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##Python for Exploratory Computing

Lots of books are written on scientific computing, but very few deal with the much more common exploratory computing (a term coined by Fernando Perez), which represents daily tasks of many scientists and engineers that try to solve problems but are not computer scientists. This set of Notebooks is written for scientists and engineers who want to use Python programming for exploratory computing, scipting, data analysis, and visualization. Python makes many of these programmig tasks quick and easy and, probably most importantly, fun.

No prior knowledge of computer programming is assumed. Each Notebook covers a specific topic and includes a number of exercises. The exercises should take less than 4 hours to complete for each Notebook. Download the Notebooks and accompanying data files from the github repositories. These Notebooks contain empty output cells. Running the output cells is part of learning Python. Notebooks with output cells cells have the addition `_sol` and can be viewed by clicking on the links to the Notebook Viewer below.

The following Notebooks are available (they are under development; more will follow soon):

###Python Basics

Notebook 1: Basics and Plotting (version with empty output cells is here)

Notebook 2: Arrays and basic If statements (version with empty output cells is here)

Notebook 3: Loops and If/Else statements (version with empty output cells is here)

Notebook 4: Functions (version with empty output cells is here)

###Applications to Water Management

Notebook WM1: Time Series Data and Pandas

Notebook WM2: Manning's Equation and Emptying Reservoir

Notebook WM3: Water Distribution Systems

###Applications to Probability and Statistics

Notebook S1: Discrete Random Variables

Notebook S2: Continuous Random Variables

Notebook S3: Distribution of the Mean, Hypothesis Tests, and the Central Limit Theorem

Notebook S4: Linear regression and curve fitting