Scientific Computing & Ipython Notebook: How to organize code?

Question

I\'m using Ipython Notebook to my research. As my file grows bigger, I constantly extract code out, things like plot method, fitting method etc.

I think I need a way to

Answer 1

While the given answers cover the topic thoroughly it is still worth mentioning Cookiecutter which provides a data science boilerplate project structure:

Cookiecutter Data Sciencee

provides data science template for projects in Python with a logical, reasonably standardized, yet flexible project structure for doing and sharing data science work.

Your analysis doesn't have to be in Python, but the template does provide some Python boilerplate (in the src folder for example, and the Sphinx documentation skeleton in docs). However, nothing is binding.

The following quote from the project description sums it up pretty nicely:

Nobody sits around before creating a new Rails project to figure out where they want to put their views; they just run rails new to get a standard project skeleton like everybody else.

Requirements:

• Python 2.7 or 3.5
• cookiecutter Python package >= 1.4.0: pip install cookiecutter

Getting started

Starting a new project is as easy as running this command at the command line. No need to create a directory first, the cookiecutter will do it for you.

cookiecutter https://github.com/drivendata/cookiecutter-data-science

Directory structure

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results-oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Related:

ProjectTemplate - provides a similar system for R data analysis.