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[null,"1.","Record Nr.",null,"UNINA9910170354403321"]
[null,null,"Autore",null,"Tarantino, Stefania"]
[null,null,"Titolo",null,"Àneu metròs/senza madre : l'anima perduta dell'Europa : Maria Zambrano e Simone Weil / Stefania Tarantino"]
[null,null,"Pubbl/distr/stampa",null,"Napoli, : La scuola di Pitagora, 2014"]
[null,null,"ISBN",null,"978-88-6542-359-2"]
[null,null,"Descrizione fisica",null,"269 p. ; 20 cm"]
[null,null,"Collana",null,"Diotìma ; 15"]
[null,null,"Disciplina",null,"190.9"]
[null,null,"Locazione",null,"FLFBC"]
[null,null,"Collocazione",null,"P.1 P 635"]
[{"$nulls":4},"DAM A92.26 ZAMM/S 03"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Testo solo in italiano"]
[null,"2.","Record Nr.",null,"UNISALENTO991004373235407536"]
[null,null,"Autore",null,"Maugini, Armando"]
[null,null,"Titolo",null,"Sviluppo agricolo e progresso sociale dei paesi tropicali : scritti editi ed inediti / Armando Maugini ; prefazione di Giuseppe Vedovato"]
[null,null,"Pubbl/distr/stampa",null,"Firenze : Istituto agronomico per l'oltremare, 1966"]
[null,null,"Descrizione fisica",null,"XL, 649 p. ; 25 cm"]
[null,null,"Disciplina",null,"338.1"]
[null,null,"Soggetti",null,"Economia agraria - Paesi tropicali - Saggi"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"3.","Record Nr.",null,"UNINA9911006690203321"]
[null,null,"Autore",null,"Campesato Oswald"]
[null,null,"Titolo",null,"Pandas Basics"]
[null,null,"Pubbl/distr/stampa",null,"Bloomfield : , : Mercury Learning & Information, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"9781683928249"]
[{"$nulls":4},"1683928245"]
[{"$nulls":4},"9781683928256"]
[{"$nulls":4},"1683928253"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (215 pages)"]
[null,null,"Disciplina",null,"005.133"]
[null,null,"Soggetti",null,"COMPUTERS / Programming Languages / Python"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-lines -- Quotations and Comments -- Saving Your Code in a Module -- Some Standard Modules -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers -- Working with Other Bases -- The chr() Function -- The round() Function -- Formatting Numbers -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings -- Uninitialized Variables and the Value None -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Converting Strings to Dates -- Exception Handling -- Handling User Input -- Command-line Arguments -- Summary -- Chapter 2: Working with Data -- Dealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data Versus Continuous Data -- Binning Continuous Data -- Scaling Numeric Data via Normalization -- Scaling "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Finding Outliers with Pandas -- Calculating Z-scores to Find Outliers -- Finding Outliers with SkLearn (Optional) -- Working with Missing Data -- Imputing Values: When is Zero a Valid Value? -- Dealing with Imbalanced Datasets -- What is SMOTE? -- SMOTE extensions -- The Bias-Variance Tradeoff -- Types of Bias in Data -- Analyzing Classifiers (Optional) -- What is LIME? -- What is ANOVA? -- Summary -- Chapter 3: Introduction to Probability and Statistics -- What is a Probability? -- Calculating the Expected Value -- Random Variables -- Discrete versus Continuous Random Variables -- Well-known Probability Distributions -- Fundamental Concepts in Statistics -- The Mean -- The Median -- The Mode -- The Variance and Standard Deviation -- Population, Sample, and Population Variance -- Chebyshev's Inequality -- What is a p-value? -- The Moments of a Function (Optional) -- What is Skewness? -- What is Kurtosis? -- Data and Statistics -- The Central Limit Theorem -- Correlation versus Causation -- Statistical Inferences -- Statistical Terms: RSS, TSS, R^2, and F1 Score -- What is an F1 score? -- Gini Impurity, Entropy, and Perplexity -- What is the Gini Impurity? -- What is Entropy? -- Calculating the Gini Impurity and Entropy Values -- Multi-dimensional Gini Index -- What is Perplexity? -- Cross-Entropy and KL Divergence -- What is Cross-Entropy? -- What is KL Divergence? -- What's Their Purpose? -- Covariance and Correlation Matrices -- The Covariance Matrix -- Covariance Matrix: An Example -- The Correlation Matrix -- Eigenvalues and Eigenvectors -- Calculating Eigenvectors: A Simple Example -- Gauss Jordan Elimination (Optional) -- PCA (Principal Component Analysis) -- The New Matrix of Eigenvectors -- Well-known Distance Metrics -- Pearson Correlation Coefficient -- Jaccard Index (or Similarity) -- Local Sensitivity Hashing (Optional)."]
[{"$nulls":4},"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 -- "]
[{"$nulls":4},"Handling Missing Data in Pandas -- Multiple Types of Missing Values -- Test for Numeric Values in a Column -- Replacing NaN Values in Pandas -- Summary -- Chapter 6: Introduction to Pandas (3) -- Threshold Values and Outliers."]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","This book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. It contains a variety of code samples and features of NumPy and Pandas, and how to write regular expressions. Chapter 3 includes fundamental statistical concepts and Chapter 7 covers data visualization with Matplotlib and Seaborn. Companion files with code are available for downloading from the publisher. FEATURES:Provides the reader with numerous code samples for Pandas and NumPy programming concepts, and an introduction to statistical concepts and data visualizationIncludes an introductory chapter on PythonCompanion files with code"]