Pandas Essentials For Data Analysis
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Python Data Science Handbook
Author: Jake VanderPlas
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-11-21
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Pandas Essentials for Data Analysis
"Pandas Essentials for Data Analysis" Unlock the full power of data analysis with "Pandas Essentials for Data Analysis," a sophisticated and comprehensive resource for professionals, academics, and practitioners seeking mastery over the Pandas ecosystem. This book delves deeply into core structures such as Series and DataFrames, offering rigorous explanations of theoretical underpinnings, memory optimization, and performance nuances. Readers will gain practical fluency in advanced indexing, custom accessor creation, and seamless handling of diverse data types, preparing them to architect robust and efficient analytical pipelines. From high-performance data ingestion across heterogeneous sources to sophisticated data cleaning, transformation, and temporal analytics, the book provides actionable guidance on every aspect of the data workflow. Explore advanced topics such as imputation strategies, scalable join algorithms, and time series engineering, alongside best practices for ensuring data integrity, reproducibility, and automated validation. Extensive coverage is given to visualization, reporting, and the integration of Pandas with leading machine learning frameworks, ensuring your analyses are both insightful and production-ready. Through detailed case studies spanning finance, healthcare, web analytics, natural language processing, geospatial applications, and industrial IoT, "Pandas Essentials for Data Analysis" bridges the gap between foundational knowledge and real-world expertise. The final chapters expound on writing reliable, maintainable code and navigating evolving best practices in the Pandas and PyData landscape, equipping readers to confidently meet today’s demanding data challenges and tomorrow’s innovations.
Python for Data Analysis
Author: Wes McKinney
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-09-25
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. 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 IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) 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 examples