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

Explore the power of Jupyter and start deploying it in various contexts with the help of this highly practical, up-to-date guideAbout This Book* Find an easy way to code, execute, document, and share your live code with this unique guide* Learn to code efficient notebooks for interactive data analysis and deploy dashboards as web applications* This example-rich guide teaches you how to use Jupyter kernels for the major players in data science-Python, R, and JuliaWho This Book Is ForThis book is for those who are already familiar with data analytics languages such as Python, Scala, or R. It will be particularly useful for those who have used Jupyter but want to take their data analytics skills to the next level by utilizing Jupyter and other data science tools.What you will learn* Understand all the functionalities of Jupyter Notebooks such as creating / sharing documents with code, equations, and visualizations* Find out how to secure and share Notebooks* Use Python, Scala, and R with multiple other packages and other web applications to build different data visualization platforms with Jupyter* Create Jupyter Extensions to build new applications* Develop code that is efficient and effective in the realm of data science / analysis* Build interactive dashboards / widgets of Notebooks* Improve the scalability and performance of Notebooks* Use Jupyter for Machine LearningIn DetailJupyter is a very popular web application that allows you to code, analyze vast amounts of data, and create visualization, text, and rich media in a single document that can be shared across people you wish to collaborate with. Initially designed and used for statistical analysis and creating visualizations out of data, Jupyter Notebook has become so popular among data scientists that over 150,000 Jupyter Notebooks have been created on GitHub.This book will take you a step further with notebooks and help you to build multiple data analytics platforms using Jupyter and other data science tools. You'll learn different ways to engineer your data and analyze it for different purposes using multiple Jupyter Notebooks. You'll begin by setting up a data science environment to create and share Jupyter Notebooks. You'll learn to create Jupyter Notebooks to analyze simple to complex big datasets and visualize using python packages.Further on, you'll learn to capitalize on Python's flexibility and R's structured statistical packages to accelerate your data science investigations for real-world applications. Additionally, we'll cover techniques to scale an application using Jupyter extensions for Spark and other dynamic widgets. By the end of the book, you'll have gained mastery over creating and integrating multiple notebooks, being able to secure and optimize them to perform intuitive, iterative, and robust analytics.

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

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

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key 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 Description Data 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 for This 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.

Step-by-step guide to practising data science techniques with Jupyter notebooksKey features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps Cluster, transform, visualize, and extract insights from unlabelled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you'll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques.Who this book is forThe book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience.Table of contents1. Data Science Fundamentals2. Installing Software and Setting up3. Lists and Dictionaries4. Function and Packages5. NumPy Foundation6. Pandas and Dataframe7. Interacting with Databases8. Thinking Statistically in Data Science9. How to import data in Python?10. Cleaning of imported data11. Data Visualization12. Data Pre-processing13. Supervised Machine Learning14. Unsupervised Machine Learning15. Handling Time-Series Data16. Time-Series Methods 17. Case Study - 118. Case Study - 219. Case Study - 320. Case Study - 4About the authorPrateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.