Data Wrangling Using Pandas, SQL, and Java

• Autore: Campesato Oswald
• Pubblicazione: Bloomfield : , : Mercury Learning & Information, , 2022; ©2022
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (275 pages)
• Disciplina: 001.642
• Soggetto topico: Computer programming; Pandas
• 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 -- Quotation 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 in Python -- Formatting Numbers in Python -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings in Python -- 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 Alignmet -- Working with Dates -- Converting Strings to Dates -- Exception Handling -- Handling User Input -- Command-Line Arguments -- Summary -- Chapter 2: Working with DataDealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data vs. Continuous Data -- "Binning" Continuous Data -- Scaling Numeric Data via Normalization -- ScalingNumeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- MappingCategorical 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 Pandas -- What is Pandas? -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- A Pandas Data Frame Example -- Describing a Pandas Data Frame -- Pandas Boolean Data Frames -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Converting Categorical Data to Numeric Data -- Merging and Splitting Columns in Pandas -- Combining Pandas Data Frames -- Data Manipulation with Pandas Data Frames -- Pandas Data Frames and CSV Files -- Useful Options for the Pandas read_csv() Function -- Reading Selected Rows from CSV Files -- Pandas Data Frames and Excel Spreadsheets -- Useful Options for Reading Excel Spreadsheets -- Select, Add, and Delete Columns in Data Frames -- Handling Outliers in Pandas -- Pandas Data Frames and Simple Statistics -- Finding Duplicate Rows in Pandas -- Finding Missing Values in Pandas -- Missing Values in an Iris-Based Dataset -- Sorting Data Frames in Pandas -- Working with groupby() in Pandas -- Aggregate Operations with the titanic.csv Dataset -- Working with apply() and mapapply() in Pandas -- Working with JSON-based Data -- Python Dictionary and JSON -- Python, Pandas, and JSON -- Summary -- Chapter 4: RDBMS and SQL -- What is an RDBMS? -- What Relationships Do Tables Have in an RDBMS? -- Features of an RDBMS -- What is ACID? -- When Do We Need an RDBMS? -- The Importance of Normalization -- A Four-Table RDBMS -- Detailed Table Descriptions -- The customers Table -- The purchase_orders Table -- The line_items Table -- The item_desc Table -- What is SQL? -- DCL, DDL, DQL, DML, and TCL -- SQL Privileges -- Properties of SQL Statements -- The CREATE Keyword -- What about MariaDB? -- Summary -- Index.
• Sommario/riassunto: "This book contains a fast-paced introduction to as much relevant information about managing data that can be reasonably included in a book of this size. However, you will be exposed to a variety of features of NumpPy and Pandas, how to create databases and tables in MySQL, and how to perform many data cleaning tasks and data wrangling. Some topics are presented in a cursory manner, which is for two main reasons. First, it's important that you be exposed to these concepts. In some cases, you will find topics that might pique your interest, and hence motivate you to learn more about them through self-study; in other cases, you will probably be satisfied with a brief introduction. In other words, you will decide whether or not to delve into more detail regarding the topics in this book. Second, a full treatment of all the topics that are covered in this book would significantly increase the its size of this book, and few people have the time to read technical tomes"--
• Titolo autorizzato: Data Wrangling Using Pandas, SQL, and Java
• ISBN: 9781683929024; 1683929020; 9781683929031; 1683929039
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911004813803321
• Lo trovi qui: Univ. Federico II