Examples

This page contains comprehensive examples for each plot type available in Exploralytics.

Setting Up Example Data

First, let’s create some sample data to use in our examples:

import pandas as pd
import numpy as np

# Create sample numerical data
np.random.seed(42)
data = {
    'sales': np.random.normal(1000, 200, 1000),
    'profit': np.random.normal(300, 80, 1000),
    'customers': np.random.normal(500, 150, 1000),
    'marketing_spend': np.random.normal(200, 50, 1000),
    'satisfaction': np.random.normal(4.2, 0.5, 1000)
}
df = pd.DataFrame(data)

# Create sample categorical data
categories = {
    'product_category': ['Electronics', 'Clothing', 'Food', 'Books', 'Sports'],
    'revenue': [120000, 85000, 65000, 45000, 35000]
}
df_categories = pd.DataFrame(categories)

1. Histogram Plots

Single Column Histogram

Create a histogram for a single column:

viz.plot_histograms(
    df,
    specific_cols=['sales'],
    title='Sales Distribution',
    subtitle='Distribution of sales values',
    show_mean=True,
    show_median=True
)

Multiple Column Histograms

Create histograms for multiple columns in a grid:

viz.plot_histograms(
    df,
    num_cols=2,
    title='Key Metrics Distribution',
    subtitle='Distribution of various business metrics',
    show_mean=True
)

2. Correlation Matrix

Create a correlation heatmap:

viz.plot_correlation_map(
    df,
    title='Correlation Analysis',
    subtitle='Relationship between different metrics',
    footer='Data from Q3 2023'
)

Customization options:

• Custom color scale

• Interactive hover information

• Automatic handling of upper triangle

3. Correlation with Target

Analyze feature correlations with a target variable:

viz.plot_correlation_with_target(
    df,
    target_column='profit',
    title='Profit Correlations',
    subtitle='How different metrics correlate with profit',
    footer='Based on 2023 data'
)

Features:

• Automatic sorting by correlation strength

• Color coding for positive/negative correlations

• Interactive tooltips

4. Horizontal Bar Plot

Simple Category Distribution

Create a simple horizontal bar plot:

viz.plot_hbar(
    df_categories,
    x_col='product_category',
    title='Product Categories',
    subtitle='Distribution of product categories'
)

Advanced Bar Plot

Create a bar plot with highlighting and reference line:

viz.plot_hbar(
    df_categories,
    x_col='product_category',
    y_col='revenue',
    title='Revenue by Category',
    subtitle='Total revenue for each product category',
    add_hline=True,                    # Add mean line
    top_n=3,                           # Show only top 3
    highlight_top_n=(2, '#2E75B6'),    # Highlight top 2 in blue
    highlight_low_n=(1, '#FF9999')     # Highlight bottom 1 in red
)

5. Dot Plot

Basic Dot Plot

Create a simple dot plot:

viz.plot_dot(
    df_categories,
    x_col='product_category',
    y_col='revenue',
    title='Revenue by Category',
    subtitle='Revenue distribution across product categories'
)

Advanced Dot Plot

Create a dot plot with reference line and highlighting:

viz.plot_dot(
    df_categories,
    x_col='product_category',
    y_col='revenue',
    title='Revenue by Category',
    subtitle='Revenue distribution across product categories',
    add_hline_at=('Average', 70000),    # Add reference line
    highlight_top_n=(2, '#2E75B6'),     # Highlight top 2
    highlight_low_n=(1, '#FF9999')      # Highlight bottom 1
)

Real-World Examples

Sales Analysis

Here’s a complete example analyzing sales data:

# Load sales data
sales_data = pd.read_csv('sales.csv')

# Create visualizer
viz = Visualizer(template='plotly_white')

# Distribution of sales
viz.plot_histograms(
    sales_data,
    specific_cols=['daily_sales'],
    title='Daily Sales Distribution',
    show_mean=True
)

# Sales by category
viz.plot_hbar(
    sales_data,
    x_col='category',
    y_col='total_sales',
    title='Sales by Category',
    highlight_top_n=(3, '#2E75B6')
)

# Correlation analysis
viz.plot_correlation_map(
    sales_data,
    title='Sales Metrics Correlation'
)

Customer Analysis

Example of customer data analysis:

# Load customer data
customer_data = pd.read_csv('customers.csv')

# Customer satisfaction distribution
viz.plot_histograms(
    customer_data,
    specific_cols=['satisfaction_score'],
    title='Customer Satisfaction Distribution'
)

# Correlation with satisfaction
viz.plot_correlation_with_target(
    customer_data,
    target_column='satisfaction_score',
    title='Factors Affecting Satisfaction'
)