verticapy.vDataColumn.candlestick#

Draws the Time Series of the vDataColumn.

Parameters#

ts: str

TS (Time Series) vDataColumn used to order the data. The vDataColumn type must be date like (date, datetime, timestamp…) or numerical.

method: str, optional

The method used to aggregate the data.

count:
Number of elements.
density:
Percentage of the distribution.
mean:
Average of the vDataColumns of.
min:
Minimum of the vDataColumns of.
max:
Maximum of the vDataColumns of.
sum:
Sum of the vDataColumns of.
q%:
q Quantile of the vDataColumns of (ex: 50% to get the median).

It can also be a cutomized aggregation (ex: AVG(column1) + 5).

q: tuple, optional

Tuple including the 2 quantiles used to draw the Plot.

start_date: str / PythonNumber / date, optional

Input Start Date. For example, time = ‘03-11-1993’ will filter the data when ‘ts’ is less than the 3rd of November 1993.

end_date: str / PythonNumber / date, optional

Input End Date. For example, time = ‘03-11-1993’ will filter the data when ‘ts’ is greater than the 3rd of November 1993.

chart: PlottingObject, optional

The chart object to plot on.

**style_kwargs

Any optional parameter to pass to the plotting functions.

Returns#

obj: Plotting Object.

Examples#

Note

The below example is a very basic one. For other more detailed examples and customization options, please see Candlestick

Let’s begin by importing VerticaPy.

import verticapy as vp

Let’s also import numpy to create a dataset.

import numpy as np

Let’s generate a dataset using the following data.

data = vp.vDataFrame(
    {
        "date": [1990 + i for i in range(N)] * 5,
        "population": [100 + i for i in range(N)] + [300 + i * 2 for i in range(N)] + [200 + i ** 2 - 3 * i for i in range(N)] + [50 + i ** 2 - 6 * i for i in range(N)] + [700 + i ** 2 - 10 * i for i in range(N)],
    }
)

Now we are ready to draw the plot:

data["population"].candlestick(ts = "date")