Trend#

Open this notebook in Jupyterlite | Download this notebook from GitHub (right-click to download).

import numpy as np
import pandas as pd
import panel as pn

pn.extension()

The Trend is a value indicator providing a visual representation of a value along with an indicator of its recent trend. It supports streaming data to the plot component making it possible to provide performant live updates on the recent trends in some value.

Parameters:#

For details on other options for customizing the component see the layout and styling how-to guides.

data (dict(str, np.ndarray) or pd.DataFrame): The plot data as a dictionary of arrays or a pandas DataFrame.
layout (str, default='column'): The layout of the indicator, either ‘column’ or ‘row’.
plot_x (str, default='y'): The column in the data corresponding to x-values of the plot.
plot_y (str, default='x'): The column in the data corresponding to y-values of the plot.
plot_color (str, default='#428bca'): The color to use in the plot.
plot_type (str, default='bar'): The plot type to render the plot data as, on of ‘line’, ‘bar’, ‘step’ or ‘area’.
pos_color (str, default='#5cb85c'): The color used to indicate a positive change.
neg_color (str, default='#d9534f'): The color used to indicate a negative change.
name (str): The name or a short description of the indicator.
value (float or int or “auto”, default='auto'): The primary value to be displayed.
value_change (float or int or “auto”, default='auto'): The change in the value expressed as a fraction.

The simplest form of a Trend just requires providing some data with x- and y-values, either declared as a dictionary or a pandas.DataFrame. The value and value_change values will then be automatically computed from the data:

data = {'x': np.arange(50), 'y': np.random.randn(50).cumsum()}

trend = pn.indicators.Trend(
    name='Price', data=data, width=200, height=200
)
trend

Streaming#

The Trend indicator also provides a convenient method to stream new data, which supports a rollover argument to limit the amount of data displayed. We will register a periodic callback using pn.state.add_periodic_callback to update the plot:

def stream_data():
    trend.stream({'x': [trend.data['x'][-1]+1], 'y': [trend.data['y'][-1]+np.random.randn()]}, rollover=50)

pn.state.add_periodic_callback(stream_data, period=250, count=100);

Plot types#

In addition to the default plot_type the stream indicator also supports several other options:

pn.Row(*(trend.clone(plot_type=pt) for pt in trend.param.plot_type.objects))

Controls#

To get a feeling for the different parameters of the Trend indicator we will display a set of controls:

trend = trend.clone()

pn.Row(trend.controls(), trend)

Open this notebook in Jupyterlite | Download this notebook from GitHub (right-click to download).