Università Roma Tre

The course will cover the following aspects of programming in Python:
• An introduction to programming: computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms and programs.
• How to use the Python interpreter: invoking the interpreter; argument passing; interactive mode; notebooks; online coding platforms.
• Basic concepts of Python programming: variables and assignments; expressions and statements; operations; printing; comments; debbugging; data types; numbers and strings; input.
• Functions: built-in functions; function calls; importing modules and functions; math functions; function composition; defining new functions; parameters and arguments; mandatory vs. optional arguments; arguments’ order and keyword assignment; scope of a variable.
• Taking decisions: boolean expressions and logical operators; conditional and alternative execution; if-elif-else statements; chained vs. nested conditionals.
• Iterations: reassignment and updating variables; the while statement; the break statement; sequences and looping; the in operator; the for loop.
• Data structures (strings, lists, tuples, dictionaries): definition, properties, operations and methods; indexing vs. assignment; mutability and immutability; map, filter and reduce; referencing and aliasing; packing and unpacking; lookup and reverse lookup; variable-length arguments.
• Files: persistence; opening and closing and the with construct; reading and writing; format operator; filenames and paths; catching exceptions; pickling.
• Modules and packages: defining a module; defining a package; importing a package vs. importing a module vs. importing a function; installing packages.
• Classes and objects: classes, types, objects and instances; instances as return values; attributes and methods; objects mutability; instantiation and the __init__ method; operator overloading and special methods; static methods and class methods; inheritance.
• Pythonic programming: conditional expressions; EAFP (Easier to Ask for Forgiveness than Permission); list comprehension; generator expressions; any and all; sets.
• Scientific programming: Numpy, arrays and broadcasting; Pandas, dataframes and series; Scikit-learn and basic machine learning with Python; Matplotlib and plotting in Python

Allen B. Downey, “Think Python: How to Think Like a Computer Scientist (2nd Edition)”, O’Reilly, ISBN-13: 978-1491939369