Learning Python: basic level
These notebook files are intended to help you self-learn Python.
The audience already has a basic idea about programming, about loops, about structure of source code.
Python can be used in many areas of application, but these notebooks, as you will see, have examples primarily from science, engineering, life sciences, and applied mathematics.
The topics listed below give an idea of what is covered. Within in each notebook are a series of simple or more challenging problems. The problems are designed to build on the topics just learned, as well as the topics from earlier notebooks.
- Printing output to the screen
- Creating variables
- Variable types
- Calculations with variables
- Built-in constants and mathematical functions
- For loops: iterating
- Commenting and variable names
- From strings to lists to strings to lists
- If and else if branching in code
- Reading from a file
- Creating errors, checking for errors and handling errors
- Challenge problems
- Introducing vectors, matrices and arrays.
- Using NumPy to create various vectors, matrices and arrays, containing specific values.
- Get everyone to more or less the same level of understanding.
- Mathematical operations (addition, multiplication, matrix math) on arrays.
- Challenge problems that require using vectors and matrices.
- Functions with single inputs, or multiple inputs: arguments based on their position, or name.
- Functions with no (None) or single outputs, and multiple outputs in a tuple.
- Challenge problems that recall work from prior modules, and apply your knowledge.
- Dictionary objects in Python: the very basics.
- Introducing Pandas' two main classes: Series and DataFrame: what they are, and how to use them.
- Loading and saving data to/from CSV and Excel files.
- Iterating over entries in a dictionary.
- Getting and setting values in dictionaries.
- Reading data from many CSV or Excel files.
- Moving average calculations.
- Statistics and Data Visualization: combined
- Box plots; mean, median, percentiles
- Bar plots; categorical vs numeric variables
- Histograms; visualizing distribution and Central Limit Theorem
- Goals of your data analysis project broken down
- Data tables; correlations; pie charts are not so useful
- Time-series; trends, induced delay from a moving average, random walks
- Scatter plots; showing 5 variables in 1 plot
- Extending the box plot: violin, swarm and raincloud plots