PyCreeper is an easy-to-use data analysis and plotting library for Python that I developed alongside working on various data science projects.
The main purpose of the library is to
wrap tens of lines of Python code, that one normally needs to create publication-ready
plots with matplotlib,
into one-line function calls that follow an established pattern.
PyCreeper takes away a data scientist's need to understand various quirks of
matplotlib and instead provides
ready-to-use and well-documented code.
The following plot types are supported in pyCreeper:
Bar plots
Matrix plots
Pie charts
Box plots
Line plots
Each plot type offers a number of customization options on how data is logically organized within the plot image.
Styling
A very useful feature of pyCreeper is
styles.
Without needing an in-depth knowledge of matplotlib, one can quickly
setup colors, line widths and fonts of their
plots. Perhaps more importantly, the default styles of pyCreeper make visual sense and result in
publication-quality plot images.
Because I am a big fan of
good object-oriented programming principles, styling is fully encapsulated withing the
crGraphStyle class, which
uses enumerations and type checking to ensure that only valid styles are being applied.
Data analysis
Included in the library are also the
crData and
crFiles classes
that contain utility functions for
reading raw data files,
checking data for consistency, manipulating multi-dimensional lists and performing simple analysis on lists of data.