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Linear Algebra with Python : Theory and Applications / Makoto Tsukada ... [et al.]
| Linear Algebra with Python : Theory and Applications / Makoto Tsukada ... [et al.] |
| Pubbl/distr/stampa | Singapore, : Springer, 2023 |
| Descrizione fisica | xv, 309 p. : ill. ; 24 cm |
| Soggetto topico |
15-XX - Linear and multilinear algebra; matrix theory [MSC 2020]
68-XX - Computer science [MSC 2020] |
| Soggetto non controllato |
Dynamical systems
Fourier expansions Generalized inverse Jordan normal form Jupyter Notebook KL Expansion Kalman Filter Markov Chains Markov random fields Matplotlib Matrix Representation NumPy One-Parameter Semigroups Orthogonal Projection Peron-Frobenius Theorem Python Singular value decomposition Spectral Radius SymPy Tensor products |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00279239 |
| Singapore, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Pandas Basics
| Pandas Basics |
| Autore | Campesato Oswald |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2022 |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina | 005.133 |
| Soggetto topico | COMPUTERS / Programming Languages / Python |
| Soggetto non controllato |
Computer Science
Data Science Developers Matplotlib NumPy Programming Python Seaborn data mining |
| ISBN |
9781683928249
1683928245 9781683928256 1683928253 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-lines -- Quotations and Comments -- Saving Your Code in a Module -- Some Standard Modules -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers -- Working with Other Bases -- The chr() Function -- The round() Function -- Formatting Numbers -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings -- Uninitialized Variables and the Value None -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Converting Strings to Dates -- Exception Handling -- Handling User Input -- Command-line Arguments -- Summary -- Chapter 2: Working with Data -- Dealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data Versus Continuous Data -- Binning Continuous Data -- Scaling Numeric Data via Normalization -- Scaling Numeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- Mapping Categorical Data to Numeric Values -- Working with Dates -- Working with Currency -- Working with Outliers and Anomalies -- Outlier Detection/Removal -- Finding Outliers with NumPy.
Finding Outliers with Pandas -- Calculating Z-scores to Find Outliers -- Finding Outliers with SkLearn (Optional) -- Working with Missing Data -- Imputing Values: When is Zero a Valid Value? -- Dealing with Imbalanced Datasets -- What is SMOTE? -- SMOTE extensions -- The Bias-Variance Tradeoff -- Types of Bias in Data -- Analyzing Classifiers (Optional) -- What is LIME? -- What is ANOVA? -- Summary -- Chapter 3: Introduction to Probability and Statistics -- What is a Probability? -- Calculating the Expected Value -- Random Variables -- Discrete versus Continuous Random Variables -- Well-known Probability Distributions -- Fundamental Concepts in Statistics -- The Mean -- The Median -- The Mode -- The Variance and Standard Deviation -- Population, Sample, and Population Variance -- Chebyshev's Inequality -- What is a p-value? -- The Moments of a Function (Optional) -- What is Skewness? -- What is Kurtosis? -- Data and Statistics -- The Central Limit Theorem -- Correlation versus Causation -- Statistical Inferences -- Statistical Terms: RSS, TSS, R^2, and F1 Score -- What is an F1 score? -- Gini Impurity, Entropy, and Perplexity -- What is the Gini Impurity? -- What is Entropy? -- Calculating the Gini Impurity and Entropy Values -- Multi-dimensional Gini Index -- What is Perplexity? -- Cross-Entropy and KL Divergence -- What is Cross-Entropy? -- What is KL Divergence? -- What's Their Purpose? -- Covariance and Correlation Matrices -- The Covariance Matrix -- Covariance Matrix: An Example -- The Correlation Matrix -- Eigenvalues and Eigenvectors -- Calculating Eigenvectors: A Simple Example -- Gauss Jordan Elimination (Optional) -- PCA (Principal Component Analysis) -- The New Matrix of Eigenvectors -- Well-known Distance Metrics -- Pearson Correlation Coefficient -- Jaccard Index (or Similarity) -- Local Sensitivity Hashing (Optional). Types of Distance Metrics -- What is Bayesian Inference? -- Bayes' Theorem -- Some Bayesian Terminology -- What is MAP? -- Why Use Bayes' Theorem? -- Summary -- Chapter 4: Introduction to Pandas (1) -- What is Pandas? -- Pandas Options and Settings -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- Alternatives to Pandas -- A Pandas Data Frame with a NumPy Example -- Describing a Pandas Data Frame -- Pandas Boolean Data Frames -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Reading CSV Files in Pandas -- Specifying a Separator and Column Sets in Text Files -- Specifying an Index in Text Files -- The loc() and iloc() Methods in Pandas -- Converting Categorical Data to Numeric Data -- Matching and Splitting Strings in Pandas -- Converting Strings to Dates in Pandas -- Working with Date Ranges in Pandas -- Detecting Missing Dates in Pandas -- Interpolating Missing Dates in Pandas -- Other Operations with Dates in Pandas -- Merging and Splitting Columns in Pandas -- Reading HTML Web Pages in Pandas -- Saving a Pandas Data Frame as an HTML Web Page -- Summary -- Chapter 5: Introduction to Pandas (2) -- Combining Pandas Data Frames -- Data Manipulation with Pandas Data Frames (1) -- Data Manipulation with Pandas Data Frames (2) -- Data Manipulation with Pandas Data Frames (3) -- Pandas Data Frames and CSV Files -- Managing Columns in Data Frames -- Switching Columns -- Appending Columns -- Deleting Columns -- Inserting Columns -- Scaling Numeric Columns -- Managing Rows in Pandas -- Selecting a Range of Rows in Pandas -- Finding Duplicate Rows in Pandas -- Inserting New Rows in Pandas -- Handling Missing Data in Pandas -- Multiple Types of Missing Values -- Test for Numeric Values in a Column -- Replacing NaN Values in Pandas -- Summary -- Chapter 6: Introduction to Pandas (3) -- Threshold Values and Outliers. The Pandas Pipe Method -- Pandas query() Method for Filtering Data -- Sorting Data Frames in Pandas -- Working with groupby() in Pandas -- Working with apply() and mapapply() in Pandas -- Handling Outliers in Pandas -- Pandas Data Frames and Scatterplots -- Pandas Data Frames and Simple Statistics -- Aggregate Operations in Pandas Data Frames -- Aggregate Operations with the titanic.csv Dataset -- Save Data Frames as CSV Files and Zip Files -- Pandas Data Frames and Excel Spreadsheets -- Working with JSON-based Data -- Python Dictionary and JSON -- Python, Pandas, and JSON -- Window Functions in Pandas -- Useful One-line Commands in Pandas -- What is pandasql? -- What is Method Chaining? -- Pandas and Method Chaining -- Pandas Profiling -- Alternatives to Pandas -- Summary -- Chapter 7: Data Visualization -- What is Data Visualization? -- Types of Data Visualization -- What is Matplotlib? -- Lines in a Grid in Matplotlib -- A Colored Grid in Matplotlib -- Randomized Data Points in Matplotlib -- A Histogram in Matplotlib -- A Set of Line Segments in Matplotlib -- Plotting Multiple Lines in Matplotlib -- Trigonometric Functions in Matplotlib -- Display IQ Scores in Matplotlib -- Plot a Best-Fitting Line in Matplotlib -- The Iris Dataset in Sklearn -- Sklearn, Pandas, and the Iris Dataset -- Working with Seaborn -- Features of Seaborn -- Seaborn Built-in Datasets -- The Iris Dataset in Seaborn -- The Titanic Dataset in Seaborn -- Extracting Data from the Titanic Dataset in Seaborn (1) -- Extracting Data from the Titanic Dataset in Seaborn (2) -- Visualizing a Pandas Dataset in Seaborn -- Data Visualization in Pandas -- What is Bokeh? -- Summary -- Index. |
| Record Nr. | UNINA-9911006690203321 |
Campesato Oswald
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| Bloomfield : , : Mercury Learning & Information, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Pyomo - Optimization Modeling in Python / Michael L. Bynum ... [et al.]
| Pyomo - Optimization Modeling in Python / Michael L. Bynum ... [et al.] |
| Edizione | [3. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xvii, 225 p. : ill. ; 24 cm |
| Soggetto topico |
90C90 - Applications of mathematical programming [MSC 2020]
68W30 - Symbolic computation and algebraic computation [MSC 2020] 00A71 - General theory of mathematical modeling [MSC 2020] 68N15 - Theory of programming languages [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] |
| Soggetto non controllato |
Algebraic modeling languages
Hybrid optimization Mathematical modeling tool Matplotlib Modeling and simulation NumPy PySP Pyomo modeling library Pyomo tutorial Python data Python optimization Python script SciPy |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0275136 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Pyomo - Optimization Modeling in Python / Michael L. Bynum ... [et al.]
| Pyomo - Optimization Modeling in Python / Michael L. Bynum ... [et al.] |
| Edizione | [3. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xvii, 225 p. : ill. ; 24 cm |
| Soggetto topico |
00A71 - General theory of mathematical modeling [MSC 2020]
68N15 - Theory of programming languages [MSC 2020] 68W30 - Symbolic computation and algebraic computation [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] 90C90 - Applications of mathematical programming [MSC 2020] |
| Soggetto non controllato |
Algebraic modeling languages
Hybrid optimization Mathematical modeling tool Matplotlib Modeling and simulation NumPy PySP Pyomo modeling library Pyomo tutorial Python data Python optimization Python script SciPy |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00275136 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Pyomo - Optimization Modeling in Python / William E. Hart ... [et al.]
| Pyomo - Optimization Modeling in Python / William E. Hart ... [et al.] |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xviii, 277 p. : ill. ; 24 cm |
| Soggetto topico |
90C90 - Applications of mathematical programming [MSC 2020]
68W30 - Symbolic computation and algebraic computation [MSC 2020] 00A71 - General theory of mathematical modeling [MSC 2020] 68N15 - Theory of programming languages [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] |
| Soggetto non controllato |
Algebraic modeling languages
Hybrid optimization Mathematical modeling tool Matplotlib Modeling and simulation NumPy PySP Pyomo modeling library Pyomo tutorial Python data Python optimization Python script SciPy |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123512 |
| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Pyomo - Optimization Modeling in Python / William E. Hart ... [et al.]
| Pyomo - Optimization Modeling in Python / William E. Hart ... [et al.] |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xviii, 277 p. : ill. ; 24 cm |
| Soggetto topico |
00A71 - General theory of mathematical modeling [MSC 2020]
68N15 - Theory of programming languages [MSC 2020] 68W30 - Symbolic computation and algebraic computation [MSC 2020] 90-XX - Operations research, mathematical programming [MSC 2020] 90C90 - Applications of mathematical programming [MSC 2020] |
| Soggetto non controllato |
Algebraic modeling languages
Hybrid optimization Mathematical modeling tool Matplotlib Modeling and simulation NumPy PySP Pyomo modeling library Pyomo tutorial Python data Python optimization Python script SciPy |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00123512 |
| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Python Programming Using Problem Solving
| Python Programming Using Problem Solving |
| Autore | Bhasin Harsh |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomfield : , : Mercury Learning & Information, , 2023 |
| Descrizione fisica | 1 online resource (601 pages) |
| Disciplina | 005.133 |
| Soggetto topico |
Python (Computer program language)
COMPUTERS / General |
| Soggetto non controllato |
Matplotlib
NumPy Pandas algorithm business communication computer science engineering programming science |
| ISBN |
1-68392-861-X
1-68392-860-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title -- Title -- Copyright -- Dedication -- Content -- Preface -- Section I: Algorithmic Problem-Solving and Python Fundamentals -- Chapter 1: Algorithmic Problem-Solving -- 1.1 Introduction -- 1.2 Definition and Characteristics -- 1.3 Notations: Pseudocode and Flow Chart -- 1.4 Strategies for Problem-Solving: Recursion Versus Iteration -- 1.5 Asymptotic Notation -- 1.6 Complexity -- 1.7 Illustrations -- 1.7.1 Minimum in a List -- 1.7.2 Insert a Card in a Pack of Cards (Or Insert an element ina sorted list). There are ten cards in the pack, numbered from 1 to 10. -- 1.7.3 Guess a Number in a Given Range -- 1.7.4 Tower of Hanoi -- 1.8 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Application -- Chapter 2: Introduction to Python -- 2.1 Introduction -- 2.2 Features of Python -- 2.2.1 Easy -- 2.2.2 Type and Run -- 2.2.3 Syntax -- 2.2.4 Mixing -- 2.2.5 Dynamic Typing -- 2.2.6 Built-in Object Types -- 2.2.7 Numerous Libraries and Tools -- 2.2.8 Portable -- 2.2.9 Free -- 2.3 The Paradigms -- 2.3.1 Procedural -- 2.3.2 Object-Oriented -- 2.3.3 Functional -- 2.4 Chronology and Uses -- 2.4.1 Chronology -- 2.4.2 Uses -- 2.5 Installation of Anaconda -- 2.6 Implementation of an Algorithm: Statement, State, Control Blocks, and Functions -- 2.6.1 Statement -- 2.6.2 State -- 2.6.3 Control Flow -- 2.7 Conclusion -- Glossary -- Points to Remember -- Resources -- Exercises -- Multiple Choice Questions -- Theory -- Chapter 3: Fundamentals -- 3.1 Introduction -- 3.2 Basic Input Output -- 3.2.1 Print Function -- 3.2.2 Input -- 3.3 Running a Program -- 3.3.1 Using the Command Prompt -- 3.3.2 Executing Programs Written in .py Files -- 3.3.3 Using Anaconda Navigator -- 3.4 The Jupyter Notebook -- 3.5 Value Type and Reference Type -- 3.6 Tokens, Keywords, and Identifiers -- 3.6.1 Python Keywords.
3.6.2 Python Identifiers -- 3.6.3 Python Escape Sequence -- 3.7 Statements -- 3.7.1 Expression Statement -- 3.7.2 Assignment Statements -- 3.7.3 The Assert Statements -- 3.7.4 The Pass Statements -- 3.7.5 The Control Statements -- 3.8 Comments -- 3.9 Operators -- 3.10 Types and Examples of Operators -- 3.10.1 Arithmetic Operators -- 3.10.2 String Operators -- 3.10.3 Comparison Operators -- 3.10.4 Assignment Operators -- 3.10.5 Logical Operators -- 3.10.6 Priority of Operators -- 3.11 Basic Data Types -- 3.11.1 Integer -- 3.11.2 Float -- 3.11.3 String -- 3.12 Conclusion -- Exercises -- Multiple Choice Questions -- Theory -- Explore -- Section II: Procedural Programming -- Chapter 4: Conditional Statements -- 4.1 Introduction -- 4.2 "If," If-Else, and If-Elif-Else Constructs -- 4.3 The If-Elif-Else Ladder -- 4.4 Logical Operators -- 4.5 The Ternary Operator -- 4.6 The Get Construct -- 4.7 Examples -- 4.8 Summary -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Programming Exercises -- Chapter 5: Looping -- 5.1 Introduction -- 5.2 While -- 5.3 Patterns -- 5.4 Nesting and Applications of Loops in Lists -- 5.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Programming Exercises -- Chapter 6: Functions -- 6.1 Introduction -- 6.2 Features of a Function -- 6.2.1 Modular Programming -- 6.2.2 Reusability of Code -- 6.2.3 Manageability -- 6.2.3.1 Easy debugging -- 6.2.3.2 Efficient -- 6.3 Basic Terminology -- 6.3.1 Name of a Function -- 6.3.2 Arguments -- 6.3.3 Return Value -- 6.4 Definition and Invocation -- 6.4.1 Working -- 6.5 Types of Function -- 6.5.1 Arguments: Types of Arguments -- 6.6 Implementing Search -- 6.7 Scope -- 6.8 Recursion -- 6.8.1 Rabbit Problem -- 6.8.2 Disadvantages of Using Recursion -- 6.9 Conclusion -- Glossary -- Points to Remember -- Exercises. Multiple Choice Questions -- Programming Exercises -- Questions Based on Recursion -- Theory -- Extra Questions -- Chapter 7: File Handling -- 7.1 Introduction -- 7.2 The File Handling Mechanism -- 7.3 The Open Function and File Access Modes -- 7.4 Python Functions for File Handling -- 7.4.1 The Essential Ones -- 7.4.2 The OS Methods -- 7.4.3 Miscellaneous Functions and File Attributes -- 7.5 Command Line Arguments -- 7.6 Implementation and illustrations -- 7.7 Conclusion -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 8: Lists, tuple, and Dictionar -- 8.1 Introduction -- 8.2 Lists -- 8.2.1 Accessing Elements: Indexing and Slicing -- 8.2.2 Mutability -- 8.2.3 Operators -- 8.2.4 Traversal -- 8.2.5 Functions -- 8.3 Tuple -- 8.3.1 Accessing Elements of a Tuple -- 8.3.2 Nonmutability -- 8.3.3 Operators -- 8.3.4 Traversal -- 8.3.5 Functions -- 8.4 Associate Arrays and Dictionaries -- 8.4.1 Displaying Elements of a Dictionary -- 8.4.2 Some Important Functions of Dictionaries -- 8.4.2.1 The len function returns the number of elements in a given dictionary. -- 8.4.2.2 The max function returns the key with maximum value. If the key is a string, then the value in the lexicographic ordering would be returned. -- 8.4.2.3 The min function returns the key with minimum value. If the key is a string, then the value in the lexicographic ordering would be returned. -- 8.4.2.4 The sorted function would sort the elements of a given dictionary by their keys. If the keys are strings then lexicographic ordering would be followed. -- 8.4.2.5 The pop function takes out the element with the given key from the dictionary. -- 8.4.3 Input from the User -- 8.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises. Chapter 9: Iterations, Generators, and Comprehensions -- 9.1 Introduction -- 9.2 The Power of "For -- 9.3 Iterator -- 9.4 Defining an Iterable Object -- 9.5 Generators -- 9.6 Comprehensions -- 9.7 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 10: Strings -- 10.1 Introduction -- 10.2 Loops Revised -- 10.3 String Operators -- 10.3.1 The Concatenation Operator (+) -- 10.3.2 The Replication Operator (*) -- 10.3.3 The Membership Operator -- 10.4 In-Built Functions -- 10.4.1 len() -- 10.4.2 Capitalize() -- 10.4.3 Find() -- 10.4.4 Count -- 10.4.5 endswith() -- 10.4.6 encode -- 10.4.7 decode -- 10.4.8 Miscellaneous Functions -- 10.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Section III: Object-Oriented Programming -- Chapter 11: Introduction to Object-Oriented Paradigm -- 11.1 Introduction -- 11.2 Creating New Types -- 11.3 Attributes and Functions -- 11.3.1 Attributes -- 11.3.2 Functions -- 11.4 Elements of Object-Oriented Programming -- 11.4.1 Class -- 11.4.2 Object -- 11.4.3 Encapsulation -- 11.4.4 Data Hiding -- 11.4.5 Inheritance -- 11.4.6 Polymorphism -- 11.4.7 Reusability -- 11.5 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Explore and Design -- Chapter 12: Classes and Objects -- 12.1 Introduction to Classes -- 12.2 Defining a Class -- 12.3 Creating an Object -- 12.4 Scope of Data Members -- 12.5 Nesting -- 12.6 Constructor -- 12.7 Multiple __Init__(s) -- 12.8 Destructors -- 12.9 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 13: Inheritance -- 13.1 Introduction to Inheritance and Composition -- 13.1.1 Inheritance and Methods -- 13.1.2 Composition. 13.2 Inheritance: Importance and Types -- 13.2.1 Need for Inheritance -- 13.2.2 Types of Inheritance -- 13.2.2.1 Simple inheritance -- 13.2.2.2 Hierarchical inheritance -- 13.2.2.3 Multilevel inheritance -- 13.2.2.4 Multiple inheritance and hybrid inheritance -- 13.3 Methods -- 13.3.1 Bound Methods -- 13.3.2 Unbound Method -- 13.3.3 Methods are Callable Objects -- 13.3.4 The Importance and Usage of Super -- 13.3.5 Calling the Base Class Function Using Super -- 13.4 Search in Inheritance Tree -- 13.5 Class Interface and Abstract Classes -- 13.6 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 14: Operator Overloading -- 14.1 Introduction -- 14.2 __Init__ Revisited -- 14.2.1 Overloading __init__(Sort of) -- 14.3 Methods for Overloading Binary Operators -- 14.4 Overloading Binary Operators: The Fraction Example -- 14.5 Overloading the += Operator -- 14.6 Overloading the > -- and < -- Operators -- 14.7 Overloading the __Bool__ Operator: Precedence of __Bool__ Over __Len__ -- 14.8 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Chapter 15: Exception Handling -- 15.1 Introduction -- 15.2 Importance and Mechanism -- 15.2.1 An Example of Try/Except -- 15.2.2 Manually Raising Exceptions -- 15.3 Build-in Exceptions in Python -- 15.4 The Process -- 15.4.1 Example -- 15.4.2 Exception Handling: Try/Except -- 15.4.3 Raising Exceptions -- 15.5 Crafting User Defined Exceptions -- 15.6 An Example of Exception Handling -- 15.7 Conclusion -- Glossary -- Points to Remember -- Exercises -- Multiple Choice Questions -- Theory -- Programming Exercises -- Section IV: Numpy, Pandas, and Matplotlib -- Chapter 16: Numpy-I -- 16.1 Introduction -- 16.2 Fundamentals. 16.2.1 Similarity and Differences Between a List and a NumPy Array. |
| Record Nr. | UNINA-9910915680903321 |
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Bhasin Harsh
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| Bloomfield : , : Mercury Learning & Information, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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