Acquire the ability to implement high-level programs in the interpreted language Python . Understand the main constructs used in Python and its application to scientific computing and data processing scenarios.
Curriculum
scheda docente
materiale didattico
• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; 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; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling 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; object 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 comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.
Programma
The course will cover the following aspects of Python programming:• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; 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; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling 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; object 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 comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.
Testi Adottati
Allen B. Downey, “Think in Python" (2nd edition)”, Green Tea Press, 2015Bibliografia Di Riferimento
https://greenteapress.com/thinkpython2/thinkpython2.pdfModalità Frequenza
Attendance is not mandatory, but it is strongly recommended (see the evaluation details).Modalità Valutazione
The assessment is divided into three parts: • Practical exam [max 20 points], which can be completed through: - 5 in-class exercises (every Thursday, except the first one) to be submitted by the end of the lesson - A short final project to be agreed upon with the instructor • Final multiple-choice written exam [max 10 points] • Oral exam (optional)
scheda docente
materiale didattico
• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; 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; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling 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; object 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 comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.
Programma
The course will cover the following aspects of Python programming:• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; 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; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling 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; object 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 comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.
Testi Adottati
Allen B. Downey, “Think in Python" (2nd edition)”, Green Tea Press, 2015Bibliografia Di Riferimento
https://greenteapress.com/thinkpython2/thinkpython2.pdfModalità Frequenza
Attendance is not mandatory, but it is strongly recommended (see the evaluation details).Modalità Valutazione
The assessment is divided into three parts: • Practical exam [max 20 points], which can be completed through: - 5 in-class exercises (every Thursday, except the first one) to be submitted by the end of the lesson - A short final project to be agreed upon with the instructor • Final multiple-choice written exam [max 10 points] • Oral exam (optional)
scheda docente
materiale didattico
• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; 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; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling 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; object 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 comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.
Programma
The course will cover the following aspects of Python programming:• Introduction to Programming: Computer architectures; memory and data; CPU and programs; programming languages; problems, algorithms, and programs.
• Using the Python Interpreter: Invoking the interpreter; passing arguments; interactive mode; notebooks; online coding platforms.
• Basic Python Programming Concepts: Variables and assignments; expressions and statements; operations; printing; comments; debugging; data types; numbers and strings; input.
• Functions: Built-in functions; function calls; importing modules and functions; mathematical functions; function composition; defining new functions; parameters and arguments; required and optional arguments; argument order and keyword assignment; variable scope.
• Making Decisions: Boolean expressions and logical operators; conditional and alternative execution; if-elif-else structure; chained and nested conditionals.
• Iterations: Variable reassignment and updates; while loop; break statement; sequences and loops; the in operator; 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; search and reverse search; variable-length arguments.
• Files: Persistence; opening and closing files with the with statement; reading and writing; format operator; file names and paths; handling 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; object 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 comprehensions; generator expressions; any and all operators; sets.
• Scientific Programming: Numpy, arrays, and broadcasting; Pandas, dataframes, and series; Scikit-Learn and an introduction to machine learning with Python; Matplotlib and data visualization in Python.
Testi Adottati
Allen B. Downey, “Think in Python" (2nd edition)”, Green Tea Press, 2015Bibliografia Di Riferimento
https://greenteapress.com/thinkpython2/thinkpython2.pdfModalità Frequenza
Attendance is not mandatory, but it is strongly recommended (see the evaluation details).Modalità Valutazione
The assessment is divided into three parts: • Practical exam [max 20 points], which can be completed through: - 5 in-class exercises (every Thursday, except the first one) to be submitted by the end of the lesson - A short final project to be agreed upon with the instructor • Final multiple-choice written exam [max 10 points] • Oral exam (optional)