verticapy.vDataFrame.pivot_table#

Draws the pivot table of one or two columns based on an aggregation.

Parameters#

columns: SQLColumns

List of the vDataColumns names. The list must have one or two elements.

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).

of: str, optional

The vDataColumn used to compute the aggregation.

max_cardinality: tuple, optional

Maximum number of distinct elements for vDataColumns 1 and 2 to be used as categorical. For these elements, no h is picked or computed.

h: tuple, optional

Interval width of the vDataColumns 1 and 2 bars. Only valid if the vDataColumns are numerical. Optimized h will be computed if the parameter is empty or invalid.

fill_none: float, optional

The empty values of the pivot table are filled by this number.

mround: int, optional

Rounds the coefficient using the input number of digits. It is only used to display the final pivot table.

with_numbers: bool, optional

If set to True, no number is displayed in the final drawing.

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 Pivot Table

Let’s begin by importing VerticaPy.

import verticapy as vp

Let’s also import numpy to create a dataset.

import numpy as np

We can create a variable N to fix the size:

N = 30

Let’s generate a dataset using the following data.

data = vp.vDataFrame(
    {
        "category1": [np.random.choice(['A','B','C']) for _ in range(N)],
        "category2": [np.random.choice(['D','E']) for _ in range(N)],
    }
)

Below are examples of one types of pivot_table plots:

Pivot Plot

Pivot Plot

data.pivot_table(columns = ["category1", "category2"])