Listing 1 - 7 of 7 |
Sort by
|
Choose an application
Data mining. --- Python (Computer program language). --- Programming --- Statistical science --- Mathematical statistics --- Python --- Programmeertalen --- Data mining --- Programming languages (Electronic computers) --- Python (Computer program language) --- 681.3*D32 --- Scripting languages (Computer science) --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 681.3*D32 language classifications: applicative languages; data-flow languages; design languages; extensible languages; macro and assembly languages; nonprocedural languages; specialized application and very high-level languages (Programminglanguages) --- language classifications: applicative languages; data-flow languages; design languages; extensible languages; macro and assembly languages; nonprocedural languages; specialized application and very high-level languages (Programminglanguages) --- Python (software) --- Programmeertaal --- Information Technology --- General and Others
Choose an application
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Python (Computer program language) --- Programming languages (Electronic computers) --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- PXL-IT 2018 --- programmeertalen --- Python --- Statistical science --- Mathematical statistics --- Programming --- Python (Computer program language). --- Programming languages (Electronic computers). --- Python 3.6. --- Datenanalyse. --- Datenmanagement. --- Data Mining. --- Database searching --- Electronic data processing --- Languages, Artificial --- Scripting languages (Computer science) --- Python 3.6 --- Datenanalyse --- Datenmanagement --- Data Mining --- Data mining --- python --- data-analyse --- data mining
Choose an application
"Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process
Data Mining. --- Data mining. --- Datenanalyse. --- Datenmanagement. --- Programming languages (Electronic computers). --- Python (Computer program language). --- Python 3.6. --- Python (Computer program language) --- Programming languages (Electronic computers) --- Exploration de données. --- Python (langage de programmation) --- Langages de programmation. --- Data mining
Choose an application
Choose an application
Apache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author’s 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange. This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You’ll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You’ll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You’ll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications. By the end of this book, you’ll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.
Choose an application
Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.10 und pandas 1.4, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und Zusatzmaterial zum Buch sind auf GitHub verfügbar.
Choose an application
"From fundamental techniques to advanced strategies for handling big data, visualization, and more, this book equips you with skills to excel in real-world data analysis projects. Key Features This book targets features in pandas 2.x and beyond Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. With this latest edition unlock the full potential of pandas 2.x onwards. Whether you're a beginner or an experienced data analyst, this book offers a wealth of practical recipes to help you excel in your data analysis projects. This cookbook covers everything from fundamental data manipulation tasks to advanced techniques for handling big data, visualization, and more. Each recipe is designed to address common real-world challenges, providing clear explanations and step-by-step instructions to guide you through the process. Explore cutting-edge topics such as idiomatic pandas coding, efficient handling of large datasets, and advanced data visualization techniques. Whether you're looking to sharpen or expand your skills, the ""Pandas Cookbook"" is your essential companion for mastering data analysis and manipulation with pandas 2.x, and beyond. What you will learn The pandas type system and how to best navigate it Import/export DataFrames to/from common data formats Data exploration in pandas through dozens of practice problems Grouping, aggregation, transformation, reshaping, and filtering data Merge data from different sources through pandas SQL-like operations Leverage the robust pandas time series functionality in advanced analyses Scale pandas operations to get the most out of your system The large ecosystem that pandas can coordinate with and supplement Who this book is for This book is for Python developers, data scientists, engineers, and analysts. pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas".
Python (Computer program language) --- Programming languages (Electronic computers) --- Data mining. --- Science --- Mathematics --- Computer programs.
Listing 1 - 7 of 7 |
Sort by
|