Engineering Python
Absolute-beginner Python for engineers — language basics, data and control, functions and files, NumPy and pandas — on ExamMaster.
Undergraduate depth: concept notes and, where they help, in-browser labs. Reading is free.
Lessons
- List Comprehensions (Light)In Engineering Python because compact transforms of lists show up in labs — a comprehension is a for-loop written in one expression.
- ConditionalsIn Engineering Python because labs branch on thresholds and flags — if/elif/else is how a script chooses a path.
- Dicts and LookupIn Engineering Python because configuration and labelled measurements are key→value tables — dict lookup is the everyday map.
- Errors and try/exceptIn Engineering Python because labs fail on bad casts and missing keys — reading tracebacks and catching expected errors keeps a notebook usable.
- Expressions and OperatorsIn Engineering Python because every formula in a lab is an expression — arithmetic, comparison and string ops are the calculators you will reuse.
- Reading and Writing TextIn Engineering Python because labs exchange notes and small tables as text — open/read/write patterns matter even when the browser has no host disk.
- Defining FunctionsIn Engineering Python because labs reuse steps — def, parameters and return package a calculation you can call with new inputs.
- Hello ScriptsIn Engineering Python because every later lab starts by running instructions and reading output — print and comments are the first tools.
- Input and OutputIn Engineering Python because labs format results for humans and take simple values as inputs — even when the browser supplies them as variables.
- Lists and IndexingIn Engineering Python because measurements and feature rows arrive as ordered sequences — indexing, slicing and mutation are how you touch them.
- Loops: for and whileIn Engineering Python because labs repeat over rows and counters — for, while and range are how you accumulate results.
- Matplotlib ScatterIn Engineering Python because a two-column table is easier to read as points than as rows — scatter is the first plot after pandas.
- Modules and importIn Engineering Python because labs lean on the standard library — import math or json is how you reuse battle-tested code.
- NumPy ArraysIn Engineering Python because ML labs pass features as arrays — ndarray, shape and dtype are the vocabulary those labs assume.
- NumPy Ops and SlicingIn Engineering Python because labs scale features and take subsets — elementwise ops, slicing and aggregations replace slow Python loops.
- Light OOP: ShapesIn Engineering Python because a few labs group data with the formulas that belong to it — a class is that grouping, not a second language.
- Select and FilterIn Engineering Python because labs subset columns and rows — selection and boolean filters are the everyday pandas verbs.
- Series and DataFramesIn Engineering Python because tabular labs use labelled columns — Series and DataFrame are how pandas names those tables.
- Simple TransformsIn Engineering Python because feature columns are created and aggregated in pandas before they reach an ML estimator.
- Regular ExpressionsIn Engineering Python because lab notes hide numbers inside text — a pattern finds them without you writing a character-by-character loop.
- Scope and DefaultsIn Engineering Python because lab helpers take optional settings — local scope and default arguments cover the everyday cases without decorators.
- Strings in DepthIn Engineering Python because lab text is measured and searched — length, substring checks and counts are the everyday string tools.
- Tables for MLIn Engineering Python because AI/ML/DL labs assume you can build a feature table — rows as examples, columns as features — before fitting anything.
- Tuples and SetsIn Engineering Python because some sequences must not change and some collections only care about unique membership — tuple and set.
- Types: Numbers and StringsIn Engineering Python because labs mix counts, measurements and labels — knowing int, float, str and bool prevents silent wrong arithmetic.
- Variables and NamesIn Engineering Python because labs store intermediate values under names — assignment and rebinding are how every later calculation starts.
Practise Engineering Python
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