Stai cercando i migliori ipython notebook? Allora sei nel posto giusto! Abbiamo creato una classifica con i 10 modelli che reputiamo migliori, la nostra graduatoria prende in considerazione diversi parametri come il prezzo, la reputazione del brand, l’apprezzamento e le critiche nel web, le recensioni delle persone che lo hanno acquistato e anche se ci sono eventuali offerte.

Guida all’acquisto dei migliori ipython notebook

Siamo sicuri che grazie alla nostra classifica troverai il prodotto che più rispecchia le tue esigenze, in ogni caso prima di procedere all’acquisto ti vogliamo dare alcuni consigli che potranno tornarti utili. Quando fai acquisti online (e anche in negozi fisici) è sempre buona norma segnarsi le caratteristiche che cerchiamo in un determinato prodotto, dopo che abbiamo segnato queste specifiche possiamo analizzare la classifica e vedere se il prodotto ha tutto quello che cerchiamo (clicca sul pulsante “Maggiori informazioni” dentro le scheda prodotto della classifica per vedere informazioni interessanti che possono tornarti utili per la scelta).

Non dimenticarti di fissare un budget per la spesa, anche in questo caso ricorda che spendere meno non è sempre una buona idea, a volte per risparmiare rinunciamo a caratteristiche fondamentali che in futuro ci faranno pentire della nostra scelta perché sentiamo la mancanza di quella particolare funzione di cui abbiamo bisogno. In questo caso la scelta migliore è di aumentare un pò il budget e cercare di capire se così riusciamo ad avere tutto quello di cui abbiamo necessità.

Anche spendere troppo per funzioni che non sono necessarie non è una buona scelta, può capitare che spendiamo di più e poi non utilizziamo a pieno il bene che abbiamo acquistato. Bisogna solo prestare un pò di attenzione nel momento dell’acquisto.

Inoltre, a volte, è meglio acquistare da marchi più conosciuti perché generalmente riescono a fornirti una migliore assistenza in caso di problemi (n.b. non è sempre detto, a volte capita proprio l’opposto) ed è per questo che nella nostra lista cerchiamo di tenere i migliori brand di ipython notebook.

Ecco 3 ipython notebook che consigliamo

Listed Volatility and Variance Derivatives: A Python-based Guide (Wiley Finance) (English Edition)

Al primo posto, sul gradino più alto del podio, abbiamo Listed Volatility and Variance Derivatives: A Python-based Guide (Wiley Finance) (English Edition), vi citiamo alcuni punti di forza di questo prodotto:

• Leverage Python for expert-level volatility and variance derivative trading Listed Volatility and Variance Derivatives is a comprehensive treatment of all aspects of these increasingly popular derivatives products, and has the distinction of being both the first to cover European volatility and variance products provided by Eurex and the first to offer Python code for implementing comprehensive quantitative analyses of these financial products. For those who want to get started right away, the book is accompanied by a dedicated Web page and a Github repository that includes all the code from the book for easy replication and use, as well as a hosted version of all the code for immediate execution. Python is fast making inroads into financial modelling and derivatives analytics, and recent developments allow Python to be as fast as pure C++ or C while consisting generally of only 10% of the code lines associated with the compiled languages. This complete guide offers rare insight into the use of Python to undertake complex quantitative analyses of listed volatility and variance derivatives. Learn how to use Python for data and financial analysis, and reproduce stylised facts on volatility and variance markets Gain an understanding of the fundamental techniques of modelling volatility and variance and the model-free replication of variance Familiarise yourself with micro structure elements of the markets for listed volatility and variance derivatives Reproduce all results and graphics with IPython/Jupyter Notebooks and Python codes that accompany the book Listed Volatility and Variance Derivatives is the complete guide to Python-based quantitative analysis of these Eurex derivatives products.

Svantaggi:

• Nessuna nota negativa da segnalare.

Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning (English Edition)

Il secondo prodotto che suggeriamo è Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning (English Edition), per completezza vi elenchiamo alcune caratteristiche interessanti:

• Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.Key FeaturesGet a hands-on, fast-paced introduction to the Python data science stackExplore ways to create useful metrics and statistics from large datasetsCreate detailed analysis reports with real-world dataBook DescriptionProcessing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools.By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs.What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or querying formatsPrepare data for statistical analysis, visualization, and machine learningPresent data in the form of effective visualsWho this book is forBig Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book. Table of ContentsThe Python Data Science StackStatistical VisualizationsWorking with Big Data FrameworksDiving Deeper with SparkHandling Missing Values and Correlation AnalysisExploratory Data AnalysisReproducibility in Big Data AnalysisCreating a Full Analysis Report

Svantaggi:

• Nessuna nota negativa da segnalare.

IPython Interactive Computing and Visualization Cookbook: Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and ... Notebook, 2nd Edition (English Edition)

Anche se è al gradino più basso del podio è un’ottima alternativa ai già citati prodotti, ecco delle funzioni che potrebbero esservi utili:

• Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanationsBook DescriptionPython is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform.IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theoryWho this book is forThis book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods. Table of ContentsA Tour of Interactive Computing with Jupyter and IPythonBest Practices in Interactive ComputingMastering the Jupyter NotebookProfiling and OptimizationHigh-Performance ComputingData VisualizationStatistical Data AnalysisMachine LearningNumerical OptimizationSignal ProcessingImage and Audio ProcessingDeterministic Dynamical SystemsStochastic Dynamical SystemsGraphs, Geometry, and Geographic Information SystemsSymbolic and Numerical Mathematics

Svantaggi:

• Nessuna nota negativa da segnalare.

Conclusione

Per concludere vi ringraziamo di cuore per aver letto la nostra guida e se il sito vi è stato utile o se vi è piaciuto aggiungeteci ai preferiti e condividetelo con i vostri amici! Inoltre se avete tempo potreste anche leggere i 10 migliori profumo da uomo in assoluto.