Python data analysis data manipulation and complex data analysis with Python, second edition

Learn how to apply powerful data analysis techniques with popular open source Python modulesAbout 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 projectsWho This Book Is ForThis 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 like NumPy, SciPy, Pandas, stasmodels, scikit-learn, theano, keras, and tensorflow on various platforms\* Prepare, 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, 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, Deep Learning and EnsemblesIn DetailData analysis allows making sense of heaps of data. Python, with its strong set of libraries, is a popular language used today to conduct various data analysis, machine learning and visualization tasks.With this book, you will learn about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and time-series analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time. Cover Credits About the Author About the Reviewers www.PacktPub.com Customer Feedback Table of Contents Preface Chapter 1: Getting Started with Python Libraries Installing Python 3 Installing data analysis libraries On Linux or Mac OS X On Windows Using IPython as a shell Reading manual pages Jupyter Notebook NumPy arrays A simple application Where to find help and references Listing modules inside the Python libraries Visualizing data using Matplotlib Summary Chapter 2: NumPy Arrays The NumPy array object Advantages of NumPy arrays Creating a multidimensional array. Selecting NumPy array elementsNumPy numerical types Data type objects Character codes The dtype constructors The dtype attributes One-dimensional slicing and indexing Manipulating array shapes Stacking arrays Splitting NumPy arrays NumPy array attributes Converting arrays Creating array views and copies Fancy indexing Indexing with a list of locations Indexing NumPy arrays with Booleans Broadcasting NumPy arrays Summary References Chapter 3: The Pandas Primer Installing and exploring Pandas The Pandas DataFrames The Pandas Series Querying data in Pandas. Statistics with Pandas DataFramesData aggregation with Pandas DataFrames Concatenating and appending DataFrames Joining DataFrames Handling missing values Dealing with dates Pivot tables Summary References Chapter 4: Statistics and Linear Algebra Basic descriptive statistics with NumPy Linear algebra with NumPy Inverting matrices with NumPy Solving linear systems with NumPy Finding eigenvalues and eigenvectors with NumPy NumPy random numbers Gambling with the binomial distribution Sampling the normal distribution Performing a normality test with SciPy. Creating a NumPy masked arrayDisregarding negative and extreme values Summary Chapter 5: Retrieving, Processing, and Storing Data Writing CSV files with NumPy and Pandas The binary .npy and pickle formats Storing data with PyTables Reading and writing Pandas DataFrames to HDF5 stores Reading and writing to Excel with Pandas Using REST web services and JSON Reading and writing JSON with Pandas Parsing RSS and Atom feeds Parsing HTML with Beautiful Soup Summary Reference Chapter 6: Data Visualization The matplotlib subpackages Basic matplotlib plots Logarithmic plots. Scatter plotsLegends and annotations Three-dimensional plots Plotting in Pandas Lag plots Autocorrelation plots Plot.ly Summary Chapter 7: Signal Processing and Time Series The statsmodels modules Moving averages Window functions Defining cointegration Autocorrelation Autoregressive models ARMA models Generating periodic signals Fourier analysis Spectral analysis Filtering Summary Chapter 8: Working with Databases Lightweight access with sqlite3 Accessing databases from Pandas SQLAlchemy Installing and setting up SQLAlchemy Populating a database with SQLAlchemy. 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. Annotation Learn how to apply powerful data analysis techniques with popular open source Python modulesAbout 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 ForThis 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 analysisIn DetailData 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 approachThe 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 Learn how to apply powerful data analysis techniques with popular open source Python modulesKey Features[•]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.Book DescriptionData 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.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 analysisWho this book is forThis 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. "This volume comprises the 61 revised refereed papers accepted for presentation at the ICEC/PPSN III conferences held jointly in Jerusalem, Israel in October 1994. With the appearance of more and more powerful computers, there is increased interest in algorithms relying upon analogies to natural processes. This book presents a wealth of new theoretical and experimental results on artificial problem solving by applying evolutionary computation metaphors, including evolution strategies, evolutionary programming, genetic algorithms, genetic programming, and classifier systems. Topics such as simulated annealing, immune networks, neural networks, fuzzy systems, and complex, real-world optimization problems are also treated."--Publisher's Website.

This volume comprises the 61 revised refereed papers accepted for presentation at the ICEC/PPSN III conferences held jointly in Jerusalem, Israel in October 1994.
With the appearance of more and more powerful computers, there is increased interest in algorithms relying upon analogies to natural processes. This book presents a wealth of new theoretical and experimental results on artificial problem solving by applying evolutionary computation metaphors, including evolution strategies, evolutionary programming, genetic algorithms, genetic programming, and classifier systems. Topics such as simulated annealing, immune networks, neural networks, fuzzy systems, and complex, real-world optimization problems are also treated.