Beschreibung Practical Data Analysis Using Jupyter Notebook: Learn how to speak the language of data by extracting useful and actionable insights using Python (English Edition). Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter NotebookKey FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook DescriptionData literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is forThis book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book. Table of ContentsFundamentals of data analysisOverview of Python and Installation of Jupyter notebookGetting Started with NumPyCreating your first Pandas DataFrameGathering and Loading Data in PythonVisualizing and working with time series dataExploring Cleaning, Refining and Blending DatasetsUnderstanding Joins, Relationships and Data AggregatesPlotting, Visualization and StorytellingExploring Text Data and Unstructured DataPractical Sentiment AnalysisDiscovering Patterns in Data and providing insights
Python Data Science Handbook - GitHub Pages ~ This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks.. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license.. If you find this content useful, please consider supporting the work by buying the book!
Project Jupyter / Home ~ Language of choice. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. Share notebooks. Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. Interactive output. Your code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types. Big data integration. Leverage big data tools, such as .
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Practical Data Analysis With Python ~ jupyter notebook this is the code repository for practical data analysis using jupyter notebook published by packt learn how to speak the language of data by extracting useful and actionable insights using python written by wes mckinney the creator of the python pandas project this book is a practical modern introduction to data science tools in python its ideal for analysts new to python and .
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GitHub - PacktPublishing/Pandas-Cookbook: Pandas Cookbook ~ The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Instructions and Navigation. All of the code is organized into folders .
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