Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key Features Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods Get to grips with graph processing and sentiment analysis Book DescriptionData analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learn Explore data science and its various process models Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values Create interactive visualizations using Matplotlib, Seaborn, and Bokeh Retrieve, process, and store data in a wide range of formats Understand data preprocessing and feature engineering using pandas and scikit-learn Perform time series analysis and signal processing using sunspot cycle data Analyze textual data and image data to perform advanced analysis Get up to speed with parallel computing using Dask Who this book is forThis book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book * Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types * Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning * Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books Who This Book Is For This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed. What You Will Learn * Set up reproducible data analysis * Clean and transform data * Apply advanced statistical analysis * Create attractive data visualizations * Web scrape and work with databases, Hadoop, and Spark * Analyze images and time series data * Mine text and analyze social networks * Use machine learning and evaluate the results * Take advantage of parallelism and concurrency In Detail Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You'll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios. Style and Approach The book is written in "cookbook" style striving for high realism in data analysis. Through the recipe-based format, you can read each recipe separately as required and immediately apply the knowledge gained.

A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy. This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.

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This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.

The book is written in beginner's guide style with each aspect of NumPy demonstrated with real world examples and required screenshots. If you are a programmer, scientist, or engineer who has basic Python knowledge and would like to be able to do numerical computations with Python, this book is for you. No prior knowledge of NumPy is required.

Written in Cookbook style, the code examples will take your Numpy skills to the next level. This book will take Python developers with basic Numpy skills to the next level through some practical recipes.