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 pandas 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)(marimo.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 pandas 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

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