Presents case studies and instructions on how to solve data analysis problems using Python.
python for data analysis
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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
If you need to learn how to use the Python Programming Language to implement your own Data Analysis solution, and you are searching for a reference to start from, then keep reading. Data Analysis represents now one of the most interesting and useful applications among all the possible applications of Machine Learning and Artificial Intelligence. It represents also an incredible opportunity if you want to grow your business or if you are searching for a new job, but it requires a solid background in terms of processes and technologies involved. Developing your first Data Analysis project is a challenging task, and it can be vital to rely on a good guide. This book can assist you in understanding What is and how Data Analysis works How to set up your home computer to start your projects How to develop a Data Analysis project How to use the most popular Data Analysis libraries Even if you have not a degree in computer science or math, even if you have never worked on a Data Analysis project, with this book you can understand how to benefit from this set of technologies and unlock their extraordinary potential. If you want to give a new meaning to your data using the most modern Data Analysis techniques, scroll up and click the buy now button!
Are you looking for a book with in-depth information on data analysis with Python? Do you want to know the basics of programming and know more in a short time? Know the data analysis with an easy and accessible programming language? "Python for Data Analysis" is the book that will give you the information you need to access the basics of programming related to data analysis with Python. Python has transitioned over the years to provide accurate solutions for the day-to-day problems that face society. More programmers are getting into the programming field to improve what has already been there. However, that process requires dedication and self-motivation. Programming is not for the faint-hearted, but with the right mindset, you can achieve more within a short duration. All you need to know are the basics of Python programming to begin the coding journey. This book combines information that is crucial in data analysis when using Python. The area of computer programming is now a popular discipline among many computer users. The emergence of this field as a popular area has seen the explosion of various software programs that can assist in data analysis and interpretation. At the moment, there exist different computer languages which can support a computer user to handle data through creation, storage, and recovery of information. The impact of these programs is to substitute the standard forms of analysis that are tedious and time-consuming. The plans have various attributes that enable users to explain data and create theories. This means that these programs are robust and reliable in conducting qualitative data analysis. The computer qualitative data analysis techniques, on the other hand, are liked by many because of their simplicity and have outstanding attributes that assist in data analysis. These attributes save time, are simple to study and understand and apply fewer finances when compared to programming languages. You will learn: Definition of Python programming as well as the skills that learners should possess for a successful adventure. The History of Python programming How to download and install Python The definition of Python variables and the crucial naming sequence that programmers should be well conversant with. Types of variables and data variables in Python Computer data management and analysis Top 10 Python Libraries ...and more. Would You Like To Know More? Scroll to the top of the page and select the buy now button
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 fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects. Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. What You Will Learn Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms Prepare and clean your data, and use it for exploratory analysis Manipulate your data with Pandas Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian Understand signal processing and time series data analysis Get to grips with graph processing and social network analysis In Detail Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.
Become an expert at using Python for advanced statistical analysis of data using real-world examples About This Book Clean, format, and explore data using graphical and numerical summaries Leverage the IPython environment to efficiently analyze data with Python Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed. What You Will Learn Read, sort, and map various data into Python and Pandas Recognise patterns so you can understand and explore data Use statistical models to discover patterns in data Review classical statistical inference using Python, Pandas, and SciPy Detect similarities and differences in data with clustering Clean your data to make it useful Work in Jupyter Notebook to produce publication ready figures to be included in reports In Detail Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You'll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You'll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you'll discover how to use Python's tools for supervised machine learning. Style and approach This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.