Histograms

Histograms are essential for understanding data distributions, identifying patterns like skewness and multimodality, and comparing distributions across groups. Rekha’s histograms offer flexible binning, density estimation, and comparison features.

Basic Usage

import rekha as rk
import pandas as pd
import numpy as np

# Simple histogram
data = np.random.normal(50, 15, 1000)
df = pd.DataFrame({'values': data})

fig = rk.histogram(df, x='values', title='Distribution of Values')
fig.show()

Examples Gallery

Basic Histogram

Simple distribution visualization:

import rekha as rk
import numpy as np

# Generate normal distribution
np.random.seed(42)
data = np.random.normal(100, 15, 1000)
df = pd.DataFrame({'values': data})

fig = rk.histogram(
    data=df,
    x='values',
    title='Normal Distribution',
    labels={'values': 'Value'},
    nbins=30
)

Full example

Distribution Comparison

Compare multiple distributions side by side:

# Create multiple distributions for comparison
data_long = pd.melt(
    df[['normal', 'skewed_right']],
    var_name='distribution',
    value_name='value'
)

fig = rk.histogram(
    data=data_long,
    x='value',
    facet_col='distribution',
    title='Distribution Comparison',
    labels={'value': 'Value', 'distribution': 'Type'},
    nbins=25
)

Full example

Grouped by Category

Compare distributions across groups:

fig = rk.histogram(
    data=df_iris,
    x='petal_length',
    color='species',
    title='Petal Length by Species',
    labels={'petal_length': 'Petal Length (cm)', 'species': 'Species'},
    alpha=0.8,
    nbins=20
)

Full example

Parameters

See the API Reference for complete parameter documentation.

See Also

• Box Plots - For statistical summaries

• CDF Plots - For cumulative distributions

• API Reference - Complete parameter documentation