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verticapy.jupyter.extensions.chart_magic.chart_magic

verticapy.jupyter.extensions.chart_magic.chart_magic(line: str, cell: str | None = None, local_ns: dict | None = None) → Highstock | Highchart

Draws responsive charts using the Matplotlib, Plotly, or Highcharts library.

Different cutomization parameters are available for Plotly, Highcharts, and Matplotlib. For a comprehensive list of customization features, please consult the documentation for the respective plotting libraries: plotly, matplotlib and highcharts.

Parameters

-c / –commandstr, optional

SQL Command to execute.

-f / –filestr, optional

Input File. You can use this option if you want to execute the input file.

-k / –kindstr, optional

Chart Type, one of the following:

• area:

  Area Chart.

• area_range:

  Area Range Chart.

• area_ts:

  Area Chart with Time Series Design.

• bar:

  Bar Chart.

• biserial:

  Biserial Point Matrix (Correlation between binary variables and numerical)

• boxplot:

  Box Plot.

• bubble:

  Bubble Plot.

• candlestick:

  Candlestick and Volumes (Time Series Special Plot).

• cramer:

  Cramer’s V Matrix (Correlation between categories).

• donut:

  Donut Chart.

• donut3d:

  3D Donut Chart.

• heatmap:

  Heatmap.

• hist:

  Histogram.

• kendall:

  Kendall Correlation Matrix.

  Warning

  This method uses a CROSS JOIN during computation and is therefore computationally expensive at O(n * n), where n is the total count of the vDataFrame.

• line:

  Line Plot.

• negative_bar:

  Multi-Bar Chart for binary classes.

• pearson:

  Pearson Correlation Matrix.

• pie:

  Pie Chart.

• pie_half:

  Half Pie Chart.

• pie3d:

  3D Pie Chart.

• scatter:

  Scatter Plot.

• spider:

  Spider Chart.

• spline:

  Spline Plot.

• stacked_bar:

  Stacker Bar Chart.

• stacked_hist:

  Stacked Histogram.

• spearman:

  Spearman Correlation Matrix.

-o / –outputstr, optional

Output File. You can use this option if you want to export the result of the query to the HTML format.

Returns

Chart Object

Examples

The following examples demonstrate:

• Setting up the environment

• Drawing graphics

• Exporting to HTML

• Using variables

• Using SQL files

Hint

To see more examples, please refer to the ref:chart_gallery.guide.

Setting up the environment

If you don’t already have one, create a new connection:

import verticapy as vp

# Save a new connection
vp.new_connection(
    {
        "host": "10.211.55.14",
        "port": "5433",
        "database": "testdb",
        "password": "XxX",
        "user": "dbadmin",
    },
    name = "VerticaDSN",
)

Otherwise, to use an existing connection:

vp.connect("VerticaDSN")

Load the chart extension:

Run the following to load some sample datasets. Once loaded, these datasets are stored in the ‘public’ schema. You can change the target schema with the ‘schema’ parameter:

from verticapy.datasets import load_titanic, load_amazon, load_iris

titanic = load_titanic()

amazon = load_amazon()

iris = load_iris()

Use the set_option() function to set your desired plotting library:

vp.set_option("plotting_lib","plotly")

Drawing graphics

The following examples draw various responsive charts from SQL queries.

Pie Chart

%chart -k pie -c "SELECT pclass, AVG(age) AS av_avg FROM titanic GROUP BY 1;"

Line Plot

%%chart -k line
SELECT
    date,
    AVG(number) AS number
FROM amazon
GROUP BY 1;