Expenditure Data Analysis

This repository contains scripts to analyze expenditure data, generating categorized reports and visualizations of financial transactions over time. The analysis outputs a summary report and visualizations for easy data interpretation.

Docker Installation and Usage

You can use Docker to run the analysis script without needing to install dependencies directly on your machine.

Clone the repository:

git clone https://github.com/pxaris/expenditure-analyzer.git
cd expenditure-analyzer

Step 1: Build the Docker Image

In the root directory of this repository, build the Docker image:

docker build -t expenditure-analyzer .

Step 2: Run the Analysis with Docker

To analyze expenditure data, place your CSV file in the data/ directory, then specify the --data_filename with the following Docker command:

docker run --rm -v $(pwd)/data:/app/data -v $(pwd)/report:/app/report expenditure-analyzer --data_filename 'sample_data.csv'

This command:

• Mounts the data directory to /app/data in the container, so it can access the specified at --data_filename CSV file.
• Mounts the report directory to /app/report in the container, where it will save the generated reports and figures.

Step 3: View the Results

After the analysis completes, you can find the output in the report/ directory:

• Report: The report.txt file provides a summary of the analyzed expenditure data, including the total and average expenditures, breakdowns by category, and insights by month.
• Figures: The generated figures, including bar charts of monthly expenditures and pie charts showing expenditure distribution by category, are also saved in the report/ directory.

These outputs offer a comprehensive view of your spending patterns, making it easier to understand and interpret your financial data over time.

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Manual Installation and Usage

To get started, install the required Python packages. It's recommended to use a virtual environment.

Requirements Installation

1. Clone the repository:

   git clone https://github.com/pxaris/expenditure-analyzer.git
   cd expenditure-analyzer

2. Install the required packages:

   pip install -r requirements.txt

Usage

To analyze expenditure data, follow these steps:

Step 1: Configure Default Variables

Open the config.py file and set the following default configuration variables if needed:

• DATA_FILENAME: Name of the default CSV file containing transaction data, e.g., 'sample_data.csv'.
• N_SKIPROWS: Number of rows to skip at the beginning of the CSV file, typically for headers or additional info, e.g., 6.
• CSV_DELIMITER: The delimiter used in the CSV file, e.g., ';'.
• DATE_COLUMN: Name of the column in the CSV file containing transaction dates, e.g., 'Ημ/νία συναλλαγής'.
• EXPENDITURE_COLUMN: Name of the column with expenditure amounts, e.g., 'Ποσό (EUR)'.
• EXPENDITURE_CATEGORY_COLUMN: Name of the column categorizing expenditures, e.g., 'Κατηγορία δαπάνης'.
• CURRENCY: Currency symbol to display in the report, e.g., '€'.

Step 2: Run the Analysis

You can override the default DATA_FILENAME and REPORT_DIR values directly from the command line.

Basic Usage

To use the default values specified in config.py, simply run:

python analyze.py

Custom Usage

To specify a different data file or report directory, use the following arguments:

python analyze.py --data_filename 'custom_data.csv' --report_dir 'custom_report_dir'

This will process the specified input data, perform analysis, and generate reports and visualizations in the designated report directory.

Step 3: View the Results

After the analysis completes, the Report and the Figures will be located in the report/ directory (or the directory specified in --report_dir).

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Additional Information

• Sample Data: A sample CSV file is included to demonstrate the format required for analysis. It follows the rationale of an exported data file from a Greek bank and the respective analysis can be found in the sample_report/ directory.
• Customization: Modify config.py and adjust the analyze.py and utils.py scripts as needed to fit different datasets or specific analysis requirements.

Enjoy analyzing your expenditure data!