Plotly-Dash:如何过滤具有多个数据框列的仪表板?
问题描述
我有一个使用 dash 构建的 Python 仪表板，我想过滤 Investor 或 Fund列.
I have a Python dashboard built using dash, that I want to filter on either the Investor or the Fund column.
Investor Fund Period Date Symbol Shares Value
0 Rick Fund 3 2019-06-30 AVLR 3 9
1 Faye Fund 2 2015-03-31 MEG 11 80
2 Rick Fund 3 2018-12-31 BAC 10 200
3 Dre Fund 4 2020-06-30 PLOW 2 10
4 Faye Fund 2 2015-03-31 DNOW 10 100
5 Mike Fund 1 2015-03-31 JNJ 1 10
6 Mike Fund 1 2018-12-31 QSR 4 20
7 Mike Fund 1 2018-12-31 LBTYA 3 12
换句话说，用户应该能够在同一筛选字段中输入一个或多个投资者和/或一个或多个基金，仪表板将相应更新.所以我觉得我需要改变:
In other words, the user should be able to input one or more investors, and/or one or more Funds in the same filter field, and the dashboard will update accordingly. So I think I need to change:
options=[{'label': i, 'value': i} for i in df['Investor'].unique()]
类似于 groupby 但不积极?这是我的代码:
to something like groupby but am not positive? Here is my code:
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
data = {'Investor': {0: 'Rick', 1: 'Faye', 2: 'Rick', 3: 'Dre', 4: 'Faye', 5: 'Mike', 6: 'Mike', 7: 'Mike'},
'Fund': {0: 'Fund 3', 1: 'Fund 2', 2: 'Fund 3', 3: 'Fund 4', 4: 'Fund 2', 5: 'Fund 1', 6: 'Fund 1', 7: 'Fund 1'},
'Period Date': {0: '2019-06-30', 1: '2015-03-31', 2: '2018-12-31', 3: '2020-06-30', 4: '2015-03-31', 5: '2015-03-31', 6: '2018-12-31', 7: '2018-12-31'},
'Symbol': {0: 'AVLR', 1: 'MEG', 2: 'BAC', 3: 'PLOW', 4: 'DNOW', 5: 'JNJ', 6: 'QSR', 7: 'LBTYA'},
'Shares': {0: 3, 1: 11, 2: 10, 3: 2, 4: 10, 5: 1, 6: 4, 7: 3},
'Value': {0: 9, 1: 80, 2: 200, 3: 10, 4: 100, 5: 10, 6: 20, 7: 12}}
df = pd.DataFrame.from_dict(data)
def generate_table(dataframe, max_rows=100):
return html.Table(
# Header
[html.Tr([html.Th(col) for col in dataframe.columns])] +
# Body
[html.Tr([
html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
]) for i in range(min(len(dataframe), max_rows))]
)
app = dash.Dash()
app.layout = html.Div(
children=[html.H4(children='Investor Portfolio'),
dcc.Dropdown(
id='dropdown',
options=[{'label': i, 'value': i} for i in df['Investor'].unique()],
multi=True, placeholder='Filter by Investor or Fund...'),
html.Div(id='table-container')
])
@app.callback(dash.dependencies.Output('table-container', 'children'),
[dash.dependencies.Input('dropdown', 'value')])
def display_table(dropdown_value):
if dropdown_value is None:
return generate_table(df)
dff = df[df.Investor.str.contains('|'.join(dropdown_value))]
dff = dff[['Investor', 'Period Date', 'Symbol','Shares', 'Value']]
return generate_table(dff)
if __name__ == '__main__':
app.run_server(debug=True)
解决方案
诚然，我对 Dash 很陌生，但据我所知，您可以通过扩展选项列表，然后使用 or 条件在您在 Dash 应用程序中显示的 dff 中包含 Fund 列.
I am admittedly pretty new to Dash, but from what I can tell, you can achieve what you want by extending your options list, and then using an or condition in the dff that you are displaying in the Dash App to include the Fund column.
这有点蛮力，一个更好的解决方案是让 Dash 知道您选择的选项来自哪些列.但是，如果来自不同列的条目包含相同的字符串，这只会是一个问题(并且这里 Investor 和 Fund 的唯一值永远不会相同).
This is a bit brute force, and a nicer solution would be for Dash to know which columns your selected options are coming from. However, this would only be an issue if entries from different columns contained the same string (and here the unique values for Investor and Fund aren't ever the same).
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
data = {'Investor': {0: 'Rick', 1: 'Faye', 2: 'Rick', 3: 'Dre', 4: 'Faye', 5: 'Mike', 6: 'Mike', 7: 'Mike'},
'Fund': {0: 'Fund 3', 1: 'Fund 2', 2: 'Fund 3', 3: 'Fund 4', 4: 'Fund 2', 5: 'Fund 1', 6: 'Fund 1', 7: 'Fund 1'},
'Period Date': {0: '2019-06-30', 1: '2015-03-31', 2: '2018-12-31', 3: '2020-06-30', 4: '2015-03-31', 5: '2015-03-31', 6: '2018-12-31', 7: '2018-12-31'},
'Symbol': {0: 'AVLR', 1: 'MEG', 2: 'BAC', 3: 'PLOW', 4: 'DNOW', 5: 'JNJ', 6: 'QSR', 7: 'LBTYA'},
'Shares': {0: 3, 1: 11, 2: 10, 3: 2, 4: 10, 5: 1, 6: 4, 7: 3},
'Value': {0: 9, 1: 80, 2: 200, 3: 10, 4: 100, 5: 10, 6: 20, 7: 12}}
df = pd.DataFrame.from_dict(data)
def generate_table(dataframe, max_rows=100):
return html.Table(
# Header
[html.Tr([html.Th(col) for col in dataframe.columns])] +
# Body
[html.Tr([
html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
]) for i in range(min(len(dataframe), max_rows))]
)
app = dash.Dash()
app.layout = html.Div(
children=[html.H4(children='Investor Portfolio'),
dcc.Dropdown(
id='dropdown',
## extend the options to consider unique Fund values as well
options=[{'label': i, 'value': i} for i in df['Investor'].unique()] + [{'label': i, 'value': i} for i in df['Fund'].unique()],
multi=True, placeholder='Filter by Investor or Fund...'),
html.Div(id='table-container')
])
@app.callback(dash.dependencies.Output('table-container', 'children'),
[dash.dependencies.Input('dropdown', 'value')])
def display_table(dropdown_value):
if dropdown_value is None:
return generate_table(df)
## add an 'or' condition for the other column you want to use to slice the df
## and update the columns that are displayed
dff = df[df.Investor.str.contains('|'.join(dropdown_value)) | df.Fund.str.contains('|'.join(dropdown_value))]
dff = dff[['Investor', 'Fund', 'Period Date', 'Symbol','Shares', 'Value']]
return generate_table(dff)
if __name__ == '__main__':
app.run_server(debug=True)
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