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Titre : Python for Data Analysis : Data Wrangling with pandas, NumPy & Jupyter Type de document : Ouvrage Auteurs : Wes McKinney, Auteur Mention d'édition : Third Edition Editeur : Sebastopol [USA] : O'Reilly Année de publication : 2022 Importance : 561 p. Présentation : ill. ISBN/ISSN/EAN : 978-1-09-810403-0 Langues : Anglais (eng) Termes (thésaurus) : Analyse des données
Base de données
Big DataMots-clés (libres) : Python Résumé : Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, 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.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
- Use the Jupyter notebook and IPython shell for exploratory computing
- Learn basic and advanced features in NumPy
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examplesRéservation
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Titre : Python for Excel : A Modern Environment for automation and Data Analysis Type de document : Ouvrage Auteurs : Felix Zumstein, Auteur Autre Editeur : Sebastopol [USA] : O'Reilly Année de publication : 2021 Importance : 316 p. Présentation : ill. ISBN/ISSN/EAN : 978-1-4920-8100-5 Langues : Français (fre) Termes (thésaurus) : Analyse des données
AutomatisationMots-clés (libres) : Python Résumé : While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently.
Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started.
- Use Python without extensive programming knowledge
- Get started with modern tools, including Jupyter notebooks and Visual Studio code
- Use pandas to acquire, clean, and analyze data and replace typical Excel calculations
- Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports
- Use xlwings to build interactive Excel tools that use Python as a calculation engine
- Connect Excel to databases and CSV files and fetch data from the internet using Python code
- Use Python as a single tool to replace VBA, Power Query, and Power PivotRéservation
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Titre : Python for finance : Mastering data-driven finance Type de document : Ouvrage Auteurs : Yves Hilpisch, Auteur Editeur : Sebastopol [USA] : O'Reilly Année de publication : 2019 Importance : 691 p. Présentation : ill. ISBN/ISSN/EAN : 978-1-4920-2433-0 Langues : Français (fre) Termes (thésaurus) : Algorithme
Finance
Langage informatique
Programmation informatiqueRésumé : The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics.
Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.Réservation
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