Python and Jupyter for Science Crashcourse!

Install Anaconda to use a package and environment manager for science workflows. It includes 7500+ open source packages and tools like Jupiter, Navigator, and Spyder for data analysis.

Explore Python data types, including integers, floats, complex numbers, strings, and booleans, and learn to create lists and tuples, check types with type(), and understand mutability and zero-based indexing.

Learn how to write and call functions in Python, using def, parameters, and default values. Explore returning results, working with built-in functions, and calling functions from other packages in Jupyter.

Explore Python control structures by practicing if statements and else, using for loops with range, and touching on functions and code styling.

Explore how Python packages organize code and how to create and import your own package with modules and __init__.py. Learn to install third-party packages with pip or conda.

Learn how Python classes model objects with properties and methods, create list objects, define your own classes with __init__, and access attributes and functions using dot notation.

Learn to use pandas for tabular data in science, reading Excel files, exploring data frames, selecting by label or position, adding new columns, and applying groupby for mean calculations.

Explore numpy arrays for scientific computing in Python, including creating, indexing, and broadcasting; perform elementwise operations and matrix products with fast, precompiled C code.

Explore sympy, an open-source Python package for symbolic computation installed with Anaconda; define symbols, simplify expressions, solve equations, and plot functions.

Learn how to create and style data visualizations in Python with matplotlib, using object-oriented plots or pyplot, including pie plots, figures, axes, labels, legends, and basic styling.

Explore how Seaborn, Bokeh, and Plotly extend matplotlib for statistical data visualization, with interactive dashboards via Voilà and Jupyter notebooks.

Explore how temperature influences lithium ion battery resistance using Python and Jupyter, reading data from text files and applying electrochemical impedance spectroscopy insights with Arrhenius linearization and linear regression.

Learn to read text data with numpy loadtxt or pandas, loop through files, extract temperature from filenames, assemble data into arrays or dataframes, and export results to Excel.

Learn to read multiple temperature files with pandas, combine them into a single data frame using concat, extract file-name metadata, and explore the data with basic descriptive methods.

Learn to plot data with scatter plots, label axes and legends, filter out unwanted points, extend data frames with kelvin temperatures, and compute means with standard deviations and error bars.

Learn to plot data with matplotlib, create subplots, add error bars for means, customize labels and legends, and style graphs with patches and arrows for science plots.

Prepare data for linear regression by applying a natural log to exponential data, then fit a linear model with scikit-learn and evaluate with R^2, mean squared error, and plot results.

Learn to build a Python-based heat simulation of a lithium-ion battery, modeling cooling from 60 to under 40 degrees Celsius with ambient 20 degrees Celsius and surface heat transfer.

Explore heat conduction theory and its numerical solution in Python, converting a three-dimensional model to two-dimensional, and applying a finite difference method to simulate temperature over time.

Set boundary conditions on a temperature array for heat diffusion model, fixing interior at 60 degrees and edges at 20, using a deep copy for time steps and heat maps.

visualize airways with heat maps using Matplotlib pcolormesh, create subplots, and explore color maps and color bars from simulation data.

this lecture guides building and visualizing a heat map in Python using Matplotlib, sets up time steps and spatial grid, and prepares to implement the calculation with Python loops.

Learn the syntax and indentation of Python for loops, use range to repeat actions, print results, and build nested for loops with multiple variables and lists.

Explore a Python and Jupyter based heat diffusion model, implementing nested for loops and an explicit time stepping scheme, with visualization via Matplotlib and Seaborn heat maps.