Conditional Formatting
Conditional formatting is provided through the style_data_conditional
property. The if keyword provides a set of conditional formatting
statements and the rest of the keywords are camelCased CSS properties.
The if syntax supports several operators, row_index, column_id,
filter_query, column_type, column_editable, and state.
filter_query is the most flexible option when dealing with data.
Here is an example of all operators:
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
df['id'] = df.index
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[
{'name': 'Date', 'id': 'Date', 'type': 'datetime', 'editable': False},
{'name': 'Delivery', 'id': 'Delivery', 'type': 'datetime'},
{'name': 'Region', 'id': 'Region', 'type': 'text'},
{'name': 'Temperature', 'id': 'Temperature', 'type': 'numeric'},
{'name': 'Humidity', 'id': 'Humidity', 'type': 'numeric'},
{'name': 'Pressure', 'id': 'Pressure', 'type': 'any'},
],
editable=True,
style_data_conditional=[
{
'if': {
'column_id': 'Region',
},
'backgroundColor': 'dodgerblue',
'color': 'white'
},
{
'if': {
'filter_query': '{Humidity} > 19 && {Humidity} < 41',
'column_id': 'Humidity'
},
'backgroundColor': 'tomato',
'color': 'white'
},
{
'if': {
'column_id': 'Pressure',
# since using .format, escape { with {{
'filter_query': '{{Pressure}} = {}'.format(df['Pressure'].max())
},
'backgroundColor': '#85144b',
'color': 'white'
},
{
'if': {
'row_index': 5, # number | 'odd' | 'even'
'column_id': 'Region'
},
'backgroundColor': 'hotpink',
'color': 'white'
},
{
'if': {
'filter_query': '{id} = 4', # matching rows of a hidden column with the id, `id`
'column_id': 'Region'
},
'backgroundColor': 'RebeccaPurple'
},
{
'if': {
'filter_query': '{Delivery} > {Date}', # comparing columns to each other
'column_id': 'Delivery'
},
'backgroundColor': '#3D9970'
},
{
'if': {
'column_editable': False # True | False
},
'backgroundColor': 'rgb(240, 240, 240)',
'cursor': 'not-allowed'
},
{
'if': {
'column_type': 'text' # 'text' | 'any' | 'datetime' | 'numeric'
},
'textAlign': 'left'
},
{
'if': {
'state': 'active' # 'active' | 'selected'
},
'backgroundColor': 'rgba(0, 116, 217, 0.3)',
'border': '1px solid rgb(0, 116, 217)'
}
]
)
if __name__ == '__main__':
app.run(debug=True)
Notes:
- filter_query supports different operators depending on the data type
of the column:
=,>,>=,<,<=, andcontainsare supported by
all data types (numeric,text,datetime, andany)- With
contains, the right-hand-side needs to be a string,
so{Date} contains "01"will work but{Date} contains 1will not. datestartswithis supported bydatetimeis nilis supported by all data types-
is blankis supported by all data types -
A column’s default data type is
any column_typerefers to the data type of the column (numeric,text,datetime, andany)column_editablecan be equal toTrueorFalse(new in Dash 1.12.0)statecan be equal to'active'or'selected'(new in Dash 1.12.0). Use this to change
the default pink background and border colors for selected and active cells.row_indexis absolute - if you filter or sort your table,
the 5th row will remain highlighted. Try sorting the columns and
notice how “San Francisco” remains highlighted but “London” does not.column_id,row_index, andheader_indexcan be equal to a scalar
(as above) or a list of values. For example,'column_id': ['Region', 'Pressure']
is valid (new in Dash 1.12.0).DataTablefiltering & conditional formatting
performs faster when specified a list of values vs a list of separateifblocks.- Note
'filter_query': '{Delivery} > {Date}': Filter queries can compare columns
to each other! idis a special hidden column that can be used as an alternative
torow_indexfor highlighting data by index. Since each row has a
uniqueid, the conditional formatting associated with thisid
will remain associated with that data after sorting and filtering.RebeccaPurple,hotpink,DodgerBlue… These are
named CSS colors.
We recommend avoiding the common color names like
red,blue,greenas they look very outdated. For other color
suggestions, see http://clrs.cc/.- Since we’re using
.format, we escape{with\{{and}with\}}. - To highlight a row, omit
column_id. To highlight a particular cell, includecolumn_id. style_cell_conditional(all cells, including headers),
style_header_conditional(header cells),
style_filter_conditional(filter input boxes)
are alternative keywords that can be used for filtering other parts of the table.- Limitation - If the table is editable, then the maximum value could change
if the user edits the table. Since this example hard codes the
maximum value in the filter expression, the highlighting value
would no longer be highlighted. As a workaround, you could update
style_data_conditionalvia a callback wheneverderived_virtual_datachanges.
This limitation applies for any conditional formatting with hardcoded
numbers computed from an expression in Python
(including many of the examples below!).
See plotly/dash-table#755 for updates.
Alternative Highlighting Styles
Instead of highlighting the background cell, you can change the color
of the text, bold the text, add underlines, or style it using any
other CSS property.
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
from dash.dash_table.Format import Format, Sign
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[
{"name": i, "id": i} for i in df.columns
],
style_data_conditional=[
{
'if': {
'filter_query': '{Humidity} > 19 && {Humidity} < 41',
'column_id': 'Humidity'
},
'color': 'tomato',
'fontWeight': 'bold'
},
{
'if': {
'filter_query': '{Pressure} > 19',
'column_id': 'Pressure'
},
'textDecoration': 'underline'
}
]
)
if __name__ == '__main__':
app.run(debug=True)
Special Characters like Emojis, Stars, Checkmarks, Circles
You can copy and paste emoji unicode characters directly into your code.
We recommend copying values from emojipedia, e.g.
https://emojipedia.org/star/.
New unicode emoji characters are released every year and may not be
available in the character sets of your audience’s machines.
The appearance of these icons differs on most operating systems.
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
df['Rating'] = df['Humidity'].apply(lambda x:
'⭐⭐⭐' if x > 30 else (
'⭐⭐' if x > 20 else (
'⭐' if x > 10 else ''
)))
df['Growth'] = df['Temperature'].apply(lambda x: '↗️' if x > 0 else '↘️')
df['Status'] = df['Temperature'].apply(lambda x: '🔥' if x > 0 else '🚒')
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{"name": i, "id": i} for i in df.columns
],
)
if __name__ == '__main__':
app.run(debug=True)
Filtering and Conditional Formatting Recipes
Highlighting the Max Value in a Column
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{"name": i, "id": i} for i in df.columns
],
style_data_conditional=[
{
'if': {
'filter_query': '{{Pressure}} = {}'.format(df['Pressure'].max()),
'column_id': 'Pressure'
},
'backgroundColor': '#FF4136',
'color': 'white'
},
]
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting a Row with the Min Value
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{"name": i, "id": i} for i in df.columns
],
style_data_conditional=[
{
'if': {
'filter_query': '{{Temperature}} = {}'.format(df['Temperature'].min()),
},
'backgroundColor': '#FF4136',
'color': 'white'
},
]
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting the Top Three or Bottom Three Values in a Column
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{"name": i, "id": i} for i in df.columns
],
style_data_conditional=(
[
{
'if': {
'filter_query': '{{Temperature}} = {}'.format(i),
'column_id': 'Temperature',
},
'backgroundColor': '#0074D9',
'color': 'white'
}
for i in df['Temperature'].nlargest(3)
] +
[
{
'if': {
'filter_query': '{{Pressure}} = {}'.format(i),
'column_id': 'Pressure',
},
'backgroundColor': '#7FDBFF',
'color': 'white'
}
for i in df['Pressure'].nsmallest(3)
]
)
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting the Max Value in Every Row
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
def highlight_max_row(df):
df_numeric_columns = df.select_dtypes('number').drop(['id'], axis=1)
return [
{
'if': {
'filter_query': '{{id}} = {}'.format(i),
'column_id': col
},
'backgroundColor': '#3D9970',
'color': 'white'
}
# idxmax(axis=1) finds the max indices of each row
for (i, col) in enumerate(
df_numeric_columns.idxmax(axis=1)
)
]
df['id'] = df.index
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns if i != 'id'],
style_data_conditional=highlight_max_row(df)
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting the Top Two Values in a Row
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
def style_row_by_top_values(df, nlargest=2):
numeric_columns = df.select_dtypes('number').drop(['id'], axis=1).columns
styles = []
for i in range(len(df)):
row = df.loc[i, numeric_columns].sort_values(ascending=False)
for j in range(nlargest):
styles.append({
'if': {
'filter_query': '{{id}} = {}'.format(i),
'column_id': row.keys()[j]
},
'backgroundColor': '#39CCCC',
'color': 'white'
})
return styles
df['id'] = df.index
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns if i != 'id'],
style_data_conditional=style_row_by_top_values(df)
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting the Maximum Value in the Table
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
def style_table_by_max_value(df):
if 'id' in df:
numeric_columns = df.select_dtypes('number').drop(['id'], axis=1)
else:
numeric_columns = df.select_dtypes('number')
max_across_numeric_columns = numeric_columns.max()
max_across_table = max_across_numeric_columns.max()
styles = []
for col in max_across_numeric_columns.keys():
if max_across_numeric_columns[col] == max_across_table:
styles.append({
'if': {
'filter_query': '{{{col}}} = {value}'.format(
col=col, value=max_across_table
),
'column_id': col
},
'backgroundColor': '#39CCCC',
'color': 'white'
})
return styles
df['id'] = df.index
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns if i != 'id'],
style_data_conditional=style_table_by_max_value(df)
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting a Range of Values
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=[
{
'if': {
'filter_query': '{2018} >= 5 && {2018} < 10',
'column_id': '2018'
},
'backgroundColor': '#B10DC9',
'color': 'white'
}
]
)
if __name__ == '__main__':
app.run(debug=True)
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=[
{
'if': {
'filter_query': '{{{col}}} >= 5 && {{{col}}} < 10'.format(col=col),
'column_id': col
},
'backgroundColor': '#B10DC9',
'color': 'white'
} for col in df.columns
]
)
if __name__ == '__main__':
app.run(debug=True)
Let’s break down \{{\{col}}}. We want the final expression to look something like
{2017} > 5 & {2017} < 10 where 2017 is the name of the column.
Since we’re using .format(), we need to escape the brackets,
so {2017} would be {{2017}}. Then, we need to replace 2017 with {col}
for the find-and-replace, so becomes\{{\{col}}}.format(col=col)
Highlighting Top 10% or Bottom 10% of Values by Column
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=[
{
'if': {
'filter_query': '{{{}}} >= {}'.format(col, value),
'column_id': col
},
'backgroundColor': '#B10DC9',
'color': 'white'
} for (col, value) in df.quantile(0.9).iteritems()
]
)
if __name__ == '__main__':
app.run(debug=True)
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns if i != 'id'],
style_data_conditional=[
{
'if': {
'filter_query': '{{{}}} <= {}'.format(col, value),
'column_id': col
},
'backgroundColor': '#B10DC9',
'color': 'white'
} for (col, value) in df.quantile(0.1).iteritems()
]
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting Values above Average and Below Average
Here, the highlighting is done per column.
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=(
[
{
'if': {
'filter_query': '{{{}}} > {}'.format(col, value),
'column_id': col
},
'backgroundColor': '#3D9970',
'color': 'white'
} for (col, value) in df.quantile(0.1).iteritems()
] +
[
{
'if': {
'filter_query': '{{{}}} <= {}'.format(col, value),
'column_id': col
},
'backgroundColor': '#FF4136',
'color': 'white'
} for (col, value) in df.quantile(0.5).iteritems()
]
)
)
if __name__ == '__main__':
app.run(debug=True)
Here, the highlighting is done per table.
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
def highlight_above_and_below_max(df):
df_numeric_columns = df.select_dtypes('number').drop(['id'], axis=1)
mean = df_numeric_columns.mean().mean()
return (
[
{
'if': {
'filter_query': '{{{}}} > {}'.format(col, mean),
'column_id': col
},
'backgroundColor': '#3D9970',
'color': 'white'
} for col in df_numeric_columns.columns
] +
[
{
'if': {
'filter_query': '{{{}}} <= {}'.format(col, mean),
'column_id': col
},
'backgroundColor': '#FF4136',
'color': 'white'
} for col in df_numeric_columns.columns
]
)
df['id'] = df.index
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns if 'id' not in df],
style_data_conditional=highlight_above_and_below_max(df)
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting None, NaN, or Empty String Values
Three filter queries help with empty or blank values:
- {my_column} is nil will match None values
- {my_column} is blank will match None values and empty strings
- {my_column} = "" will match empty strings
from dash import Dash, dash_table, html
import pandas as pd
import numpy as np
data_with_none = [
{'Firm': 'Acme', '2017': '', '2018': 5, '2019': 10, '2020': 4},
{'Firm': 'Olive', '2017': None, '2018': 3, '2019': 13, '2020': 3},
{'Firm': 'Barnwood', '2017': np.NaN, '2018': 7, '2019': 3, '2020': 6},
{'Firm': 'Henrietta', '2017': 14, '2018': 1, '2019': 13, '2020': 1},
]
df = pd.DataFrame(data_with_none)
app = Dash(__name__)
app.layout = html.Div([
html.Pre(repr(df)),
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=(
[
{
'if': {
'filter_query': '{{{}}} is blank'.format(col),
'column_id': col
},
'backgroundColor': 'tomato',
'color': 'white'
} for col in df.columns
]
)
)
])
if __name__ == '__main__':
app.run(debug=True)
Firm 2017 2018 2019 2020 0 Acme 5 10 4 1 Olive None 3 13 3 2 Barnwood NaN 7 3 6 3 Henrietta 14 1 13 1
Displaying Special Values for NaN or None Values
from dash import Dash, dash_table, html
import pandas as pd
import numpy as np
from dash.dash_table.Format import Format
data_with_none = [
{'Firm': 'Acme', '2017': '', '2018': 5, '2019': 10, '2020': 4},
{'Firm': 'Olive', '2017': None, '2018': 3, '2019': 13, '2020': 3},
{'Firm': 'Barnwood', '2017': np.NaN, '2018': 7, '2019': 3, '2020': 6},
{'Firm': 'Henrietta', '2017': 14, '2018': 1, '2019': 13, '2020': 1},
]
df = pd.DataFrame(data_with_none)
app = Dash(__name__)
app.layout = html.Div([
html.Pre(repr(df)),
dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{
'name': i,
'id': i,
'type': 'numeric',
'format': Format(
nully='N/A'
)
} for i in df.columns
],
editable=True
)
])
if __name__ == '__main__':
app.run(debug=True)
Firm 2017 2018 2019 2020 0 Acme 5 10 4 1 Olive None 3 13 3 2 Barnwood NaN 7 3 6 3 Henrietta 14 1 13 1
Limitations:
- Format(nully=) does not yet match for empty strings, only
None values. See plotly/dash-table#763
for updates.
- 'type': 'numeric' needs to be set, see plotly/dash-table#762
for updates.
An alternative method would be to fill in e.g. “N/A” in the data before rendering:
from dash import Dash, dash_table, html
import pandas as pd
import numpy as np
from dash.dash_table.Format import Format
data_with_none = [
{'Firm': 'Acme', '2017': '', '2018': 5, '2019': 10, '2020': 4},
{'Firm': 'Olive', '2017': None, '2018': 3, '2019': 13, '2020': 3},
{'Firm': 'Barnwood', '2017': np.NaN, '2018': 7, '2019': 3, '2020': 6},
{'Firm': 'Henrietta', '2017': 14, '2018': 1, '2019': 13, '2020': 1},
]
df = pd.DataFrame(data_with_none)
df = df.fillna('N/A').replace('', 'N/A')
app = Dash(__name__)
app.layout = html.Div([
html.Pre(repr(df)),
dash_table.DataTable(
data=df.to_dict('records'),
columns=[{'name': i, 'id': i} for i in df.columns],
editable=True,
style_data_conditional=[
{
'if': {
'filter_query': '{{{col}}} = "N/A"'.format(col=col),
'column_id': col
},
'backgroundColor': 'tomato',
'color': 'white'
} for col in df.columns
]
)
])
if __name__ == '__main__':
app.run(debug=True)
Firm 2017 2018 2019 2020 0 Acme N/A 5 10 4 1 Olive N/A 3 13 3 2 Barnwood N/A 7 3 6 3 Henrietta 14 1 13 1
Limitation: If your table is editable, then if a user deletes the
contents of a cell, “N/A” will no longer be displayed.
This is unlike the example with Format where the DataTable will
automatically display N/A for any empty cells, even after editing.
Highlighting Text that Contains a Value
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{'name': i, 'id': i} for i in df.columns
],
style_data_conditional=[
{
'if': {
'filter_query': '{Region} contains "New"'
},
'backgroundColor': '#0074D9',
'color': 'white'
}
]
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting Text that Equals a Value
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{'name': i, 'id': i} for i in df.columns
],
style_data_conditional=[
{
'if': {
'filter_query': '{Region} = "San Francisco"'
},
'backgroundColor': '#0074D9',
'color': 'white'
}
]
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting Cells by Value with a Colorscale Like a Heatmap
This recipe shades cells with style_data_conditional and creates a
legend with HTML components. You’ll need to pip install colorlover
to get the colorscales.
from dash import Dash, dash_table, html
import pandas as pd
from collections import OrderedDict
wide_data = [
{'Firm': 'Acme', '2017': 13, '2018': 5, '2019': 10, '2020': 4},
{'Firm': 'Olive', '2017': 3, '2018': 3, '2019': 13, '2020': 3},
{'Firm': 'Barnwood', '2017': 6, '2018': 7, '2019': 3, '2020': 6},
{'Firm': 'Henrietta', '2017': -3, '2018': -10, '2019': -5, '2020': -6},
]
df = pd.DataFrame(wide_data)
app = Dash(__name__)
def discrete_background_color_bins(df, n_bins=5, columns='all'):
import colorlover
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
if columns == 'all':
if 'id' in df:
df_numeric_columns = df.select_dtypes('number').drop(['id'], axis=1)
else:
df_numeric_columns = df.select_dtypes('number')
else:
df_numeric_columns = df[columns]
df_max = df_numeric_columns.max().max()
df_min = df_numeric_columns.min().min()
ranges = [
((df_max - df_min) * i) + df_min
for i in bounds
]
styles = []
legend = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
backgroundColor = colorlover.scales[str(n_bins)]['seq']['Blues'][i - 1]
color = 'white' if i > len(bounds) / 2. else 'inherit'
for column in df_numeric_columns:
styles.append({
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'backgroundColor': backgroundColor,
'color': color
})
legend.append(
html.Div(style={'display': 'inline-block', 'width': '60px'}, children=[
html.Div(
style={
'backgroundColor': backgroundColor,
'borderLeft': '1px rgb(50, 50, 50) solid',
'height': '10px'
}
),
html.Small(round(min_bound, 2), style={'paddingLeft': '2px'})
])
)
return (styles, html.Div(legend, style={'padding': '5px 0 5px 0'}))
(styles, legend) = discrete_background_color_bins(df)
app.layout = html.Div([
html.Div(legend, style={'float': 'right'}),
dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=styles
),
])
if __name__ == '__main__':
app.run(debug=True)
Highlighting with a Colorscale on a Single Column
from dash import Dash, dash_table, html
import pandas as pd
from collections import OrderedDict
wide_data = [
{'Firm': 'Acme', '2017': 13, '2018': 5, '2019': 10, '2020': 4},
{'Firm': 'Olive', '2017': 3, '2018': 3, '2019': 13, '2020': 3},
{'Firm': 'Barnwood', '2017': 6, '2018': 7, '2019': 3, '2020': 6},
{'Firm': 'Henrietta', '2017': -3, '2018': -10, '2019': -5, '2020': -6},
]
df = pd.DataFrame(wide_data)
app = Dash(__name__)
def discrete_background_color_bins(df, n_bins=5, columns='all'):
import colorlover
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
if columns == 'all':
if 'id' in df:
df_numeric_columns = df.select_dtypes('number').drop(['id'], axis=1)
else:
df_numeric_columns = df.select_dtypes('number')
else:
df_numeric_columns = df[columns]
df_max = df_numeric_columns.max().max()
df_min = df_numeric_columns.min().min()
ranges = [
((df_max - df_min) * i) + df_min
for i in bounds
]
styles = []
legend = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
backgroundColor = colorlover.scales[str(n_bins)]['seq']['Blues'][i - 1]
color = 'white' if i > len(bounds) / 2. else 'inherit'
for column in df_numeric_columns:
styles.append({
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'backgroundColor': backgroundColor,
'color': color
})
legend.append(
html.Div(style={'display': 'inline-block', 'width': '60px'}, children=[
html.Div(
style={
'backgroundColor': backgroundColor,
'borderLeft': '1px rgb(50, 50, 50) solid',
'height': '10px'
}
),
html.Small(round(min_bound, 2), style={'paddingLeft': '2px'})
])
)
return (styles, html.Div(legend, style={'padding': '5px 0 5px 0'}))
(styles, legend) = discrete_background_color_bins(df, columns=['2018'])
app.layout = html.Div([
legend,
dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=styles
)
])
if __name__ == '__main__':
app.run(debug=True)
Displaying Data Bars
These recipes display a creative use of background linear-gradient
colors to display horizontal bar charts within the table.
Your mileage may vary! Feel free to modify these recipes for your own
use.
from dash import Dash, dash_table
import pandas as pd
df_gapminder = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')
df = df_gapminder[:500]
app = Dash(__name__)
def data_bars(df, column):
n_bins = 100
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
ranges = [
((df[column].max() - df[column].min()) * i) + df[column].min()
for i in bounds
]
styles = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
max_bound_percentage = bounds[i] * 100
styles.append({
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'background': (
"""
linear-gradient(90deg,
#0074D9 0%,
#0074D9 {max_bound_percentage}%,
white {max_bound_percentage}%,
white 100%)
""".format(max_bound_percentage=max_bound_percentage)
),
'paddingBottom': 2,
'paddingTop': 2
})
return styles
def data_bars_diverging(df, column, color_above='#3D9970', color_below='#FF4136'):
n_bins = 100
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
col_max = df[column].max()
col_min = df[column].min()
ranges = [
((col_max - col_min) * i) + col_min
for i in bounds
]
midpoint = (col_max + col_min) / 2.
styles = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
min_bound_percentage = bounds[i - 1] * 100
max_bound_percentage = bounds[i] * 100
style = {
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'paddingBottom': 2,
'paddingTop': 2
}
if max_bound > midpoint:
background = (
"""
linear-gradient(90deg,
white 0%,
white 50%,
{color_above} 50%,
{color_above} {max_bound_percentage}%,
white {max_bound_percentage}%,
white 100%)
""".format(
max_bound_percentage=max_bound_percentage,
color_above=color_above
)
)
else:
background = (
"""
linear-gradient(90deg,
white 0%,
white {min_bound_percentage}%,
{color_below} {min_bound_percentage}%,
{color_below} 50%,
white 50%,
white 100%)
""".format(
min_bound_percentage=min_bound_percentage,
color_below=color_below
)
)
style['background'] = background
styles.append(style)
return styles
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=(
data_bars(df, 'lifeExp') +
data_bars(df, 'gdpPercap')
),
style_cell={
'width': '100px',
'minWidth': '100px',
'maxWidth': '100px',
'overflow': 'hidden',
'textOverflow': 'ellipsis',
},
page_size=20
)
if __name__ == '__main__':
app.run(debug=True)
Data Bars without Text
Display the data bars without text by creating a new column and making
the text transparent.
from dash import Dash, dash_table
import pandas as pd
df_gapminder = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')
df = df_gapminder[:500]
df['gdpPercap relative values'] = df['gdpPercap']
app = Dash(__name__)
def data_bars(df, column):
n_bins = 100
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
ranges = [
((df[column].max() - df[column].min()) * i) + df[column].min()
for i in bounds
]
styles = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
max_bound_percentage = bounds[i] * 100
styles.append({
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'background': (
"""
linear-gradient(90deg,
#0074D9 0%,
#0074D9 {max_bound_percentage}%,
white {max_bound_percentage}%,
white 100%)
""".format(max_bound_percentage=max_bound_percentage)
),
'paddingBottom': 2,
'paddingTop': 2
})
return styles
def data_bars_diverging(df, column, color_above='#3D9970', color_below='#FF4136'):
n_bins = 100
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
col_max = df[column].max()
col_min = df[column].min()
ranges = [
((col_max - col_min) * i) + col_min
for i in bounds
]
midpoint = (col_max + col_min) / 2.
styles = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
min_bound_percentage = bounds[i - 1] * 100
max_bound_percentage = bounds[i] * 100
style = {
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'paddingBottom': 2,
'paddingTop': 2
}
if max_bound > midpoint:
background = (
"""
linear-gradient(90deg,
white 0%,
white 50%,
{color_above} 50%,
{color_above} {max_bound_percentage}%,
white {max_bound_percentage}%,
white 100%)
""".format(
max_bound_percentage=max_bound_percentage,
color_above=color_above
)
)
else:
background = (
"""
linear-gradient(90deg,
white 0%,
white {min_bound_percentage}%,
{color_below} {min_bound_percentage}%,
{color_below} 50%,
white 50%,
white 100%)
""".format(
min_bound_percentage=min_bound_percentage,
color_below=color_below
)
)
style['background'] = background
styles.append(style)
return styles
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=(
data_bars(df, 'gdpPercap relative values') + [{
'if': {'column_id': 'gdpPercap relative values'},
'color': 'transparent'
}]
),
style_cell={
'width': '100px',
'minWidth': '100px',
'maxWidth': '100px',
'overflow': 'hidden',
'textOverflow': 'ellipsis',
},
page_size=20
)
if __name__ == '__main__':
app.run(debug=True)
Diverging Data Bars
The data_bars_diverging function splits up the data into two quadrants
by the midpoint.
Alternative representations of data bars may split up the data by
positive and negative numbers or by the average values.
Your mileage may vary! Feel free to modify the data_bars_diverging
function to your own visualization needs. If you create something new,
please share your work on the Dash Community Forum.
from dash import Dash, dash_table
import pandas as pd
df_gapminder = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')
df = df_gapminder[:500]
app = Dash(__name__)
def data_bars(df, column):
n_bins = 100
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
ranges = [
((df[column].max() - df[column].min()) * i) + df[column].min()
for i in bounds
]
styles = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
max_bound_percentage = bounds[i] * 100
styles.append({
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'background': (
"""
linear-gradient(90deg,
#0074D9 0%,
#0074D9 {max_bound_percentage}%,
white {max_bound_percentage}%,
white 100%)
""".format(max_bound_percentage=max_bound_percentage)
),
'paddingBottom': 2,
'paddingTop': 2
})
return styles
def data_bars_diverging(df, column, color_above='#3D9970', color_below='#FF4136'):
n_bins = 100
bounds = [i * (1.0 / n_bins) for i in range(n_bins + 1)]
col_max = df[column].max()
col_min = df[column].min()
ranges = [
((col_max - col_min) * i) + col_min
for i in bounds
]
midpoint = (col_max + col_min) / 2.
styles = []
for i in range(1, len(bounds)):
min_bound = ranges[i - 1]
max_bound = ranges[i]
min_bound_percentage = bounds[i - 1] * 100
max_bound_percentage = bounds[i] * 100
style = {
'if': {
'filter_query': (
'{{{column}}} >= {min_bound}' +
(' && {{{column}}} < {max_bound}' if (i < len(bounds) - 1) else '')
).format(column=column, min_bound=min_bound, max_bound=max_bound),
'column_id': column
},
'paddingBottom': 2,
'paddingTop': 2
}
if max_bound > midpoint:
background = (
"""
linear-gradient(90deg,
white 0%,
white 50%,
{color_above} 50%,
{color_above} {max_bound_percentage}%,
white {max_bound_percentage}%,
white 100%)
""".format(
max_bound_percentage=max_bound_percentage,
color_above=color_above
)
)
else:
background = (
"""
linear-gradient(90deg,
white 0%,
white {min_bound_percentage}%,
{color_below} {min_bound_percentage}%,
{color_below} 50%,
white 50%,
white 100%)
""".format(
min_bound_percentage=min_bound_percentage,
color_below=color_below
)
)
style['background'] = background
styles.append(style)
return styles
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
sort_action='native',
columns=[{'name': i, 'id': i} for i in df.columns],
style_data_conditional=(
data_bars_diverging(df, 'lifeExp') +
data_bars_diverging(df, 'gdpPercap')
),
style_cell={
'width': '100px',
'minWidth': '100px',
'maxWidth': '100px',
'overflow': 'hidden',
'textOverflow': 'ellipsis',
},
page_size=20
)
if __name__ == '__main__':
app.run(debug=True)
Highlighting Dates
from dash import Dash, dash_table
import pandas as pd
from collections import OrderedDict
data = OrderedDict(
[
("Date", ["2015-01-01", "2015-10-24", "2016-05-10", "2017-01-10", "2018-05-10", "2018-08-15"]),
("Region", ["Montreal", "Toronto", "New York City", "Miami", "San Francisco", "London"]),
("Temperature", [1, -20, 3.512, 4, 10423, -441.2]),
("Humidity", [10, 20, 30, 40, 50, 60]),
("Pressure", [2, 10924, 3912, -10, 3591.2, 15]),
]
)
df = pd.DataFrame(data)
app = Dash(__name__)
app.layout = dash_table.DataTable(
data=df.to_dict('records'),
columns=[
{'name': i, 'id': i}
if i != 'Date' else
{'name': 'Date', 'id': 'Date', 'type': 'datetime'}
for i in df.columns
],
style_data_conditional=[{
'if': {'filter_query': '{Date} datestartswith "2015-10"'},
'backgroundColor': '#85144b',
'color': 'white'
}]
)
if __name__ == '__main__':
app.run(debug=True)