通过选择散点图上的点来更新破折号 table
Update dash table by selecting points on scatter plot
我正在开发仪表板。这是我的代码：
# IMPORT SECTION
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from math import ceil
from matplotlib.cm import Set3
# INPUT DATA
n = 7
d_min = 0.2
d_max = 0.8
d_step = 0.1
N_min = 2000
N_max = 8000
N_step = 1000
D = 40
h = 20
dataframe_file = 'data.xlsx'
# COLOR AND FONT DEFINITION
grey = '#e0e1f5'
black = '#212121'
scatter_colors = ['#' + ''.join(['{:02x}'.format(int(255*Set3(i)[j])) for j in range(3)]) for i in range(n)]
fontsize = 18
fontfamily = 'Arial, sans-serif'
# READ CSV DATA
df = pd.read_excel(dataframe_file)
# CREATE DATA FOR DASH DATATABLE
df_scatter_colors = ceil(len(df) / len(scatter_colors)) * scatter_colors
df_scatter_colors = df_scatter_colors[:len(df)]
df.insert(loc = 0, column = 'COLOR', value = df_scatter_colors)
headers = [{"name": i, "id": i} for i in df.columns]
table = df.to_dict('records')
table_colors = [{'if': {'row_index': i, 'column_id': 'COLOR'},
'background-color': df.iloc[i]['COLOR'],
'color': df.iloc[i]['COLOR']} for i in range(df.shape[0])]
# CREATE DATA AND LAYOUT FOR THE SCATTERPLOT
x_jitter = 0.05 * N_step * np.random.randn(len(df))
y_jitter = 0.05 * d_step * 1000 * np.random.randn(len(df))
data = [go.Scatter(x = df['NUMBER'] + x_jitter,
y = df['DIAMETER'] + y_jitter,
text = df['PRODUCT'],
mode = 'markers',
hoverinfo = 'skip',
showlegend = False,
marker_color = 'rgba(0, 0, 0, 0)',
marker = {'size': 25,
'line': {'color': df['COLOR'],
'width': 8}})]
layout = go.Layout(plot_bgcolor = black,
hovermode = 'x unified',
uirevision = 'value')
figure = go.Figure(data = data, layout = layout)
# DASHBOARD LAYOUT
app = dash.Dash(external_stylesheets = [dbc.themes.BOOTSTRAP])
app.layout = html.Div(id = 'general_div',
children = [html.Div(id = 'first_row',
children = [dcc.Graph(id = 'main_graph',
figure = figure,
style = {'height': 800,
'width': 1400})],
className = 'row'),
html.Div(id = 'second_row',
children = [dash_table.DataTable(id = 'main_table',
columns = headers,
data = table,
style_data_conditional = table_colors,
style_table = {'margin-left': '3vw',
'margin-top': '3vw'},
style_cell = {'font-family': fontfamily,
'fontSize': fontsize},
style_header = {'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'})],
className = 'row')])
# CALLBACK DEFINITION
@app.callback(Output('main_table', 'style_data_conditional'),
[Input('main_graph', 'selectedData'),
Input('main_table', 'style_data_conditional')])
def display_selected_data(selectedData, style_data_conditional):
# what to do here and how to run this callback?
return style_data_conditional
if __name__ == "__main__":
app.run_server()
仪表板中显示散点图 (dcc.Graph) 和 table (dash_table.DataTable)。散点图的每个点对应于 table 的特定行，我从 excel 文件中读取这些数据。
excel 文件中的数据格式如下：
PRODUCT CODE NUMBER DIAMETER
AAAAA 1412 8000 0.049
BBBBB 1418 3900 0.08
CCCCC 1420 7600 0.06
DDDDD 1426 8500 0.049
EEEEE 1430 3900 0.08
FFFFF 1442 3900 0.08
GGGGG 1490 8500 0.049
HHHHH 1504 9000 0.18
IIIII 1514 5500 0.224
JJJJJ 1584 7600 0.06
KKKKK 1606 8500 0.049
LLLLL 1618 7600 0.06
MMMMM 1638 7600 0.06
NNNNN 1640 7600 0.06
OOOOO 1666 3900 0.08
PPPPP 1670 8000 0.049
QQQQQ 1672 8000 0.049
RRRRR 1674 7600 0.06
SSSSS 1700 7100 0.071
TTTTT 1704 8500 0.049
UUUUU 1712 7600 0.06
VVVVV 1718 7600 0.06
WWWWW 1722 8000 0.065
我想实现这个功能：当用户选择散点图中的某个点时，代码会突出显示 table 中的相应行（例如将这些行中单元格的背景颜色更改为'pink'，'COLOR' 列除外，它保持其颜色）。
检查了这些来源：
我试图画出这样的回调，但没有成功：
@app.callback(Output('selected_data', 'children'),
[Input('main_graph', 'selectedData'),
Input('main_table', 'style_data_conditional')])
def display_selected_data(selectedData, style_data_conditional):
selected_points = []
for point in selectedData['points']:
selected_points.append(point['marker.line.color'])
selected = [{'if': {'filter': '{COLOR} eq ' + f'"{color}"',
'column_id': 'PRODUCT'},
'backgroundColor': 'pink'} for color in selected_points]
style_data_conditional.extend(selected)
return style_data_conditional
提前致谢。
版本信息
Python 3.7.0
dash 1.12.0
dash-bootstrap-components 0.10.1
dash-core-components 1.10.0
dash-html-components 1.0.3
matplotlib 3.0.2
numpy 1.15.4
plotly 4.7.0
我设法通过将 selectedData 作为 main_graph 的输入并通过函数 update_table_style 处理 main_table 的 style_data_conditional 作为输出来解决问题.
这里我用深灰色给奇数行着色，以提高 table 的可见性，然后我通过样式条件设置所选行的背景颜色。最后，我根据每行的颜色更改第一列的背景（每行第一列报告的颜色）。
代码：
# IMPORT SECTION
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from math import ceil
from matplotlib.cm import Set3
# INPUT DATA
n = 7
d_min = 0.2
d_max = 0.8
d_step = 0.1
N_min = 2000
N_max = 8000
N_step = 1000
D = 40
h = 20
dataframe_file = 'data.xlsx'
# COLOR AND FONT DEFINITION
grey = '#e0e1f5'
black = '#212121'
scatter_colors = ['#' + ''.join(['{:02x}'.format(int(255*Set3(i)[j])) for j in range(3)]) for i in range(n)]
fontsize = 18
fontfamily = 'Arial, sans-serif'
# READ CSV DATA
df = pd.read_excel(dataframe_file)
# CREATE DATA FOR DASH DATATABLE
df_scatter_colors = ceil(len(df) / len(scatter_colors)) * scatter_colors
df_scatter_colors = df_scatter_colors[:len(df)]
df.insert(loc = 0, column = 'COLOR', value = df_scatter_colors)
headers = [{"name": i, "id": i} for i in df.columns]
table = df.to_dict('records')
# CREATE DATA AND LAYOUT FOR THE SCATTERPLOT
x_jitter = 0.05 * N_step * np.random.randn(len(df))
y_jitter = 0.05 * d_step * 1000 * np.random.randn(len(df))
data = [go.Scatter(x = df['NUMBER'] + x_jitter,
y = df['DIAMETER'] + y_jitter,
text = df['PRODUCT'],
mode = 'markers',
hoverinfo = 'skip',
showlegend = False,
marker_color = 'rgba(0, 0, 0, 0)',
marker = {'size': 25,
'line': {'color': df['COLOR'],
'width': 8}})]
layout = go.Layout(plot_bgcolor = black,
hovermode = 'x unified',
uirevision = 'value')
figure = go.Figure(data = data, layout = layout)
def update_table_style(selectedData):
table_style_conditions = [{'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(240, 240, 240)'}]
if selectedData != None:
points_selected = []
for point in selectedData['points']:
points_selected.append(point['pointIndex'])
selected_styles = [{'if': {'row_index': i},
'backgroundColor': 'pink'} for i in points_selected]
table_style_conditions.extend(selected_styles)
table_style_conditions.extend([{'if': {'row_index': i, 'column_id': 'COLOR'},
'background-color': df.iloc[i]['COLOR'],
'color': df.iloc[i]['COLOR']} for i in range(df.shape[0])])
return table_style_conditions
# DASHBOARD LAYOUT
app = dash.Dash(external_stylesheets = [dbc.themes.BOOTSTRAP])
app.layout = html.Div(id = 'general_div',
children = [html.Div(id = 'first_row',
children = [dcc.Graph(id = 'main_graph',
figure = figure,
style = {'height': 800,
'width': 1400})],
className = 'row'),
html.Div(id = 'second_row',
children = [dash_table.DataTable(id = 'main_table',
columns = headers,
data = table,
# style_data_conditional = table_colors,
style_table = {'margin-left': '3vw',
'margin-top': '3vw'},
style_cell = {'font-family': fontfamily,
'fontSize': fontsize},
style_header = {'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'})],
className = 'row')])
# CALLBACK DEFINITION
@app.callback(Output('main_table', 'style_data_conditional'),
[Input('main_graph', 'selectedData')])
def display_selected_data(selectedData):
table_style_conditions = update_table_style(selectedData)
return table_style_conditions
if __name__ == "__main__":
app.run_server()
着色部分是这样的：
table_style_conditions = [{'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(240, 240, 240)'}]
if selectedData != None:
points_selected = []
for point in selectedData['points']:
points_selected.append(point['pointIndex'])
selected_styles = [{'if': {'row_index': i},
'backgroundColor': 'pink'} for i in points_selected]
table_style_conditions.extend(selected_styles)
table_style_conditions.extend([{'if': {'row_index': i, 'column_id': 'COLOR'},
'background-color': df.iloc[i]['COLOR'],
'color': df.iloc[i]['COLOR']} for i in range(df.shape[0])])
这是我得到的结果：
我正在开发仪表板。这是我的代码：
# IMPORT SECTION
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from math import ceil
from matplotlib.cm import Set3
# INPUT DATA
n = 7
d_min = 0.2
d_max = 0.8
d_step = 0.1
N_min = 2000
N_max = 8000
N_step = 1000
D = 40
h = 20
dataframe_file = 'data.xlsx'
# COLOR AND FONT DEFINITION
grey = '#e0e1f5'
black = '#212121'
scatter_colors = ['#' + ''.join(['{:02x}'.format(int(255*Set3(i)[j])) for j in range(3)]) for i in range(n)]
fontsize = 18
fontfamily = 'Arial, sans-serif'
# READ CSV DATA
df = pd.read_excel(dataframe_file)
# CREATE DATA FOR DASH DATATABLE
df_scatter_colors = ceil(len(df) / len(scatter_colors)) * scatter_colors
df_scatter_colors = df_scatter_colors[:len(df)]
df.insert(loc = 0, column = 'COLOR', value = df_scatter_colors)
headers = [{"name": i, "id": i} for i in df.columns]
table = df.to_dict('records')
table_colors = [{'if': {'row_index': i, 'column_id': 'COLOR'},
'background-color': df.iloc[i]['COLOR'],
'color': df.iloc[i]['COLOR']} for i in range(df.shape[0])]
# CREATE DATA AND LAYOUT FOR THE SCATTERPLOT
x_jitter = 0.05 * N_step * np.random.randn(len(df))
y_jitter = 0.05 * d_step * 1000 * np.random.randn(len(df))
data = [go.Scatter(x = df['NUMBER'] + x_jitter,
y = df['DIAMETER'] + y_jitter,
text = df['PRODUCT'],
mode = 'markers',
hoverinfo = 'skip',
showlegend = False,
marker_color = 'rgba(0, 0, 0, 0)',
marker = {'size': 25,
'line': {'color': df['COLOR'],
'width': 8}})]
layout = go.Layout(plot_bgcolor = black,
hovermode = 'x unified',
uirevision = 'value')
figure = go.Figure(data = data, layout = layout)
# DASHBOARD LAYOUT
app = dash.Dash(external_stylesheets = [dbc.themes.BOOTSTRAP])
app.layout = html.Div(id = 'general_div',
children = [html.Div(id = 'first_row',
children = [dcc.Graph(id = 'main_graph',
figure = figure,
style = {'height': 800,
'width': 1400})],
className = 'row'),
html.Div(id = 'second_row',
children = [dash_table.DataTable(id = 'main_table',
columns = headers,
data = table,
style_data_conditional = table_colors,
style_table = {'margin-left': '3vw',
'margin-top': '3vw'},
style_cell = {'font-family': fontfamily,
'fontSize': fontsize},
style_header = {'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'})],
className = 'row')])
# CALLBACK DEFINITION
@app.callback(Output('main_table', 'style_data_conditional'),
[Input('main_graph', 'selectedData'),
Input('main_table', 'style_data_conditional')])
def display_selected_data(selectedData, style_data_conditional):
# what to do here and how to run this callback?
return style_data_conditional
if __name__ == "__main__":
app.run_server()
仪表板中显示散点图 (dcc.Graph) 和 table (dash_table.DataTable)。散点图的每个点对应于 table 的特定行，我从 excel 文件中读取这些数据。
excel 文件中的数据格式如下：
PRODUCT CODE NUMBER DIAMETER
AAAAA 1412 8000 0.049
BBBBB 1418 3900 0.08
CCCCC 1420 7600 0.06
DDDDD 1426 8500 0.049
EEEEE 1430 3900 0.08
FFFFF 1442 3900 0.08
GGGGG 1490 8500 0.049
HHHHH 1504 9000 0.18
IIIII 1514 5500 0.224
JJJJJ 1584 7600 0.06
KKKKK 1606 8500 0.049
LLLLL 1618 7600 0.06
MMMMM 1638 7600 0.06
NNNNN 1640 7600 0.06
OOOOO 1666 3900 0.08
PPPPP 1670 8000 0.049
QQQQQ 1672 8000 0.049
RRRRR 1674 7600 0.06
SSSSS 1700 7100 0.071
TTTTT 1704 8500 0.049
UUUUU 1712 7600 0.06
VVVVV 1718 7600 0.06
WWWWW 1722 8000 0.065
我想实现这个功能：当用户选择散点图中的某个点时，代码会突出显示 table 中的相应行（例如将这些行中单元格的背景颜色更改为'pink'，'COLOR' 列除外，它保持其颜色）。
检查了这些来源：
我试图画出这样的回调，但没有成功：
@app.callback(Output('selected_data', 'children'),
[Input('main_graph', 'selectedData'),
Input('main_table', 'style_data_conditional')])
def display_selected_data(selectedData, style_data_conditional):
selected_points = []
for point in selectedData['points']:
selected_points.append(point['marker.line.color'])
selected = [{'if': {'filter': '{COLOR} eq ' + f'"{color}"',
'column_id': 'PRODUCT'},
'backgroundColor': 'pink'} for color in selected_points]
style_data_conditional.extend(selected)
return style_data_conditional
提前致谢。
版本信息
Python 3.7.0
dash 1.12.0
dash-bootstrap-components 0.10.1
dash-core-components 1.10.0
dash-html-components 1.0.3
matplotlib 3.0.2
numpy 1.15.4
plotly 4.7.0
我设法通过将 selectedData 作为 main_graph 的输入并通过函数 update_table_style 处理 main_table 的 style_data_conditional 作为输出来解决问题.
这里我用深灰色给奇数行着色，以提高 table 的可见性，然后我通过样式条件设置所选行的背景颜色。最后，我根据每行的颜色更改第一列的背景（每行第一列报告的颜色）。
代码：
# IMPORT SECTION
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from math import ceil
from matplotlib.cm import Set3
# INPUT DATA
n = 7
d_min = 0.2
d_max = 0.8
d_step = 0.1
N_min = 2000
N_max = 8000
N_step = 1000
D = 40
h = 20
dataframe_file = 'data.xlsx'
# COLOR AND FONT DEFINITION
grey = '#e0e1f5'
black = '#212121'
scatter_colors = ['#' + ''.join(['{:02x}'.format(int(255*Set3(i)[j])) for j in range(3)]) for i in range(n)]
fontsize = 18
fontfamily = 'Arial, sans-serif'
# READ CSV DATA
df = pd.read_excel(dataframe_file)
# CREATE DATA FOR DASH DATATABLE
df_scatter_colors = ceil(len(df) / len(scatter_colors)) * scatter_colors
df_scatter_colors = df_scatter_colors[:len(df)]
df.insert(loc = 0, column = 'COLOR', value = df_scatter_colors)
headers = [{"name": i, "id": i} for i in df.columns]
table = df.to_dict('records')
# CREATE DATA AND LAYOUT FOR THE SCATTERPLOT
x_jitter = 0.05 * N_step * np.random.randn(len(df))
y_jitter = 0.05 * d_step * 1000 * np.random.randn(len(df))
data = [go.Scatter(x = df['NUMBER'] + x_jitter,
y = df['DIAMETER'] + y_jitter,
text = df['PRODUCT'],
mode = 'markers',
hoverinfo = 'skip',
showlegend = False,
marker_color = 'rgba(0, 0, 0, 0)',
marker = {'size': 25,
'line': {'color': df['COLOR'],
'width': 8}})]
layout = go.Layout(plot_bgcolor = black,
hovermode = 'x unified',
uirevision = 'value')
figure = go.Figure(data = data, layout = layout)
def update_table_style(selectedData):
table_style_conditions = [{'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(240, 240, 240)'}]
if selectedData != None:
points_selected = []
for point in selectedData['points']:
points_selected.append(point['pointIndex'])
selected_styles = [{'if': {'row_index': i},
'backgroundColor': 'pink'} for i in points_selected]
table_style_conditions.extend(selected_styles)
table_style_conditions.extend([{'if': {'row_index': i, 'column_id': 'COLOR'},
'background-color': df.iloc[i]['COLOR'],
'color': df.iloc[i]['COLOR']} for i in range(df.shape[0])])
return table_style_conditions
# DASHBOARD LAYOUT
app = dash.Dash(external_stylesheets = [dbc.themes.BOOTSTRAP])
app.layout = html.Div(id = 'general_div',
children = [html.Div(id = 'first_row',
children = [dcc.Graph(id = 'main_graph',
figure = figure,
style = {'height': 800,
'width': 1400})],
className = 'row'),
html.Div(id = 'second_row',
children = [dash_table.DataTable(id = 'main_table',
columns = headers,
data = table,
# style_data_conditional = table_colors,
style_table = {'margin-left': '3vw',
'margin-top': '3vw'},
style_cell = {'font-family': fontfamily,
'fontSize': fontsize},
style_header = {'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'})],
className = 'row')])
# CALLBACK DEFINITION
@app.callback(Output('main_table', 'style_data_conditional'),
[Input('main_graph', 'selectedData')])
def display_selected_data(selectedData):
table_style_conditions = update_table_style(selectedData)
return table_style_conditions
if __name__ == "__main__":
app.run_server()
着色部分是这样的：
table_style_conditions = [{'if': {'row_index': 'odd'},
'backgroundColor': 'rgb(240, 240, 240)'}]
if selectedData != None:
points_selected = []
for point in selectedData['points']:
points_selected.append(point['pointIndex'])
selected_styles = [{'if': {'row_index': i},
'backgroundColor': 'pink'} for i in points_selected]
table_style_conditions.extend(selected_styles)
table_style_conditions.extend([{'if': {'row_index': i, 'column_id': 'COLOR'},
'background-color': df.iloc[i]['COLOR'],
'color': df.iloc[i]['COLOR']} for i in range(df.shape[0])])
这是我得到的结果：