 - display a dataframe
import marimo as mo
import polars as pl

df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
table = mo.ui.table(df, selection="multi")
table

# Cell 2 - display the selection
table.value

Dataframe panels

Dataframe outputs in marimo come with several panels to help you visualize, explore, and page through your data interactively. These panels are accessible via toggles at the bottom-left of a dataframe output. If you need further control, after opening a panel you can

  • pin the panel to the side of your editor for persistent access;
  • toggle focus to automatically display the currently focused dataframe in the panel.
Note

Toggles are visible when editing notebooks (with marimo edit ...) but not when running notebooks as apps (with marimo run ...), except for the row viewer which is available in both.

Row viewer panel

To inspect individual rows, open the row viewer. This presents a vertical view of the selected row.

  • Press Space to select/deselect the current row
  • Use arrow keys (← →) to navigate between rows
  • Click on any row in the dataframe to view its data in the panel

Column explorer panel

To explore your data, open the column explorer where you can find summary statistics and charts for each column. Click the + button to add the chart code to a new cell.

This requires the altair package to be installed. For large dataframes, vegafusion is also needed to render charts. To use the generated Python code, enable vegafusion in your notebook:

import altair

altair.data_transformers.enable("vegafusion")

Chart builder

The chart builder toggle lets you rapidly develop charts using a GUI, while also generating Python code to insert in your notebook. Refer to the chart builder guide for more details.

Example notebook

For a comprehensive example of using Polars with marimo, check out our Polars example notebook.

Run it with:

marimo edit https://raw.githubusercontent.com/marimo-team/marimo/main/examples/third_party/polars/polars_example.py