verticapy.vDataColumn.bar#

Draws the bar chart of the vDataColumn based on an aggregation.

Parameters#

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: int, optional

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

nbins: int, optional

Number of bins. If empty, an optimized number of bins is computed.

h: PythonNumber, optional

Interval width of the bar. If empty, an optimized h is computed.

categorical: bool, optional

If set to False and the vDataColumn is numerical, the parmater ‘max_cardinality’ is ignored and the bar chart is represented as a histogram.

bargap: float, optional

A float between (0, 1] that represents the proportion taken out of each bar to render the chart. This proportion creates gaps between each bar. The bigger the value, the bigger the gap.

categoryorder: str, optional

How to sort the bars. One of the following options:

trace (no transformation)
category ascending
category descending
total ascending
total descending

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 Bar Chart

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(
    {
        "gender": ['M', 'M', 'M', 'F', 'F', 'F', 'F'],
        "grade": ['A','B','C','A','B','B', 'B'],
    }
)

Now we are ready to draw the plot:

data["grade"].bar()