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Timeseries#

The timeseries widget is designed to plot numerical data points within a 2D coordinate system, with time as a fundamental dimension. This enables analysis of trends and patterns, facilitates straightforward comparisons, and generates insights.

Beyond its fundamental capabilities, the timeseries widget offers additional features that warrant exploration.

In this page, you will learn more about the timeseries widget and its features, such as legends, tooltips, navigation toolbars, and more.

Create Timeseries Widget#

Let's delve into these aspects, but first, let's begin by creating a basic timeseries widget.

Create a new file named timeseries.py within the widgets directory.

Next, add the following code to create the timeseries and its data (in data/timeseries.json):

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import pandas as pd
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date", data=pd.read_json("data/timeseries.json")
)

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You'll need to add the timeseries_widget to your Dashboard class (in the main.py file):

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from engineai.sdk.dashboard import dashboard
from widgets.table import table_widget
from widgets.timeseries import timeseries_widget

dashboard.Dashboard(
    slug="new_dashboard",
    app_slug="<app-slug>",
    workspace_slug="<workspace-slug>",
    content=[
        table_widget,
        timeseries_widget,
    ],
)
Full Code 
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import pandas as pd
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date", data=pd.read_json("data/timeseries.json")
)

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[
  {
    "date": 1556668800000,
    "open": 52.47,
    "high": 53.83,
    "low": 43.75,
    "close": 43.77,
    "volume": 2957826400
  },
  {
    "date": 1559347200000,
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    "high": 50.39,
    "low": 42.57,
    "close": 49.48,
    "volume": 2060874800
  },
  {
    "date": 1561939200000,
    "open": 50.79,
    "high": 55.34,
    "low": 49.6,
    "close": 53.26,
    "volume": 1895406800
  },
  {
    "date": 1564617600000,
    "open": 53.47,
    "high": 54.51,
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    "volume": 2724326400
  },
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]

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from engineai.sdk.dashboard import dashboard
from widgets.table import table_widget
from widgets.timeseries import timeseries_widget

dashboard.Dashboard(
    slug="new_dashboard",
    app_slug="<app-slug>",
    workspace_slug="<workspace-slug>",
    content=[
        table_widget,
        timeseries_widget,
    ],
)

Publish your Dashboard once again, and upon doing so, you'll observe the timeseries widget showcasing all prices.

engineai dashboard publish

Result

You've successfully created a timeseries widget. Additionally, we've ensured that the timeseries widget employs the Date column as the reference for dates through the date_column parameter.

Customizing Charts and Series#

It is possible to adjust the number of charts and series on the timeseries widget to better showcase data.

There are different types of series that can be used in the Timeseries widget:

In this section, we will add two charts. The first chart will have:

  • LineSeries to visualize the open and close stock's start and end price, respectively, of a given day .
  • AreaRangeSeries to visualize the high and low stock's highest and lowest price, respectively, of a given day.

The second chart will have:

  • ColumnSeries to visualize the volume stock's trading volume of a given day.
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import pandas as pd
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json(
        "data/timeseries.json",
        orient="records",
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis().add_series(
                timeseries.LineSeries(data_column="open"),
                timeseries.LineSeries(data_column="close"),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                ),
            )
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)
Full Code 
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import pandas as pd
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json(
        "data/timeseries.json",
        orient="records",
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis().add_series(
                timeseries.LineSeries(data_column="open"),
                timeseries.LineSeries(data_column="close"),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                ),
            )
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

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from engineai.sdk.dashboard import dashboard
from widgets.table import table_widget
from widgets.timeseries import timeseries_widget

dashboard.Dashboard(
    slug="new_dashboard",
    app_slug="<app-slug>",
    workspace_slug="<workspace-slug>",
    content=[
        table_widget,
        timeseries_widget,
    ],
)

Publish your Dashboard to see the changes you have made to the timeseries widget:

engineai dashboard publish

Result

Axis Formatting#

Let's extend the formatting to the indexed price in the timeseries.py file within the widgets directory by incorporating an AxisNumberFormatting class.

This specific formatting will add a dollar sign ($) on the left side of the price along the y-axis.

This formatting adjustment will be applicable to all series represented on the y-axis.

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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json(
        "data/timeseries.json",
        orient="records",
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(data_column="open"),
                timeseries.LineSeries(data_column="close"),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                ),
            )
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)
Full Code 
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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json(
        "data/timeseries.json",
        orient="records",
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(data_column="open"),
                timeseries.LineSeries(data_column="close"),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                ),
            )
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

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  },
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  },
  {
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  },
  {
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  }
]

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from engineai.sdk.dashboard import dashboard
from widgets.table import table_widget
from widgets.timeseries import timeseries_widget

dashboard.Dashboard(
    slug="new_dashboard",
    app_slug="<app-slug>",
    workspace_slug="<workspace-slug>",
    content=[
        table_widget,
        timeseries_widget,
    ],
)

If you run the dashboard now, you will see that the formatting has changed.

engineai dashboard publish

Result

Styling Timeseries#

As for the timeseries, styling options can vary based on the specific component. For this example we will do a simple color change to the AreaRangeSeries. You will set the color to a fixed yellow color.

Let's now change the color of the AreaRangeSeries in the timeseries.py file in the widgets directory:

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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json("data/timeseries.json", orient="records"),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(data_column="open"),
                timeseries.LineSeries(data_column="close"),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            )
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)
Full Code 
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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json("data/timeseries.json", orient="records"),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(data_column="open"),
                timeseries.LineSeries(data_column="close"),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            )
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

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  {
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    "date": 1682899200000,
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  },
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    "date": 1688169600000,
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  },
  {
    "date": 1690848000000,
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    "date": 1693526400000,
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  {
    "date": 1696118400000,
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  {
    "date": 1698796800000,
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  {
    "date": 1701388800000,
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  {
    "date": 1704067200000,
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  },
  {
    "date": 1706745600000,
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  },
  {
    "date": 1709251200000,
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  },
  {
    "date": 1711929600000,
    "open": 171.19,
    "high": 171.25,
    "low": 168.23,
    "close": 168.84,
    "volume": 95498800
  },
  {
    "date": 1712016000000,
    "open": 169.06,
    "high": 169.34,
    "low": 168.23,
    "close": 168.84,
    "volume": 47248438
  }
]

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from engineai.sdk.dashboard import dashboard
from widgets.table import table_widget
from widgets.timeseries import timeseries_widget

dashboard.Dashboard(
    slug="new_dashboard",
    app_slug="<app-slug>",
    workspace_slug="<workspace-slug>",
    content=[
        table_widget,
        timeseries_widget,
    ],
)

You can now publish the dashboard and see the changes.

engineai dashboard publish

Result

Now, besides data adjustments, you are now able to change the widgets' styling. In the next sections, we’ll cover some timeseries components such as tooltips, legends, navigation toolbars, and period selectors.

Tooltips#

For all widgets that present data using charts, you have the ability to incorporate tooltips. These tooltips can be added at either the series level or the chart level.

There are various types of tooltips:

  • CategoryTooltipItem: Displays the category name.
  • DatetimeTooltipItem: Displays the datetime value.
  • NumberTooltipItem: Displays the number value.
  • TextTooltipItem: Displays the text value.

Let's add tooltips to the timeseries widget. Change the previous code in the timeseries_widget widget to include tooltips:

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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json(
        "data/timeseries.json",
        orient="records",
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(
                    data_column="open",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.LineSeries(
                    data_column="close",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            ),
            tooltips=[
                timeseries.NumberTooltipItem(data_column="high"),
                timeseries.NumberTooltipItem(data_column="low"),
            ],
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

Legend#

You also have the option to adjust the position of the legend for widgets like the timeseries.

To modify the legend position in the timeseries widget, update the previous code within the timeseries_widget widget to include the desired legend position:

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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json("data/timeseries.json", orient="records"),
    legend=timeseries.LegendPosition.RIGHT,
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(
                    data_column="open",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.LineSeries(
                    data_column="close",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            ),
            tooltips=[
                timeseries.NumberTooltipItem(data_column="high"),
                timeseries.NumberTooltipItem(data_column="low"),
            ],
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

Now, let's proceed to include a Navigation Toolbar to the timeseries widget.

To do this, you'll need to modify the previous code in the timeseries_widget widget to incorporate the Navigation Toolbar:

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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json("data/timeseries.json", orient="records"),
    legend=timeseries.LegendPosition.RIGHT,
    navigator=timeseries.ColumnSeries(data_column="volume"),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(
                    data_column="open",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.LineSeries(
                    data_column="close",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            ),
            tooltips=[
                timeseries.NumberTooltipItem(data_column="high"),
                timeseries.NumberTooltipItem(data_column="low"),
            ],
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)
Add multiple series

You have the ability to introduce one or multiple series within the Navigation Toolbar using the following code:

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navigator=timeseries.Navigator(
    timeseries.ColumnSeries(data_column="volume"),
    timeseries.LineSeries(data_column="open"),
    timeseries.LineSeries(data_column="close"),
),

Period Selector#

The timeseries widget lets you include a PeriodSelector class, enabling easy changes to the displayed data's time period in the chart.

By default, the Period Selector has the following options:

  • 1 Month
  • 3 Months
  • YTD
  • 1 Year
  • 5 Years

Let's customize the Period Selector by modifying the existing code within the timeseries_widget widget:

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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json("data/timeseries.json", orient="records"),
    legend=timeseries.LegendPosition.RIGHT,
    navigator=timeseries.ColumnSeries(data_column="volume"),
    period_selector=timeseries.PeriodSelector(
        timeseries.Period.M1,
        timeseries.Period.M3,
        timeseries.Period.ALL,
        default_selection=2,
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(
                    data_column="open",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.LineSeries(
                    data_column="close",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            ),
            tooltips=[
                timeseries.NumberTooltipItem(data_column="high"),
                timeseries.NumberTooltipItem(data_column="low"),
            ],
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

You can set the default selected period when the widget loads by incorporating the default_selection parameter. In this case, the default period selected is 3 Months.

Full Code 
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import pandas as pd
from engineai.sdk.dashboard import formatting
from engineai.sdk.dashboard.styling import color
from engineai.sdk.dashboard.widgets import timeseries

timeseries_widget = timeseries.Timeseries(
    date_column="date",
    data=pd.read_json("data/timeseries.json", orient="records"),
    legend=timeseries.LegendPosition.RIGHT,
    navigator=timeseries.ColumnSeries(data_column="volume"),
    period_selector=timeseries.PeriodSelector(
        timeseries.Period.M1,
        timeseries.Period.M3,
        timeseries.Period.ALL,
        default_selection=2,
    ),
    charts=[
        timeseries.Chart(
            left_y_axis=timeseries.YAxis(
                formatting=formatting.AxisNumberFormatting(prefix="$")
            ).add_series(
                timeseries.LineSeries(
                    data_column="open",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.LineSeries(
                    data_column="close",
                    tooltips=timeseries.NumberTooltipItem(data_column="volume"),
                ),
                timeseries.AreaRangeSeries(
                    low_data_column="low",
                    high_data_column="high",
                    name="High-Low Range",
                    styling=timeseries.AreaRangeSeriesStyling(
                        color_spec=color.Palette.BANANA_YELLOW
                    ),
                ),
            ),
            tooltips=[
                timeseries.NumberTooltipItem(data_column="high"),
                timeseries.NumberTooltipItem(data_column="low"),
            ],
        ),
        timeseries.Chart(left_y_axis=timeseries.ColumnSeries(data_column="volume")),
    ],
)

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[
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    "date": 1556668800000,
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    "high": 53.83,
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    "close": 43.77,
    "volume": 2957826400
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  {
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  {
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    "volume": 1627516300
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  {
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    "volume": 2180800100
  },
  {
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    "volume": 1687795600
  },
  {
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  },
  {
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  {
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    "volume": 2084722800
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    "volume": 1868139700
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    "date": 1667260800000,
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  {
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    "volume": 1675731200
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  {
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    "close": 144.29,
    "volume": 1443652500
  },
  {
    "date": 1675209600000,
    "open": 143.97,
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    "low": 141.32,
    "close": 147.41,
    "volume": 1307198900
  },
  {
    "date": 1677628800000,
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    "high": 165.0,
    "low": 143.9,
    "close": 164.9,
    "volume": 1520266600
  },
  {
    "date": 1680307200000,
    "open": 164.27,
    "high": 169.85,
    "low": 159.78,
    "close": 169.68,
    "volume": 969709700
  },
  {
    "date": 1682899200000,
    "open": 169.28,
    "high": 179.35,
    "low": 164.31,
    "close": 177.25,
    "volume": 1275155500
  },
  {
    "date": 1685577600000,
    "open": 177.7,
    "high": 194.48,
    "low": 176.93,
    "close": 193.97,
    "volume": 1297101100
  },
  {
    "date": 1688169600000,
    "open": 193.78,
    "high": 198.23,
    "low": 186.6,
    "close": 196.45,
    "volume": 996066400
  },
  {
    "date": 1690848000000,
    "open": 196.24,
    "high": 196.73,
    "low": 171.96,
    "close": 187.87,
    "volume": 1322439400
  },
  {
    "date": 1693526400000,
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]

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from engineai.sdk.dashboard import dashboard
from widgets.table import table_widget
from widgets.timeseries import timeseries_widget

dashboard.Dashboard(
    slug="new_dashboard",
    app_slug="<app-slug>",
    workspace_slug="<workspace-slug>",
    content=[
        table_widget,
        timeseries_widget,
    ],
)

Publish your Dashboard to see all the changes you have made to the timeseries widget:

engineai dashboard publish

Result

To Recap:

  • New Tooltips: The ability to add tooltips at either the series or chart level, providing additional information when interacting with data points.
  • Legend Position: Customization of the legend's position within the widget for visual clarity and understanding.
  • Navigation Toolbar: The addition of a navigation toolbar, enabling you to navigate and explore data more effectively.
  • Period Selector: Utilization of a period selector to dynamically adjust the displayed time period within the chart for data analysis.

Next Steps#

Next, we’ll delve into the different types of widgets and how to link them.