Real time/updating Plotly chart in Python/Flask app?

I built a Webapp with Flask (with a JS frontend) and now i would like to add charts to show my data.

I managed to embed a chart on it but it's static, is there a way to make it dynamic (for example with random values so that i can later add my own data to it)?

def index():



    rng = pd.date_range('1/1/2011', periods=7500, freq='H')

    ts = pd.Series(np.random.randn(len(rng)), index=rng)



    graphs = [



        dict(

            data=[

                dict(

                    x= arr,

                    y=[10, 20, 30],

                    type='scatter'

                ),

            ],

            layout=dict(

                title='first graph'

            )

        ) 



    ]



    # Add "ids" to each of the graphs to pass up to the client

    # for templating

    ids = ['graph-{}'.format(i) for i, _ in enumerate(graphs)]



    # Convert the figures to JSON

    # PlotlyJSONEncoder appropriately converts pandas, datetime, etc

    # objects to their JSON equivalents

    graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)



    return render_template('index.html',

                           ids=ids,

                           graphJSON=graphJSON)

asked Nov 13 '18 at 13:52

Jack022

6010

Have you seen plotly streaming? plot.ly/python/streaming-tutorial or Dash github.com/plotly/dash-wind-streaming/blob/master/README.md

– cricket_007
Nov 13 '18 at 15:18

Yeah, i looked into Dash and i'm probably going to use it!

– Jack022
Nov 13 '18 at 17:14

add a comment |

I built a Webapp with Flask (with a JS frontend) and now i would like to add charts to show my data.

I managed to embed a chart on it but it's static, is there a way to make it dynamic (for example with random values so that i can later add my own data to it)?

def index():



    rng = pd.date_range('1/1/2011', periods=7500, freq='H')

    ts = pd.Series(np.random.randn(len(rng)), index=rng)



    graphs = [



        dict(

            data=[

                dict(

                    x= arr,

                    y=[10, 20, 30],

                    type='scatter'

                ),

            ],

            layout=dict(

                title='first graph'

            )

        ) 



    ]



    # Add "ids" to each of the graphs to pass up to the client

    # for templating

    ids = ['graph-{}'.format(i) for i, _ in enumerate(graphs)]



    # Convert the figures to JSON

    # PlotlyJSONEncoder appropriately converts pandas, datetime, etc

    # objects to their JSON equivalents

    graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)



    return render_template('index.html',

                           ids=ids,

                           graphJSON=graphJSON)

asked Nov 13 '18 at 13:52

Jack022

6010

Have you seen plotly streaming? plot.ly/python/streaming-tutorial or Dash github.com/plotly/dash-wind-streaming/blob/master/README.md

– cricket_007
Nov 13 '18 at 15:18

Yeah, i looked into Dash and i'm probably going to use it!

– Jack022
Nov 13 '18 at 17:14

add a comment |

I built a Webapp with Flask (with a JS frontend) and now i would like to add charts to show my data.

I managed to embed a chart on it but it's static, is there a way to make it dynamic (for example with random values so that i can later add my own data to it)?

def index():



    rng = pd.date_range('1/1/2011', periods=7500, freq='H')

    ts = pd.Series(np.random.randn(len(rng)), index=rng)



    graphs = [



        dict(

            data=[

                dict(

                    x= arr,

                    y=[10, 20, 30],

                    type='scatter'

                ),

            ],

            layout=dict(

                title='first graph'

            )

        ) 



    ]



    # Add "ids" to each of the graphs to pass up to the client

    # for templating

    ids = ['graph-{}'.format(i) for i, _ in enumerate(graphs)]



    # Convert the figures to JSON

    # PlotlyJSONEncoder appropriately converts pandas, datetime, etc

    # objects to their JSON equivalents

    graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)



    return render_template('index.html',

                           ids=ids,

                           graphJSON=graphJSON)

asked Nov 13 '18 at 13:52

Jack022

6010

I built a Webapp with Flask (with a JS frontend) and now i would like to add charts to show my data.

I managed to embed a chart on it but it's static, is there a way to make it dynamic (for example with random values so that i can later add my own data to it)?

def index():



    rng = pd.date_range('1/1/2011', periods=7500, freq='H')

    ts = pd.Series(np.random.randn(len(rng)), index=rng)



    graphs = [



        dict(

            data=[

                dict(

                    x= arr,

                    y=[10, 20, 30],

                    type='scatter'

                ),

            ],

            layout=dict(

                title='first graph'

            )

        ) 



    ]



    # Add "ids" to each of the graphs to pass up to the client

    # for templating

    ids = ['graph-{}'.format(i) for i, _ in enumerate(graphs)]



    # Convert the figures to JSON

    # PlotlyJSONEncoder appropriately converts pandas, datetime, etc

    # objects to their JSON equivalents

    graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)



    return render_template('index.html',

                           ids=ids,

                           graphJSON=graphJSON)

python plotly

asked Nov 13 '18 at 13:52

Jack022

6010

asked Nov 13 '18 at 13:52

Jack022

6010

asked Nov 13 '18 at 13:52

Jack022

6010

asked Nov 13 '18 at 13:52

Jack022

6010

asked Nov 13 '18 at 13:52

Jack022

6010

Have you seen plotly streaming? plot.ly/python/streaming-tutorial or Dash github.com/plotly/dash-wind-streaming/blob/master/README.md

– cricket_007
Nov 13 '18 at 15:18

Yeah, i looked into Dash and i'm probably going to use it!

– Jack022
Nov 13 '18 at 17:14

add a comment |

Have you seen plotly streaming? plot.ly/python/streaming-tutorial or Dash github.com/plotly/dash-wind-streaming/blob/master/README.md

– cricket_007
Nov 13 '18 at 15:18

Yeah, i looked into Dash and i'm probably going to use it!

– Jack022
Nov 13 '18 at 17:14

Have you seen plotly streaming? plot.ly/python/streaming-tutorial or Dash github.com/plotly/dash-wind-streaming/blob/master/README.md

– cricket_007
Nov 13 '18 at 15:18

Yeah, i looked into Dash and i'm probably going to use it!

– Jack022
Nov 13 '18 at 17:14

add a comment |

1 Answer
1

active

oldest

votes

HTTP is stateless by nature, so if you want dynamic, you will have to do that in the frontend.

Without knowing more about your Frontend I cannot give concrete examples,
but hopefully this puts you on the right track:

Update the chart data in constant intervals through AJAX calls to the backend.

With that extended data update the chart through Plotly.extendtraces

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

Thanks! I'll look into it!

– Jack022
Nov 13 '18 at 17:13

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

HTTP is stateless by nature, so if you want dynamic, you will have to do that in the frontend.

Without knowing more about your Frontend I cannot give concrete examples,
but hopefully this puts you on the right track:

Update the chart data in constant intervals through AJAX calls to the backend.

With that extended data update the chart through Plotly.extendtraces

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

Thanks! I'll look into it!

– Jack022
Nov 13 '18 at 17:13

add a comment |

HTTP is stateless by nature, so if you want dynamic, you will have to do that in the frontend.

Without knowing more about your Frontend I cannot give concrete examples,
but hopefully this puts you on the right track:

Update the chart data in constant intervals through AJAX calls to the backend.

With that extended data update the chart through Plotly.extendtraces

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

Thanks! I'll look into it!

– Jack022
Nov 13 '18 at 17:13

add a comment |

HTTP is stateless by nature, so if you want dynamic, you will have to do that in the frontend.

Without knowing more about your Frontend I cannot give concrete examples,
but hopefully this puts you on the right track:

Update the chart data in constant intervals through AJAX calls to the backend.

With that extended data update the chart through Plotly.extendtraces

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

HTTP is stateless by nature, so if you want dynamic, you will have to do that in the frontend.

Without knowing more about your Frontend I cannot give concrete examples,
but hopefully this puts you on the right track:

Update the chart data in constant intervals through AJAX calls to the backend.

With that extended data update the chart through Plotly.extendtraces

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

answered Nov 13 '18 at 15:13

Sebastian Loehner

72245

Thanks! I'll look into it!

– Jack022
Nov 13 '18 at 17:13

add a comment |

Thanks! I'll look into it!

– Jack022
Nov 13 '18 at 17:13

Thanks! I'll look into it!

– Jack022
Nov 13 '18 at 17:13

add a comment |

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