Python general resources (for Hydrologists) to start with

There are a lot of resources to start with python, but for hydrologists, but here I tried, at least at the beginning, a list of readings to be quickly operative.

• Jupyter notebooks are a splendid way to organise calculations: you have first to lear how to use them (here a manual). 
• Lectures on scientific computing with Python by J.R. Johansson cover the main topics very nicely. the first four of more general interest:
• Lecture-0 Scientific Computing with Python 
• Lecture-1 Introduction to Python Programming 
• Lecture-2 Numpy - multidimensional data arrays 
• Lecture-3 Scipy - Library of scientific algorithms 
• Lecture-4 Matplotlib - 2D and 3D plotting 
• For Italians, my own introductory lectures have their place, I believe also because I used  Jupyter notebooks (and Python 3) to convey previous work by Joseph Eschgfaeller (translated from Python 2.7).
• Una introduzione gentile al Python scripting (mostly a translation from JE lectures)
• Esperimenti nella lettura di un file
• Leggere un file con  PANDAS (e plot dei dati con Matplotlib)
• Wanted to read a book freely available, please give then a look to Scipy Lecture Notes is a good (not necessarily quick) starting. The html version supports hyperlinks that the pdf one does not. 
• Kevin Sheppard's introduction to statistical analysis with Python also a manuscript to read.
• From Wes McKinney (creator of Pandas) Python for data analysis book:
• Chapter 2: Python Language Basics, IPython, and Jupyter Notebooks
• Chapter 3: Built-in Data Structures, Functions, and Files
• Chapter 4: NumPy Basics: Arrays and Vectorized Computation
• Chapter 5: Getting Started with pandas
• Chapter 6: Data Loading, Storage, and File Formats
• Chapter 7: Data Cleaning and Preparation
• Chapter 8: Data Wrangling: Join, Combine, and Reshape
• Chapter 9: Plotting and Visualization
• Chapter 10: Data Aggregation and Group Operations
• Chapter 11: Time Series
• Chapter 12: Advanced pandas
• Chapter 13: Introduction to Modeling Libraries in Python
• Chapter 14: Data Analysis Examples
• Appendix A: Advanced NumPy

• The main NumPy and SciPy documentation. 
• Python Scientific Lecture Notes a comprehensive set of tutorials on the scientific Python ecosystem. 
• Software Carpentry is an open source course on basic software development skills for people with backgrounds in science, engineering, and medicine. 
• Introduction to Statistics an introduction to the basic statistical concepts, combined with a complete set of application examples for the statistical data analysis with Python (by T. Haslwanter).