Analytics Tool

A flexible Python-based analytics tool for performing statistical analysis and data visualisation using YAML configuration files.

Features

• Statistical Analysis: Compute various statistics (mean, sum, median, standard deviation, etc.) across any variables
• Data Visualization: Generate multiple chart types including:
  • Bar charts
  • Line charts
  • Histograms
  • Box plots
  • Scatter plots (2-way charts)
  • Correlation heatmaps
• YAML Configuration: Define your analysis workflow in a simple, readable YAML file
• Flexible: Works with rectangular (tabular) data in CSV format

Installation

Prerequisites

• Python 3.7 or higher

Install Dependencies

pip install -r requirements.txt

This will install:

• pandas (data manipulation)
• matplotlib (plotting)
• seaborn (statistical visualisation)
• pyyaml (YAML parsing)
• numpy (numerical operations)

Quick Start

1. Basic Usage

Run the analytics tool with a configuration file and data file:

python analytics.py config.yaml sample_data.csv

2. Configuration File

Create a YAML configuration file to specify what analysis to perform. Here's an example:

# Output directory for charts
output_dir: output

# Statistical Analysis
statistics:
  variables: [a, b, c, d, e]
  operations:
    - mean
    - sum
    - median
    - std

# Charts to Generate
charts:
  - type: bar
    variables: [a, b, c]
    title: "Variable Comparison"

  - type: scatter
    variables: [a, b]
    title: "A vs B Scatter Plot"

3. Data Format

Your data should be in CSV format with column headers. Example:

a,b,c,d,e
49.67,74.84,15.79,29.76,28
48.62,61.31,41.73,34.00,22
...

Configuration Options

Statistics Section

statistics:
  variables: [a, b, c]  # Variables to analyse (omit for all)
  operations:           # Statistical operations to perform
    - mean              # Average
    - sum               # Total sum
    - median            # Median value
    - std               # Standard deviation
    - var               # Variance
    - min               # Minimum value
    - max               # Maximum value
    - count             # Count of values

Charts Section

Bar Chart

Compare values across variables:

- type: bar
  variables: [a, b, c, d]
  title: "Comparison Chart"

Line Chart

Show trends over data points:

- type: line
  variables: [a, b]
  title: "Trend Analysis"

Histogram

Display distribution of values:

- type: histogram
  variables: [a]
  title: "Distribution of Variable A"

Box Plot

Show statistical distribution:

- type: box
  variables: [a, b, c, d, e]
  title: "Box Plot Comparison"

Scatter Plot (2-way chart)

Visualise relationship between two variables:

- type: scatter
  variables: [a, b]  # First variable is X-axis, second is Y-axis
  title: "A vs B Relationship"

Heatmap

Show correlation between variables:

- type: heatmap
  variables: [a, b, c, d, e]
  title: "Correlation Matrix"

Example Workflow

1. Prepare your data: Create a CSV file with your variables
2. Create configuration: Define what statistics and charts you want
3. Run analysis:

   python analytics.py config.yaml data.csv

4. View results:
   • Statistics are printed to the console
   • Charts are saved in the output directory (default: output/)

Sample Data

The repository includes sample data with variables a, b, c, d, e:

# Generate new sample data (optional)
python generate_sample_data.py

# Run analytics on sample data
python analytics.py config.yaml sample_data.csv

Output

Console Output

Statistics are printed in a readable format:

==================================================
STATISTICAL ANALYSIS
==================================================

a:
------------------------------
  mean           : 50.1234
  sum            : 5012.3400
  median         : 49.5600
  ...

Chart Files

Charts are saved as PNG files in the output directory:

• chart_1_bar.png
• chart_2_scatter.png
• etc.

Advanced Usage

Custom Output Directory

Specify a custom output directory in your config:

output_dir: my_results

Multiple Analyses

Create different configuration files for different analyses:

python analytics.py basic_stats.yaml data.csv
python analytics.py detailed_charts.yaml data.csv
python analytics.py correlation_analysis.yaml data.csv

Working with Your Own Data

1. Export your data as CSV with headers
2. Note the variable names (column headers)
3. Create a config file specifying your variables
4. Run the analysis

Troubleshooting

Missing Dependencies

pip install -r requirements.txt

File Not Found

Ensure config and data files exist:

ls config.yaml sample_data.csv

Invalid Configuration

Check your YAML syntax:

• Use spaces (not tabs) for indentation
• Ensure proper list formatting with brackets or dashes
• Validate variable names match your CSV columns

Project Structure

.
├── analytics.py              # Main analytics tool
├── config.yaml               # Sample configuration
├── sample_data.csv           # Sample dataset
├── generate_sample_data.py   # Data generator script
├── requirements.txt          # Python dependencies
├── README.md                 # This file
└── output/                   # Generated charts (created automatically)

License

This project is provided as-is for educational and analytical purposes.