Python Data Science Essentials : A Practitioner’s Guide Covering Essential Data Science Principles, Tools, and Techniques, 3rd Edition

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Cover Title Page Copyright and Credits Packt Upsell Contributors Table of Contents Preface Chapter 1: First Steps Introducing data science and Python Installing Python Python 2 or Python 3? Step-by-step installation Installing the necessary packages Package upgrades Scientific distributions Anaconda Leveraging conda to install packages Enthought Canopy WinPython Explaining virtual environments Conda for managing environments A glance at the essential packages NumPy SciPy pandas pandas-profiling Scikit-learn Jupyter JupyterLab Matplotlib Seaborn Statsmodels Beautiful Soup NetworkX NLTK Gensim PyPy XGBoost LightGBM CatBoost TensorFlow Keras Introducing Jupyter Fast installation and first test usage Jupyter magic commands Installing packages directly from Jupyter Notebooks Checking the new JupyterLab environment How Jupyter Notebooks can help data scientists Alternatives to Jupyter Datasets and code used in this book Scikit-learn toy datasets The MLdata.org and other public repositories for open source data LIBSVM data examples Loading data directly from CSV or text files Scikit-learn sample generators Summary Chapter 2: Data Munging The data science process Data loading and preprocessing with pandas Fast and easy data loading Dealing with problematic data Dealing with big datasets Accessing other data formats Putting data together Data preprocessing Data selection Working with categorical and textual data A special type of data – text Scraping the web with Beautiful Soup Data processing with NumPy NumPy's n-dimensional array The basics of NumPy ndarray objects Creating NumPy arrays From lists to unidimensional arrays Controlling memory size Heterogeneous lists From lists to multidimensional arrays Resizing arrays Arrays derived from NumPy functions Getting an array directly from a file Extracting data from pandas NumPy fast operation and computations Matrix operations Slicing and indexing with NumPy arrays Stacking NumPy arrays Working with sparse arrays Summary Chapter 3: The Data Pipeline Introducing EDA Building new features Dimensionality reduction The covariance matrix Principal component analysis PCA for big data – RandomizedPCA Latent factor analysis Linear discriminant analysis Latent semantical analysis Independent component analysis Kernel PCA T-SNE Restricted Boltzmann Machine The detection and treatment of outliers Univariate outlier detection EllipticEnvelope OneClassSVM Validation metrics Multilabel classification Binary classification Regression Testing and validating Cross-validation Using cross-validation iterators Sampling and bootstrapping Hyperparameter optimization Building custom scoring functions Reducing the grid search runtime Feature selection Selection based on feature variance Univariate selection Recursive elimination Stability and L1-based selection Wrapping everything in a pipeline Combining features together and chaining transformations Building custom transformation functions Summary Chapter 4: Machine Learning Preparing tools and datasets Linear and logistic regression Naive Bayes K-Nearest Neighbors Nonlinear algorithms SVM for classification SVM for regression Tuning SVM Ensemble strategies Pasting by random samples Bagging with weak classifiers Random Subspaces and Random Patches Random Forests and Extra-Trees Estimating probabilities from an ensemble Sequences of models – AdaBoost Gradient tree boosting (GTB) XGBoost LightGBM CatBoost Dealing with big data Creating some big datasets as examples Scalability with volume Keeping up with velocity Dealing with variety An overview of Stochastic Gradient Descent (SGD) A peek into natural language processing (NLP) Word tokenization Stemming Word tagging Named entity recognition (NER) Stopwords A complete data science example – text classification An overview of unsupervised learning K-means DBSCAN – a density-based clustering technique Latent Dirichlet Allocation (LDA) Summary Chapter 5: Visualization, Insights, and Results Introducing the basics of matplotlib Trying curve plotting Using panels for clearer representations Plotting scatterplots for relationships in data Histograms Bar graphs Image visualization Selected graphical examples with pandas Working with boxplots and histograms Plotting scatterplots Discovering patterns by parallel coordinates Wrapping up matplotlib's commands Introducing Seaborn Enhancing your EDA capabilities Advanced data learning representation Learning curves Validation curves Feature importance for RandomForests Gradient Boosting Trees partial dependence plotting Creating a prediction server with machine-learning-as-a-service Summary Chapter 6: Social Network Analysis Introduction to graph theory Graph algorithms Types of node centrality Partitioning a network Graph loading, dumping, and sampling Summary Chapter 7: Deep Learning Beyond the Basics Approaching deep learning Classifying images with CNN Using pre-trained models Working with temporal sequences Summary Chapter 8: Spark for Big Data From a standalone machine to a bunch of nodes Making sense of why we need a distributed framework The Hadoop ecosystem Hadoop architecture Hadoop Distributed File System MapReduce Introducing Apache Spark PySpark Starting with PySpark Setting up your local Spark instance Experimenting with Resilient Distributed Datasets Sharing variables across cluster nodes Read-only broadcast variables Write-only accumulator variables Broadcast and accumulator variables together—an example Data preprocessing in Spark CSV files and Spark DataFrames Dealing with missing data Grouping and creating tables in-memory Writing the preprocessed DataFrame or RDD to disk Working with Spark DataFrames Machine learning with Spark Spark on the KDD99 dataset Reading the dataset Feature engineering Training a learner Evaluating a learner's performance The power of the machine learning pipeline Manual tuning Cross-validation Final cleanup Summary Appendix: Strengthen Your Python Foundations Your learning list Lists Dictionaries Defining functions Classes, objects, and object-oriented programming Exceptions Iterators and generators Conditionals Comprehensions for lists and dictionaries Learn by watching, reading, and doing Massive open online courses (MOOCs) PyCon and PyData Interactive Jupyter Don't be shy, take a real challenge Other Books You May Enjoy Index Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You'll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book. Python Data Science Essentials, Third Edition provides modern insight in setting up and performing data science operations effectively using the latest python tools and libraries. It builds faster governance on the most essential tasks such as data munging and pre-processing, along with all the techniques you require.