Interactive dataframes¶

marimo makes you more productive when working with dataframes.

Display dataframes in a rich, interactive table and chart views
Transform dataframes with filters, groupbys, aggregations, and more, no code required
Select data from tables or charts and get selections back in Python as 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.

pandaspolars

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:

pandaspolars

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.

pandaspolars

# 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

Custom filters¶

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

pandaspolars

# 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

pandaspolars

# 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