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  1. Contents
  2. st.data_editor
  3. Configuring columns
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Tip

This page only contains information on the st.data_editor API. For an overview of working with dataframes and to learn more about the data editor's capabilities and limitations, read Dataframes.

st.data_editor

Display a data editor widget.

The data editor widget allows you to edit dataframes and many other data structures in a table-like UI.

Function signature[source]

st.data_editor(data, *, width="stretch", height="auto", use_container_width=None, hide_index=None, column_order=None, column_config=None, num_rows="fixed", disabled=False, key=None, on_change=None, args=None, kwargs=None, row_height=None)

Parameters

data (Anything supported by st.dataframe)

The data to edit in the data editor.

Note

  • Styles from pandas.Styler will only be applied to non-editable columns.
  • Text and number formatting from column_config always takes precedence over text and number formatting from pandas.Styler.
  • Mixing data types within a column can make the column uneditable.
  • Additionally, the following data types are not yet supported for editing: complex, tuple, bytes, bytearray, memoryview, dict, set, frozenset, fractions.Fraction, pandas.Interval, and pandas.Period.
  • To prevent overflow in JavaScript, columns containing datetime.timedelta and pandas.Timedelta values will default to uneditable, but this can be changed through column configuration.

width ("stretch", "content", or int)

The width of the data editor. This can be one of the following:

  • "stretch" (default): The width of the editor matches the width of the parent container.
  • "content": The width of the editor matches the width of its content, but doesn't exceed the width of the parent container.
  • An integer specifying the width in pixels: The editor has a fixed width. If the specified width is greater than the width of the parent container, the width of the editor matches the width of the parent container.

height (int or "auto")

The height of the data editor. This can be one of the following:

  • "auto" (default): Streamlit sets the height to show at most ten rows.
  • An integer specifying the height in pixels: The editor has a fixed height.

Vertical scrolling within the data editor is enabled when the height does not accommodate all rows.

use_container_width (bool)

delete

use_container_width is deprecated and will be removed in a future release. For use_container_width=True, use width="stretch".

Whether to override width with the width of the parent container. If this is True (default), Streamlit sets the width of the data editor to match the width of the parent container. If this is False, Streamlit sets the data editor's width according to width.

hide_index (bool or None)

Whether to hide the index column(s). If hide_index is None (default), the visibility of index columns is automatically determined based on the data.

column_order (Iterable[str] or None)

The ordered list of columns to display. If this is None (default), Streamlit displays all columns in the order inherited from the underlying data structure. If this is a list, the indicated columns will display in the order they appear within the list. Columns may be omitted or repeated within the list.

For example, column_order=("col2", "col1") will display "col2" first, followed by "col1", and will hide all other non-index columns.

column_order does not accept positional column indices and can't move the index column(s).

column_config (dict or None)

Configuration to customize how columns are displayed. If this is None (default), columns are styled based on the underlying data type of each column.

Column configuration can modify column names, visibility, type, width, format, editing properties like min/max, and more. If this is a dictionary, the keys are column names (strings) and/or positional column indices (integers), and the values are one of the following:

  • None to hide the column.
  • A string to set the display label of the column.
  • One of the column types defined under st.column_config. For example, to show a column as dollar amounts, use st.column_config.NumberColumn("Dollar values", format="$ %d"). See more info on the available column types and config options here.

To configure the index column(s), use "_index" as the column name, or use a positional column index where 0 refers to the first index column.

num_rows ("fixed" or "dynamic")

Specifies if the user can add and delete rows in the data editor. If "fixed", the user cannot add or delete rows. If "dynamic", the user can add and delete rows in the data editor, but column sorting is disabled. Defaults to "fixed".

disabled (bool or Iterable[str | int])

Controls the editing of columns. This can be one of the following:

  • False (default): All columns that support editing are editable.
  • True: All columns are disabled for editing.
  • An Iterable of column names and/or positional indices: The specified columns are disabled for editing while the remaining columns are editable where supported. For example, disabled=["col1", "col2"] will disable editing for the columns named "col1" and "col2".

To disable editing for the index column(s), use "_index" as the column name, or use a positional column index where 0 refers to the first index column.

key (str)

An optional string to use as the unique key for this widget. If this is omitted, a key will be generated for the widget based on its content. No two widgets may have the same key.

on_change (callable)

An optional callback invoked when this data_editor's value changes.

args (list or tuple)

An optional list or tuple of args to pass to the callback.

kwargs (dict)

An optional dict of kwargs to pass to the callback.

row_height (int or None)

The height of each row in the data editor in pixels. If row_height is None (default), Streamlit will use a default row height, which fits one line of text.

Returns

(pandas.DataFrame, pandas.Series, pyarrow.Table, numpy.ndarray, list, set, tuple, or dict.)

The edited data. The edited data is returned in its original data type if it corresponds to any of the supported return types. All other data types are returned as a pandas.DataFrame.

Examples

Example 1: Basic usage

import pandas as pd
import streamlit as st

df = pd.DataFrame(
    [
        {"command": "st.selectbox", "rating": 4, "is_widget": True},
        {"command": "st.balloons", "rating": 5, "is_widget": False},
        {"command": "st.time_input", "rating": 3, "is_widget": True},
    ]
)
edited_df = st.data_editor(df)

favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
st.markdown(f"Your favorite command is **{favorite_command}** 🎈")
Built with Streamlit 🎈

Example 2: Allowing users to add and delete rows

You can allow your users to add and delete rows by setting num_rows to "dynamic":

import streamlit as st
import pandas as pd

df = pd.DataFrame(
    [
        {"command": "st.selectbox", "rating": 4, "is_widget": True},
        {"command": "st.balloons", "rating": 5, "is_widget": False},
        {"command": "st.time_input", "rating": 3, "is_widget": True},
    ]
)
edited_df = st.data_editor(df, num_rows="dynamic")

favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
st.markdown(f"Your favorite command is **{favorite_command}** 🎈")
Built with Streamlit 🎈

Example 3: Data editor configuration

You can customize the data editor via column_config, hide_index, column_order, or disabled:

import pandas as pd
import streamlit as st

df = pd.DataFrame(
    [
        {"command": "st.selectbox", "rating": 4, "is_widget": True},
        {"command": "st.balloons", "rating": 5, "is_widget": False},
        {"command": "st.time_input", "rating": 3, "is_widget": True},
    ]
)
edited_df = st.data_editor(
    df,
    column_config={
        "command": "Streamlit Command",
        "rating": st.column_config.NumberColumn(
            "Your rating",
            help="How much do you like this command (1-5)?",
            min_value=1,
            max_value=5,
            step=1,
            format="%d ⭐",
        ),
        "is_widget": "Widget ?",
    },
    disabled=["command", "is_widget"],
    hide_index=True,
)

favorite_command = edited_df.loc[edited_df["rating"].idxmax()]["command"]
st.markdown(f"Your favorite command is **{favorite_command}** 🎈")
Built with Streamlit 🎈

Configuring columns

You can configure the display and editing behavior of columns in st.dataframe and st.data_editor via the Column configuration API. We have developed the API to let you add images, charts, and clickable URLs in dataframe and data editor columns. Additionally, you can make individual columns editable, set columns as categorical and specify which options they can take, hide the index of the dataframe, and much more.

Built with Streamlit 🎈
Previous: st.dataframeNext: st.column_config
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