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.

Notebook 1: https://yint.org/pybasic01

Printing output to the screen
Creating variables
Variable types
Calculations with variables
Built-in constants and mathematical functions

Notebook 2: https://yint.org/pybasic02

Strings
Lists
For loops: iterating
Commenting and variable names

Notebook 3: https://yint.org/pybasic03

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

Notebook 4: https://yint.org/pybasic04

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.

Notebook 5: https://yint.org/pybasic05

Mathematical operations (addition, multiplication, matrix math) on arrays.
Challenge problems that require using vectors and matrices.

Notebook 6: https://yint.org/pybasic06

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.

Notebook 7: https://yint.org/pybasic07

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.

Notebook 8: https://yint.org/pybasic08

Iterating over entries in a dictionary.
Getting and setting values in dictionaries.
Reading data from many CSV or Excel files.
Moving average calculations.

Notebook 9: https://yint.org/pybasic09

Statistics and Data Visualization: combined
Box plots; mean, median, percentiles
Bar plots; categorical vs numeric variables
Histograms; visualizing distribution and Central Limit Theorem

Notebook 10: https://yint.org/pybasic10

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

Learning Python: focus on getting results

In these series of 6 notebooks the focus is for people with some/minimal experience in coding (e.g. MATLAB, C++) to get started with Python. The target audience is process engineers and scientists. People that need to get some sort of value extracted from their data.

As such, it does not teach loops, branching, data structures, etc. In other words, the theoretical computer science concepts are introduced as needed, but not explicitly.

Notebook 11: https://yint.org/pybasic11

Printing output to the screen
Creating variables, and basic calculations with them
Lists

Notebook 12: https://yint.org/pybasic12

Using and understanding the Pandas library
Creating Pandas series and data frames and basic calculations with them
Reading in Excel files with Pandas and basic calculation

Notebook 13: https://yint.org/pybasic13

Becoming more comfortable with Pandas data processing
Shape of data, unique values, adding/removing data, merging dataframes, sorting data
Basic plotting with Pandas: box plot, time series and scatter plots

Notebook 14: https://yint.org/pybasic14

For loops, for when we need to do things over and over
The groupby function, for repeated actions on sub-groups of your data
Calculating the correlation matrix

Notebook 15: https://yint.org/pybasic15

Visualize the correlation matrix (2D histograms)
Using the LinearRegression tool from scikit-learn
The .iloc function in Pandas to split data into testing and training subset
Using seaborn to visualize regression models

Notebook 16: https://yint.org/pybasic16

Pandas collection: reading in subsets of data
Handling missing values with Pandas
Filtering data, and the multi-level groupby capability of Panda
Effective table display in Pandas

License

kgdunn/python-basic-notebooks

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