# gauravgawade2008/python architecture

> Architecture diagram of the GitHub repository gauravgawade2008/python, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-20). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/gauravgawade2008/python
- Repository: https://github.com/gauravgawade2008/python

## Overview

1. **Repository shape**
   - This is not a single integrated application; it is a small portfolio of independent Python data-processing exercises.
   - The top-level README describes the repo as a learning workspace, and the real architecture lives in the two subprojects.

2. **Primary organizing boundary**
   - Each subfolder is a self-contained project with its own input data, notebook, Python script, and cleaned output file.
   - There is no shared library layer, package structure, or cross-project dependency between the two projects.

3. **Project: `sales-transactions-analysis`**
   - Inputs: `sales_transactions.csv`
   - Working/exploration artifact: `sales_transactions.ipynb`
   - Scripted version: `sales_transactions.py`
   - Output/result: `updated_sales_transactions.csv`
   - This looks like a transaction-data cleaning and analysis workflow that starts from raw CSV and produces a revised CSV.

4. **Project: `student-marks-cleaning`**
   - Inputs: `student_marks.csv`
   - Working/exploration artifact: `student_marks.ipynb`
   - Scripted version: `student_marks.py`
   - Output/result: `cleaned_student_marks.csv`
   - This appears to be a data-cleaning pipeline for student score records, again transforming raw CSV into a cleaned dataset.

5. **Data flow pattern**
   - The common flow in both projects is: raw CSV → exploratory notebook/script → cleaned or updated CSV.
   - The notebook likely supports iterative inspection and manual experimentation, while the `.py` file represents the runnable, linearized version of the same process.

6. **Role of notebooks vs scripts**
   - The `.ipynb` files are the interactive analysis layer, useful for step-by-step inspection, visualization, and trial-and-error cleaning.
   - The `.py` files are the executable, non-interactive form of the workflow, likely used to reproduce the same transformations outside the notebook environment.

7. **Outputs as downstream artifacts**
   - The cleaned CSV files are the main deliverables of the projects, not just intermediate files.
   - They serve as the boundary between raw data and analysis-ready data, which is the key architectural output in this repo.

8. **Technology/runtime**
   - The repo is centered on Python and CSV-based data handling.
   - The presence of Jupyter notebooks suggests the use of an interactive Python environment for exploratory data work.
   - No external services, databases, APIs, or deployment infrastructure are indicated by the file tree.

9. **How to diagram this repository**
   - Draw two parallel, isolated pipelines rather than one system.
   - For each pipeline, show raw CSV input feeding both a notebook and a Python script, with the script/notebook producing a cleaned CSV output.
   - The only shared context is that both are learning-oriented Python data wrangling projects under the same repository root.

10. **Overall architectural takeaway**
   - This repo is best understood as a collection of reproducible data-cleaning exercises, not an application with runtime components or service boundaries.
   - Its architecture is dominated by file-based data transformation and experimentation, with each subproject acting as a standalone unit.

Main entry points: [`sales-transactions-analysis/sales_transactions.ipynb`](https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.ipynb) (Notebook), [`student-marks-cleaning/student_marks.ipynb`](https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.ipynb) (Notebook), [`sales-transactions-analysis/sales_transactions.py`](https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.py) (Script), [`student-marks-cleaning/student_marks.py`](https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.py) (Script)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_repo_root["Repo root"]
  node_root_readme["README<br/>repo overview<br/>[README.md]"]
end

subgraph group_sales_transactions_analysis["Sales transactions"]
  node_sales_raw["Raw CSV<br/>input dataset"]
  node_sales_notebook["Notebook<br/>exploration"]
  node_sales_script["Script"]
  node_sales_output["Updated CSV<br/>result dataset"]
end

subgraph group_student_marks_cleaning["Student marks"]
  node_student_raw["Raw CSV<br/>input dataset<br/>[student_marks.csv]"]
  node_student_notebook["Notebook<br/>exploration"]
  node_student_script["Script<br/>[student_marks.py]"]
  node_student_output["Cleaned CSV<br/>result dataset"]
end

node_root_readme -.->|"describes"| node_sales_notebook
node_root_readme -.->|"describes"| node_student_notebook
node_sales_raw -->|"inspect"| node_sales_notebook
node_sales_notebook -->|"linearize"| node_sales_script
node_sales_raw -->|"process"| node_sales_script
node_sales_script -->|"produce"| node_sales_output
node_sales_notebook -.->|"refine"| node_sales_output
node_student_raw -->|"inspect"| node_student_notebook
node_student_notebook -->|"linearize"| node_student_script
node_student_raw -->|"process"| node_student_script
node_student_script -->|"produce"| node_student_output
node_student_notebook -.->|"refine"| node_student_output

click node_root_readme "https://github.com/gauravgawade2008/python/blob/main/README.md"
click node_sales_raw "https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.csv"
click node_sales_notebook "https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.ipynb"
click node_sales_script "https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.py"
click node_sales_output "https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/updated_sales_transactions.csv"
click node_student_raw "https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.csv"
click node_student_notebook "https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.ipynb"
click node_student_script "https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.py"
click node_student_output "https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/cleaned_student_marks.csv"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_root_readme toneBlue
class node_sales_raw,node_sales_notebook,node_sales_script,node_sales_output toneAmber
class node_student_raw,node_student_notebook,node_student_script,node_student_output toneMint
```

## Components

### Repo root

Learning workspace with two independent Python data-wrangling exercises.

- [README](https://github.com/gauravgawade2008/python/blob/main/README.md): repo overview · Describes the repository as a learning workspace. · `README.md`

### Sales transactions

Standalone transaction cleaning and analysis pipeline.

- [Raw CSV](https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.csv): input dataset · Source sales transaction data. · `sales-transactions-analysis/sales_transactions.csv`
- [Notebook](https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.ipynb): exploration · Interactive analysis and cleaning workspace. · `sales-transactions-analysis/sales_transactions.ipynb`
- [Script](https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/sales_transactions.py): pipeline script · Executable linear version of the sales workflow. · `sales-transactions-analysis/sales_transactions.py`
- [Updated CSV](https://github.com/gauravgawade2008/python/blob/main/sales-transactions-analysis/updated_sales_transactions.csv): result dataset · Cleaned and revised sales transaction output. · `sales-transactions-analysis/updated_sales_transactions.csv`

### Student marks

Standalone student marks cleaning pipeline.

- [Raw CSV](https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.csv): input dataset · Source student marks data. · `student-marks-cleaning/student_marks.csv`
- [Notebook](https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.ipynb): exploration · Interactive inspection and cleaning workspace. · `student-marks-cleaning/student_marks.ipynb`
- [Script](https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/student_marks.py): pipeline script · Executable linear version of the student marks workflow. · `student-marks-cleaning/student_marks.py`
- [Cleaned CSV](https://github.com/gauravgawade2008/python/blob/main/student-marks-cleaning/cleaned_student_marks.csv): result dataset · Cleaned student marks output. · `student-marks-cleaning/cleaned_student_marks.csv`

## Connections

- README → Notebook: describes · Repository context points to the sales analysis exercise.
- README → Notebook: describes · Repository context points to the student marks exercise.
- Raw CSV → Notebook: inspect · Raw sales data is explored interactively.
- Notebook → Script: linearize · Notebook work is translated into a runnable script.
- Raw CSV → Script: process · The script consumes the raw sales CSV.
- Script → Updated CSV: produce · Script writes the updated sales dataset.
- Notebook → Updated CSV: refine · Interactive analysis informs the cleaned output.
- Raw CSV → Notebook: inspect · Raw student marks are explored interactively.
- Notebook → Script: linearize · Notebook work is translated into a runnable script.
- Raw CSV → Script: process · The script consumes the raw student marks CSV.
- Script → Cleaned CSV: produce · Script writes the cleaned student marks dataset.
- Notebook → Cleaned CSV: refine · Interactive analysis informs the cleaned output.