Here is small collection of links that I find useful.
Lectures on Scientific Computing with Python. - very good introduction to the topic writen as a collection of IPython Notebooks.
OceanPython.org - OceanPython.org is a website to learn Python Programming Language for ocean- and marine-science applications and to share Python code. OceanPython.org is maintained by students, staff and post-docs at the Department of Oceanography of Dalhousie University (Canada)
EarthPy - EarthPy is a collection of IPython notebooks with examples of Earth Science related Python code. It can be tutorials, descriptions of the modules, small scripts, or just tricks, that you think might be useful for others.
Python for the Atmospheric and Oceanic Sciences - blog that show atmospheric and oceanic sciences (AOS) newcomers to Python what is available, and helps update experienced users as to the cutting-edge of using Python in AOS
Python for Signal Processing - This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks
pyFerret - PyFerret is a Python module wrapping Ferret. The pyferret module provides Python functions so Python users can easily take advantage of the Ferret's abilities to retrieve, manipulate, visualize, and save data. There are also functions to move data between Python and the Ferret engine. Python scripts used as Ferret external functions.
python-ctd - Tools to load hydrographic data into pandas DataFrame (and some rudimentary methods for data pre-processing/analysis). This module can load SeaBird CTD (CNV), Sippican XBT (EDF), and Falmouth CTD (ASCII) formats.
pyresample - Resampling (reprojection) of geospatial image data in Python. Pyresample uses a kd-tree approach for resampling. Pyresample is designed for resampling of remote sensing data and supports resampling from both fixed grids and geolocated swath data.
jasmin-cis - This is a community effort to develop an automated model/data community intercomparison suite (CIS) within the JASMIN environment at the Centre for Environmental Data Archiving (CEDA) that will provide automated scientific analysis of heterogeneous datasets from models and observations.