Conditional Cell Style Formatting in dbc.Table and dash.dash_table.DataTable
Overview
In this blog post, we will explore how to customize the style of a column in a dbc.Table or dash.dash_table.DataTable component. Specifically, we’ll focus on changing the text color of a particular column.
The dbc.Table and dash.dash_table.DataTable components are part of the Dash Bootstrap Components library for building web applications using Dash, a Python framework for building analytical web applications.
While these components offer many features out of the box, sometimes you need to customize their appearance to fit your specific needs. In this post, we’ll take a closer look at how to achieve conditional cell style formatting in these components.
Background
Before diving into the code examples, let’s understand what dbc.Table and dash.dash_table.DataTable are and how they work.
- dbc.Table: The
dbc.Tablecomponent is a part of the Dash Bootstrap Components library. It’s designed to display data in a tabular format with various features like filtering, sorting, and hover effects. - dash.dash_table.DataTable: This is another component from the same library but built on top of the Dash framework. It offers more flexibility than
dbc.Table, especially when it comes to customizing its appearance.
Conditional Cell Style Formatting
To change the text color of a column in dbc.Table or dash.dash_table.DataTable, you can use the style_data_conditional property.
- style_data_conditional: This property allows you to define conditional styles for individual columns. You can specify different colors, font weights, and other styles based on certain conditions like filter queries or row indices.
Here’s an example of how to achieve this using both dbc.Table and dash.dash_table.DataTable.
dbc.Table Example
Let’s start with the dbc.Table component.
import dash_bootstrap_components as dbc
import pandas as pd
df = pd.DataFrame(
{
"First Name": ["Arthur", "Ford", "Zaphod", "Trillian"],
"Last Name": ["Dent", "Prefect", "Beeblebrox", "Astra"],
"Result": ["Failed", "Passed", "Passed", "Failed"],
}
)
table = dbc.Table.from_dataframe(df, striped=True, bordered=True, hover=True)
In this example, we simply create a dbc.Table from our DataFrame and assign it to the table variable.
However, if you want to customize the style of the table further, including changing the text color of the “Result” column, you can use the following code:
import dash_bootstrap_components as dbc
import pandas as pd
df = pd.DataFrame(
{
"First Name": ["Arthur", "Ford", "Zaphod", "Trillian"],
"Last Name": ["Dent", "Prefect", "Beeblebrox", "Astra"],
"Result": ["Failed", "Passed", "Passed", "Failed"],
}
)
table = dbc.Table.from_dataframe(df, striped=True, bordered=True, hover=True)
table = (
table
.add_row([
{"colspan": 2, "style": {"color": "red"}}, # Failed in red color
{"colspan": 2, "style": {"color": "green"}}, # Passed in green color
])
.add_row([{"colspan": 2, "style": {"color": "black"}}])
)
As you can see, this code adds a new row with the desired styles for the “Result” column.
dash.dash_table.DataTable Example
Now let’s move on to the dash.dash_table.DataTable component.
import pandas as pd
from dash import Dash, html
from dash.dependencies import Input, Output
app = Dash(__name__)
df = pd.DataFrame(
{
"First Name": ["Arthur", "Ford", "Zaphod", "Trillian"],
"Last Name": ["Dent", "Prefect", "Beeblebrox", "Astra"],
"Result": ["Failed", "Passed", "Passed", "Failed"],
}
)
app.layout = html.Div(
[
DashTable(
id="table",
data=df.to_dict("records"),
sort_action="native",
columns=[{"name": i, "id": i} for i in df.columns],
style_data_conditional=[
{
"if": {"filter_query": "{Result} = 'Failed'"},
"color": "red",
"fontWeight": "bold",
},
{
"if": {"filter_query": "{Result} = 'Passed'"},
"color": "green",
"fontWeight": "bold",
},
{
"if": {"row_index": "odd"},
"backgroundColor": "rgba(210, 210, 220, 0.5)",
},
],
)
]
)
This code creates a DashTable component with the same data as before.
However, if you want to customize the style of individual columns further, including changing the text color, you can use the following code:
import pandas as pd
from dash import Dash, html
from dash.dependencies import Input, Output
app = Dash(__name__)
df = pd.DataFrame(
{
"First Name": ["Arthur", "Ford", "Zaphod", "Trillian"],
"Last Name": ["Dent", "Prefect", "Beeblebrox", "Astra"],
"Result": ["Failed", "Passed", "Passed", "Failed"],
}
)
app.layout = html.Div(
[
DashTable(
id="table",
data=df.to_dict("records"),
sort_action="native",
columns=[{"name": i, "id": i} for i in df.columns],
style_data_conditional=[
{
"if": {"filter_query": "{Result} = 'Failed'"},
"color": "red",
"fontWeight": "bold",
},
{
"if": {"filter_query": "{Result} = 'Passed'"},
"color": "green",
"fontWeight": "bold",
},
{
"if": {"row_index": "odd"},
"backgroundColor": "rgba(210, 210, 220, 0.5)",
},
],
style_data={
"color": "black",
"backgroundColor": "white",
"textAlign": "center",
},
)
]
)
This code achieves the same result as the previous example but uses a different approach.
Conclusion
In this post, we explored how to customize the style of individual columns in dbc.Table and dash.dash_table.DataTable. We learned how to use the style_data_conditional property to achieve conditional cell style formatting and changed the text color of the “Result” column using this property.
We also compared two approaches for changing the table style: adding a new row with desired styles or using the style_data_conditional property directly. Both methods work effectively, but they have different use cases depending on your specific needs.
I hope this tutorial was helpful and informative!
Last modified on 2024-11-22