Bar Plots

Bar plots are essential for comparing categorical data, showing distributions across groups, and visualizing survey results. Rekha’s bar plots support vertical and horizontal orientations, grouping, stacking, and custom styling.

Basic Usage

import rekha as rk
import pandas as pd

# Simple bar plot
df = pd.DataFrame({
    'category': ['A', 'B', 'C', 'D'],
    'value': [23, 45, 12, 67]
})

fig = rk.bar(df, x='category', y='value', title='Simple Bar Plot')
fig.show()

Examples Gallery

Basic Vertical Bar Plot

Simple categorical comparison:

import rekha as rk
from examples.utils import get_categorical_data

df = get_categorical_data()
region_sales = df.groupby('region')['sales'].sum().reset_index()

fig = rk.bar(
    data=region_sales,
    x='region',
    y='sales',
    title='Sales by Region',
    labels={'region': 'Region', 'sales': 'Total Sales ($)'}
)

Full example

Grouped Bar Plot

Compare multiple categories side by side:

quarter_region = df.groupby(['region', 'quarter'])['sales'].sum().reset_index()

fig = rk.bar(
    data=quarter_region,
    x='region',
    y='sales',
    color='quarter',
    title='Quarterly Sales by Region',
    labels={'region': 'Region', 'sales': 'Sales ($)', 'quarter': 'Quarter'}
)

Full example

Horizontal Bar Plot

Better for long category names:

product_sales = df.groupby('product')['sales'].sum().reset_index().sort_values('sales')

fig = rk.bar(
    data=product_sales,
    x='sales',
    y='product',
    orientation='h',
    title='Sales by Product',
    labels={'sales': 'Total Sales ($)', 'product': 'Product'}
)

Full example

Stacked Bar Plot

Show component contributions with stacked bars:

# Prepare data for stacking
sales_by_component = df.groupby(['month', 'component'])['sales'].sum().reset_index()

fig = rk.bar(
    data=sales_by_component,
    x='month',
    y='sales',
    color='component',
    barmode="stack",
    title='Monthly Sales Breakdown by Component',
    labels={'month': 'Month', 'sales': 'Sales ($)', 'component': 'Component'}
)

Full example

Parameters

See the API Reference for complete parameter documentation.

See Also

• Histograms - For continuous data distributions

• Heatmaps - For 2D categorical data

• API Reference - Complete parameter documentation