marks={ 0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5' }, value=[0, 6]) ]), html.H3('Neighborhood'), dcc.Dropdown( id='dropdown1', options=[{ 'label': i, 'value': i } for i in df.Neighborhood.unique()], multi=True, # value = [i for i in df.Neighborhood.unique()] ), html.P('\n'), html.H3('Borough'), dcc.Dropdown( id='dropdown2', options=[{ 'label': i, 'value': i } for i in df.Borough.unique()], multi=True, # value = [i for i in df.Neighborhood.unique()] )
app = dash.Dash() df = pd.read_csv('https://gist.githubusercontent.com/chriddyp/' 'cb5392c35661370d95f300086accea51/raw/' '8e0768211f6b747c0db42a9ce9a0937dafcbd8b2/' 'indicators.csv') available_indicators = df['Indicator Name'].unique() app.layout = html.Div([ html.Div( [ html.Div([ dcc.Dropdown(id='crossfilter-xaxis-column', options=[{ 'label': i, 'value': i } for i in available_indicators], value='Fertility rate, total (births per woman)'), dcc.RadioItems(id='crossfilter-xaxis-type', options=[{ 'label': i, 'value': i } for i in ['Linear', 'Log']], value='Linear', labelStyle={'display': 'inline-block'}) ], style={ 'width': '49%', 'display': 'inline-block' }), html.Div([
def _make_layout(embeddings_list: List[WordEmbeddings]) -> object: """Create the layout for the embedding projector app.""" return dbc.Container(fluid=True, children=[ html.H1(children='Embedding Projector'), # We use a hidden div to faciliate callbacks. # This 'signal' can be used both ways: to trigger a callback, or to have a callback # return nothing. # # For example, when we want to do some data processing (such as computing the PCA) # but may not necessarily want to render anything new. html.Div(id='update-embeddings-signal', style={'display': 'none'}), html.Div(id='hidden-div', style={'display': 'none'}), dcc.Loading(id='loading-spinner', fullscreen=True, loading_state={'is_loading': True}), dbc.Row([ dbc.Col(html.Div([ html.Div([ html.H3('DATA', style={'font-weight': 'bold'}), html.Label('Embeddings'), dcc.Dropdown(id='embeddings-dropdown', options=[ {'label': str(embeddings), 'value': i} for i, embeddings in enumerate(embeddings_list) ], clearable=False, value=0), html.Div(id='metadata-div'), dbc.Checklist(id='spherize-data', options=[{'label': 'Spherize data', 'value': 'yes'}], value=['yes'], inline=True, switch=True) ]), html.Div([ html.H3('PCA', style={'font-weight': 'bold'}), dbc.Label('X-axis'), dcc.Dropdown(id='x-component-dropdown', value=0, clearable=False), dbc.Label('Y-axis'), dcc.Dropdown(id='y-component-dropdown', value=1, clearable=False), dbc.Checklist(id='use-z-component', options=[{'label': 'Z-Axis', 'value': 'yes'}], value=['yes'], inline=True, switch=True, className='py-1'), dcc.Dropdown(id='z-component-dropdown', value=2, clearable=False), html.Br(), html.Label(id='pca-variance-label') ]) ]), width=2), dbc.Col(html.Div([ dcc.Loading(id='embedding-graph-loading', children=[ dcc.Graph( # Make a defualt empty graph figure=_make_embedding_scatter([], [], [], []), id='embedding-graph', style={'height': '100vh'} ) ], type='default') ]), width=8), dbc.Col(html.Div([ html.Div([ html.H3('ANALYSIS', style={'font-weight': 'bold'}), dbc.Row([ dbc.Col([dbc.Button('Show All Data', id='show-all-data-button', outline=True, color='dark', className='mr-2 my-4', disabled=True)]), dbc.Col([dbc.Button('Isolate Points', id='isolate-points-button', outline=True, color='dark', className='mr-2 my-4', disabled=True)]), dbc.Col([dbc.Button('Clear Selection', id='clear-selection-button', outline=True, color='dark', className='my-4', disabled=True)]) ], no_gutters=True), html.Div(id='selected-word-state', style={'display': 'none'}), dbc.Tabs([ dbc.Tab(tab_id='search-tab', children=[ html.Div([ dbc.Input(id='word-search-input', type='text', placeholder='Search'), dbc.FormText(id='word-search-matches', color='secondary'), html.Div([ dbc.ListGroup(id='word-search-results', className='pt-3') ], className='overflow-auto', style={ 'max-height': '50vh', 'height': '100%' }) ], className='mt-3') ], label='Search'), dbc.Tab( id='selected-word-tab', tab_id='selected-word-tab', label='Selection', disabled=True ), ], id='analysis-tabs', active_tab='search-tab') ]), ]), width=2) ]) ])
def get_cote_layout(): return html.Div(className='main', children=[ dcc.Location(id='url-cotes', refresh=False), # Pour intercepter un appel à la page html.P(children=html.A(href="/", children=lang['back_to_main'])), html.H1(children=lang['title_cotes']), dcc.Tabs( id='main-tabs', value='tab-viz6', className='main-tabs', parent_className='parent-main-tabs', colors={ 'border': 'brown', # Couleur des bordures des onglets 'primary': 'brown', # Couleur de ligne au-dessus de l'onglet sélectionné 'background': '#EFE0E0' # Couleur de fond des onglets non sélectionnés }, children = [ dcc.Tab( id='tab-viz0', value='tab-viz0', label=lang['viz0'], className='tab', selected_className='selected-tab', children=[html.Div( id='div_tab_viz0', className='div-tab', children=[dcc.Markdown(lang['info']), html.Br()] )] ), dcc.Tab( id='tab-viz4', value='tab-viz4', label=lang['viz4'], className='tab', selected_className='selected-tab', children=[html.Div( id='div_tab_viz4', className='div-tab', children=[ html.H3(lang['title_viz4']), html.Table(className='table-center', children=[html.Tbody([ html.Tr(children=[ html.Td(children=html.H6(lang['choose_action'])), html.Td(children=[dcc.Dropdown( id='viz4_choice_action', className='viz-dropdown-large', multi=False, options=[ {'label': lang['title_viz1'], 'value': 'viz1'}, {'label': lang['title_viz2'], 'value': 'viz2'}, {'label': lang['title_viz3'], 'value': 'viz3'}, {'label': lang['title_viz3a'], 'value': 'viz3a'} ], value='viz1' )]) ]), html.Tr(id='tr_viz1', children=[ html.Td(children=html.H6(lang['choose_wine_alea'])), html.Td(children=[dcc.Dropdown( id='viz1_choice', className='viz-dropdown-large', multi=False, )]) ]), html.Tr(id='tr_viz2', children=[ html.Td(children=html.H6(lang['choose_appellation'])), html.Td(children=[dcc.Dropdown( id='viz2_choice', className='viz-dropdown-large', multi=False, options=(options := [{'label': app, 'value': app} for app in DF_viz2_choice['appellation'].tolist()]), value=options[0]['value'] )]) ]), html.Tr(id='tr_viz3', children=[ html.Td(children=html.H6(lang['choose_appellation'])), html.Td(children=[dcc.Dropdown( id='viz3_choice', className='viz-dropdown-large', multi=False, options=(options := [{'label': app, 'value': app} for app in DF_viz3_choice['appellation'].tolist()]), value=options[0]['value'] )]) ]), html.Tr(id='tr_viz3a', children=[ html.Td(children=html.H6(lang['choose_wine'])), html.Td(children=[dcc.Dropdown( id='viz3a_choice', className='viz-dropdown-large', multi=False, options = (options := [{'label': label, 'value': value} for (value, label) in zip( DF_viz3a_choice['index'], DF_viz3a_choice.apply(lambda S: S['domaine'] + " : " + S['nom_du_vin'] + " " + str(int(S['millesime'])) + " (" \ + S['appellation'] + ", " + S['couleur'] + ")", axis=1 ) )]), value = options[0]['value'] )]) ]) ])]), html.Div(id='viz4_result', className='viz-result') ] )] ), get_prediction_tab('viz5'), get_prediction_tab('viz6', with_tf=True) ] ) ])
html.Div( [ html.H4('Select Company Stock', style={ 'textAlign': 'left', 'color': colors['text'] }), dcc.Dropdown( id='my-tickers', options=[{ 'label': 'MSFT', 'value': 'MSFT' }, { 'label': 'BABA', 'value': 'BABA' }, { 'label': 'SPY', 'value': 'SPY' }, { 'label': 'Coke', 'value': 'COKE' }], value='MSFT', ) ], style={ 'width': '100%', 'display': 'flex', 'align-items': 'center', 'justify-content': 'center' }),
} app.layout = html.Div([ html.H1(children='Release Deployment History', style={ 'textAlign': 'center', 'color': colors['text'] }), #html.H1(children='Release Deployment History', style={'textAlign': 'center','color': colors['text'],'backgroundColor':colors['background']}), html.Div([ html.Div('Application Name', className='app_name'), html.Div( dcc.Dropdown( id='application-dropdown', options=[{ 'label': k, 'value': k } for k in all_options.keys()], )) ]), html.Br(), html.Div([ html.Div('Environment', className='app_name'), html.Div(dcc.Dropdown(id='environments-dropdown')) ]), #html.Br(), #html.Label('CRQ Number'), #dcc.Input(id='crq-number', value='', type='text'), html.Br(), html.Br(), html.Div(id='display-selected-values')
], id="imgdiv") ], id="titlediv") datadiv = html.Div( [ # Links html.Div( [ ### Kanal 1 dcc.Dropdown(id='channel1', options=[{ 'label': i, 'value': i } for i in sorted(CHANNELS)], multi=True, value=default_shop, placeholder="Kanal auswählen", clearable=True, className='dropdown'), ### KPI 1 dcc.Dropdown( id='metric1', options=[{ 'label': i, 'value': i } for i in sorted(METRICS)], multi=False, value=default_metric, placeholder="KPI 1 auswählen (Default: Nettoumsatz)", className='dropdown',
'float': 'right', 'display': 'inline-block' }), # Space between text and dropdown html.H1(id='space', children=' '), # Dropdown html.Div([ dcc.Dropdown(options=[{ 'label': 'El Pais English', 'value': 'EPE' }, { 'label': 'The Guardian', 'value': 'THG' }, { 'label': 'The Mirror', 'value': 'TMI' }], value=['EPE', 'THG'], multi=True, id='checklist') ], style={ 'width': '40%', 'display': 'inline-block', 'float': 'left' }), # Button html.Div([html.Button('Submit', id='submit', type='submit')],
html.Div([ dcc.Markdown('###### Wind Speed'), dcc.Slider(id='wind_speed', min=0, max=30, step=5, value=10, marks={n: str(n) for n in range(0, 31, 5)}), ], style=style), html.Div([ dcc.Markdown('###### Batter'), dcc.Dropdown(id='batter', options=[{ 'label': batter, 'value': batter } for batter in batters], value=batters[0]), ], style=style), html.Div([ dcc.Markdown('###### Pitcher'), dcc.Dropdown(id='pitcher', options=[{ 'label': pitcher, 'value': pitcher } for pitcher in pitchers], value=pitchers[0]), ], style=style), html.Div([
def layout(self): return html.Div([ html.Div( [ dcc.Dropdown( id=self.dropwdown_vector_id, clearable=False, options=[{ "label": i, "value": i } for i in self.vctr_cols_no_hist], value=self.vctr_cols_no_hist[0], ), html.Div([ dcc.Checklist( id=self.chlst, options=[{ "label": "Delta time series", "value": "show_delta_series", }], labelStyle={"display": "inline-block"}, value=[], ), html.Div( id=self.show_ens_selectors, children=[ dcc.Dropdown( id=self.dropdown_iorens_id, placeholder="Base case", options=[{ "label": i, "value": i } for i in self.base_ensembles], ), dcc.Dropdown( id=self.dropdown_refens_id, placeholder="Select ensembles", options=[{ "label": i, "value": i } for i in self.delta_ensembles], multi=True, ), ], style={"display": "none"}, ), ]), ], style={ "width": "20%", "float": "left" }, ), html.Div( [ dcc.Tabs( id=self.tab_id, value="summary_data", children=[ dcc.Tab(label="Realizations", value="summary_data"), dcc.Tab(label="Statistics", value="summary_stats"), ], ), html.Div(id="tabs-content"), html.Div(id=self.chart_id), ], style={ "width": "80%", "float": "right" }, ), ])
'width': '5%' } ), ## dropdown lists for selecting projections html.Div([ html.Div('Select projections:', style={'display': 'block' } ), dcc.Dropdown( id='species_dropdown', placeholder='select species', multi=True, style={'display': 'block', 'height': '34px', 'width': '100%', 'marginBottom': '5px', 'borderWidth': '1px'} ), dcc.Dropdown( id='orb_dropdown', placeholder='select orbital', multi=True, style={'display': 'block', 'height': '34px', 'width': '100%', 'marginBottom': '5px', 'borderWidth': '1px'} ),
respaldo = pd.read_csv('respaldo.csv') df = lista_g.copy() external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) available_indicators = lista_g['dependencia_clean'] app.layout = html.Div([ html.Div( [ html.Div([ dcc.Dropdown(id='dep', options=[{ 'label': i, 'value': i } for i in available_indicators], value='cfe'), dcc.Checklist(id='activaciones', options=[{ 'label': 'Usar Dependencia', 'value': 'uso_dep' }, { 'label': 'Usar Top', 'value': 'uso_top' }], value=[]), html.Div(dcc.Slider( id='year--slider', min=df['año_dependencia'].min(), max=df['año_dependencia'].max(),
html.H1('SIR Model (Susceptible, Infectious, and Recovered)', style={'text-align': 'center', 'padding': 10, 'background-color': '#f0f0f5',}), dcc.Markdown(''' The below graphs shows the future trend of the spread of COVID-19(dotted line) using the SIR model.''', style={'text-align': 'center', 'padding': 10, 'background-color': '#f0f0f5',}), dcc.Markdown('''__Select Country for visualization__ '''), dcc.Dropdown( id = 'country_drop_down', options=[ {'label': each,'value':each} for each in df_analyse['country'].unique()], value= 'India', # which are pre-selected multi=False), dcc.Graph(figure = fig, id = 'SIR_graph') ]) @app.callback( Output('SIR_graph', 'figure'), [Input('country_drop_down', 'value')]) def update_SIR_figure(country_drop_down): traces = [] df_plot = df_analyse[df_analyse['country'] == country_drop_down]
html.Div([ html.H1('SPYN-Starter'), dcc.Markdown( children='## `EDDP Web Application Demo using PYNQ`') ], style={ 'text-align': 'left', 'padding': '10px 0px 0px 0px' }), html.Div([ dcc.Markdown(children='### `Set Mode`'), dcc.Dropdown(id='modes-dropdown', options=[{ 'label': 'Speed', 'value': 'Speed' }, { 'label': 'Current', 'value': 'Current' }], value='Speed'), dcc.Markdown(children='### `Set Decimation`'), dcc.Dropdown(id='decimate-dropdown', options=[ { 'label': 'no decimation', 'value': '0' }, { 'label': '1', 'value': '1' },
stock_header_dict = {} start = (date.today() - relativedelta(months=6)) end = date.today() #### CSS Styling that we can change to have better white space etc external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) app.config.supress_callback_exceptions = True #### Layout which holds everything app.layout = html.Div([ html.H1('Stock Search'), dcc.Dropdown(id='stock-dropdown', placeholder='Pick Stocks From Here...', searchable=True, options=search_dict), html.Div(id='stock-search-results'), html.Br(), html.H4(id='header-1', style={'color': '#0189aa'}), html.H6(id='header-2'), html.H6(id='header-3'), html.H6(id='header-4'), html.H6(id='header-5'), html.H6(id='header-6'), html.H6(id='header-7'), dcc.Graph(id='candlestick-main') ], style={'height': '100vh'})
dcc.Tabs(id="tabs-styled-with-inline", value='scraper', children=[ dcc.Tab(label='Scraper', value='scraper', style=tab_style, selected_style=tab_selected_style, children=[ html.Div([ dcc.Dropdown( id='platform-input', options=[{ 'label': 'Amazon', 'value': 'amazon' }, { 'label': 'Facebook', 'value': 'facebook' }, { 'label': 'Twitter', 'value': 'twitter' }], value='amazon', style={'color': colors['text']}), html.Button('Submit', id='button'), html.Div(id='platform-output', style={'color': colors['text']}) ]) ]), dcc.Tab(label='Preprocessing', value='preprocessing', style=tab_style, selected_style=tab_selected_style),
# Div containing everything matrix html.Div( [ # Details on matrix goes here html.Div([ html.P([ 'Generate a ', html.A(['matrix'], href='https://en.wikipedia.org/wiki/Matrix'), ]), ]), html.Div([ html.Div([ html.Label('Row: '), dcc.Dropdown( options=dropdown_options, id='row_dropdown', ), html.Label('Column: '), dcc.Dropdown( options=dropdown_options, id='column_dropdown', ), # When dealing with matrices of different sizes will need something like this # dcc.RadioItems( # options=[ # {'label': 'Two matrices of same size', 'value': 'two_matrices'}, # ], # value='two_matrices', # id="two_matrices_checklist" # ), html.Button(id='generate_matrix_submit_button',
dcc.Dropdown(id='select-industry', options=[ { 'label': 'All', 'value': 'all' }, { 'label': 'Farms', 'value': 'farms' }, { 'label': 'Oil and Gas', 'value': 'oil_and_gas' }, { 'label': 'Utilities', 'value': 'utilities' }, { 'label': 'Construction', 'value': 'construction' }, { 'label': 'Manufacturing', 'value': 'manufacturing' }, { 'label': 'Wholesale Trade', 'value': 'wholesale_trade' }, { 'label': 'Retail Trade', 'value': 'retail_trade' }, { 'label': 'Air Transportation', 'value': 'air_transportation' }, { 'label': 'Real Estate', 'value': 'real_estate' }, ], value='all'),
'layout': { 'title': 'Comparing 2020 Season Average Rebounds, Points & Assist Between Player 1 and Player 2 ', 'xlabel': 'Date', 'ylabel': ' Average Rebounds' } }, id="graph1"), html.H5("Introduction : "), html. P("The National Collegiate Athletic Association (NCAA) Women Division 1 Basketball Tournament has 64 teams. This dashboard is comparing players within a team. The graph is comparing three scores of two players. The table summarizes the scores for these two players.", style={'width': '50%'}), html.Div([ html.H5("Select Player 1: "), dcc.Dropdown(options=name_dict, id='my-dropdown1', value='Helene Haegerstrand', style={'width': '64%'}) ]), html.Div([ html.H5("Select Player 2: "), dcc.Dropdown(options=name_dict, id='my-dropdown2', value='Lucia Decortes', style={'width': '64%'}) ]), html.Div(id="table_div", style={'width': '70%'}), html.H5("Reference: "), html.P('https://sports.yahoo.com/ncaaw/teams/albany/roster', style={'width': '45%'}), html.H5("Submitted by: "), html.P('Adwoa Osei-Yeboah', style={'width': '45%'})
dbc.Row([ dbc.Col(html.P("Set Color-Scheme"), width=6), dbc.Col(html.P(""), width=6) ]), dbc.Row([ dbc.Col( dcc.Dropdown(options=[{ 'label': 'RdBu', 'value': 'RdBu' }, { 'label': 'Picnic', 'value': 'Picnic' }, { 'label': 'Rainbow', 'value': 'Rainbow' }, { 'label': 'Hot', 'value': 'Hot' }, { 'label': 'Earth', 'value': 'Earth' }], value='Earth', id='style-dropdown', style={"width": "120px"})), dbc.Col(dbc.Button("Submit", id="submit-button", color="primary", className="mr-1"), width=6) ])
- Dictations are analyzed by IBM Watson Natural Language Understanding. - Output variables: keywords, entities, concepts and categories. - Variable sets are clustered, using NMF Non-zero Matrix Factorization - Dictations and variables are mapped onto the clusters ''', className = 'nine columns') ], className = "row"), html.Div( [ html.Div( [ html.Label('#Archetypes', style={'font-weight' : 'bold'}), dcc.Dropdown( id = 'NoA', options=[{'label':k,'value':k} for k in range(2,12)], value = 6, multi = False ) ], className = 'one columns offset-by-one', style={'margin-top': '30'} ), html.Div( [ html.Label('Variables', style={'font-weight' : 'bold'}), dcc.RadioItems( id = 'Var', options=[ {'label': 'Keywords' ,'value': 'keywords'}, {'label': 'Entities' ,'value': 'entities'}, {'label': 'Concepts' ,'value': 'concepts'},
center={"lat": 4.655115, "lon": -74.055404}, #Center color_continuous_scale="matter", #Color Scheme opacity=0.5, title='Total crimes in Colombia by Department' ) Map_fig.update_layout(title='Total de Crímenes por Departamento',paper_bgcolor="#F8F9F9") values={'Robbery and theft':'robbery','Domestic violence':'dom_viol','Sex offenses':'sex_off'} #Now we create the dropdown for crimes dropdown=dcc.Dropdown( id="crime_dropdown", options=[{"label":key, "value":values[key]} for key in values.keys()], value=["sex_off",'robbery'], placeholder="Select a crime", multi=True ) #Now the dropdown for years ys = [year for year in dff.year.unique()] dropdown2=dcc.Dropdown( id="year_dropdown", options=[{"label":year, "value":year} for year in ys], value=2018, placeholder="Select a year", multi=False )
def get_prediction_tab(vizname, with_tf=True): options = [{'label': lang['xgboost'], 'value': 'xg'}] if with_tf: options = options + [{'label': lang['tensorflow'], 'value': 'tf'}] return dcc.Tab( id='tab-' + vizname, value='tab-' + vizname, label=lang[vizname], className='tab', selected_className='selected-tab', children=[ html.Div( id='div_tab_' + vizname, className='div-tab', children=[ html.H3(lang['title_' + vizname]), html.P(lang[vizname + '_desc']), html.Table( className='table-center', children=[ html.Tbody([ html.Tr(children=[ html.Td(children=html.H6( lang['choose_model'])), html.Td(children=[ dcc.Dropdown( id=vizname + '_choice_model', className='viz-dropdown-large', multi=False, options=options, value='xg') ]) ]), html.Tr(children=[ html.Td(children=html.H6( lang['choose_action'])), html.Td(children=[ dcc.Dropdown( id=vizname + '_choice_action', className='viz-dropdown-large', multi=False, options=[{ 'label': lang['see_pred'], 'value': 'viz' }, { 'label': lang['get_reco_test'], 'value': 'reco' }], value='reco') ]) ]), html.Tr( id=vizname + '_tr_choose_number', children=[ html.Td(children=html.H6( lang['choose_number'])), html.Td(children=[ dcc.Dropdown( id=vizname + '_choice_nbwines', className='viz-dropdown-small', multi=False, options=[{ 'label': n, 'value': n } for n in np.arange( 5, 55, 5)], value=20) ]) ]), html.Tr( id=vizname + '_tr_choose_wine', children=[ html.Td(children=html.H6( lang['choose_wine_alea'])), html.Td(children=[ dcc.Dropdown( id=vizname + '_choice_wine', className='viz-dropdown-large', multi=False) ]) ]) ]) ]), html.Div(id=vizname + '_result', className='viz-result') ]) ])
'value': 'panPan2', 'label': 'Bonobo (MPI-EVA panpan1.1/panPan2)' }, { 'value': 'canFam3', 'label': 'Dog (Broad CanFam3.1/canFam3)' }, { 'value': 'ce11', 'label': 'C. elegans (ce11)' }] app.layout = html.Div([ dcc.Loading(id='igv-container'), html.Hr(), html.P('Select the genome to display below.'), dcc.Dropdown(id='igv-genome-select', options=HOSTED_GENOME_DICT, value='ce11') ]) # Return the IGV component with the selected genome. @app.callback(Output('igv-container', 'children'), Input('igv-genome-select', 'value')) def return_igv(genome): return (html.Div( [dashbio.Igv( id='default-igv', genome=genome, minimumBases=100, )]))
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response: counties = json.load(response) ########### Initiate the app external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) server = app.server app.title='VA 2016' ####### Layout of the app ######## app.layout = html.Div([ html.H3('2016 Presidential Election: Vote Totals by Jurisdiction'), dcc.Dropdown( id='dropdown', options=[{'label': i, 'value': i} for i in options_list], value=options_list[10] ), html.Br(), dcc.Graph(id='display-map'), dcc.Graph(id='display-value'), html.Br(), html.A('Code on Github', href='https://github.com/austinlasseter/virginia_election_2016'), html.Br(), html.A('Data Source', href='https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LYWX3D') ]) ######### Callback #1 ######### @app.callback(dash.dependencies.Output('display-value', 'figure'), [dash.dependencies.Input('dropdown', 'value')])
app = dash.Dash() nsdq = pd.read_csv('../data/NASDAQcompanylist.csv') nsdq.set_index('Symbol', inplace=True) options = [] for tic in nsdq.index: options.append({ 'label': '{} {}'.format(tic, nsdq.loc[tic]['Name']), 'value': tic }) app.layout = html.Div([ html.H1('Stock Ticker Dashboard'), html.Div([ html.H3('Select stock symbols:', style={'paddingRight': '30px'}), dcc.Dropdown( id='my_ticker_symbol', options=options, value=['TSLA'], multi=True) ], style={ 'display': 'inline-block', 'verticalAlign': 'top', 'width': '30%' }), html.Div([ html.H3('Select start and end dates:'), dcc.DatePickerRange(id='my_date_picker', min_date_allowed=datetime(2015, 1, 1), max_date_allowed=datetime.today(), start_date=datetime(2018, 1, 1), end_date=datetime.today()) ], style={'display': 'inline-block'}),
start_date=dash_visits['dt'].min(), end_date=dash_visits['dt'].max(), display_format='YYYY-MM-DD', id='dt_selector', ), html.Br(), html.Br(), # фильтр возраста html.Label('Выбор возрастной категории'), # составляем список dcc.Dropdown(options=[{ 'label': x, 'value': x } for x in dash_visits['age_segment'].unique()], value=dash_visits['age_segment'].unique(), multi=True, id='age-dropdown'), ], className='six columns'), # конец левого блока # блок с пикером тем (правый блок) html.Div( [ # фильтр тем html.Label('Выбор темы карточек'), # составляем список dcc.Dropdown(options=[{
}), html.H2(children='Graph 1', style={ 'font-family': 'Arial, Helvetica, sans-serif', 'text-align': 'center' }), html.Div([ html.P(children='Select the first indicator:', style={ 'font-size': 15, 'font-family': 'Arial, Helvetica, sans-serif' }), dcc.Dropdown( id='xaxis-column', options=[{ 'label': i, 'value': i } for i in available_indicators], value=available_indicators[0], ) ], style={ 'width': '48%', 'display': 'inline-block', 'height': '130px' }), html.Div([ html.P(children='Select the second indicator:', style={ 'font-size': 15, 'font-family': 'Arial, Helvetica, sans-serif' }),
FILTERS = { c.dash.INPUT_SMOOTHING: ( "Smoothing:", dbc.Input(id="input_smoothing", type="number", value=c.dash.DEFAULT_SMOOTHING), ), c.dash.INPUT_TIMEWINDOW: ( "Grouping:", dcc.Dropdown( id="input_timewindow", value="M", options=[{ "label": "Month ", "value": "M" }, { "label": "Year ", "value": "Y" }], ), ), c.dash.INPUT_CATEGORIES: ( "Categories:", dcc.Dropdown(id="input_categories", multi=True, options=get_options(DF[c.cols.CATEGORY].unique())), ), }
import dash_core_components as dcc import dash_html_components as html external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) app.layout = html.Div([ html.Div([ html.Label('Dropdown'), dcc.Dropdown(options=[{ 'label': 'New York City', 'value': 'NYC' }, { 'label': u'Montréal', 'value': 'MTL' }, { 'label': 'San Francisco', 'value': 'SF' }], value='SF'), html.Label('Multi-Select Dropdown'), dcc.Dropdown(options=[{ 'label': 'New York City', 'value': 'NYC' }, { 'label': u'Montréal', 'value': 'MTL' }, { 'label': 'San Francisco', 'value': 'SF'