Python Data Science Essentials

Description

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of 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 Copyright Credits About the Authors About the Reviewer www.PacktPub.com Table of Contents Preface Chapter 1: First Steps Introducing data science and Python Installing Python Python 2 or Python 3? Step-by-step installation The installation of packages Package upgrades Scientific distributions Anaconda Leveraging conda to install packages Enthought Canopy PythonXY WinPython Explaining virtual environments conda for managing environments A glance at the essential packages NumPy SciPy pandas Scikit-learn Jupyter Matplotlib Statsmodels Beautiful Soup NetworkX NLTK Gensim PyPy XGBoost Theano Keras Introducing Jupyter Fast installation and first test usage Jupyter magic commands How Jupyter Notebooks can help data scientists Alternatives to Jupyter Datasets and code used in the book Scikit-learn toy datasets The MLdata.org public repository 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 Data preprocessing Data selection Working with categorical and text 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 the 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's fast operations and computations Matrix operations Slicing and indexing with NumPy arrays Stacking NumPy arrays Summary Chapter 3: The Data Pipeline Introducing EDA Building new features Dimensionality reduction The covariance matrix Principal Component Analysis (PCA) PCA for big data – RandomizedPCA Latent Factor Analysis (LFA) Linear Discriminant Analysis (LDA) Latent Semantical Analysis (LSA) Independent Component Analysis (ICA) Kernel PCA T-SNE Restricted Boltzmann Machine (RBM) 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 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) Approaching deep learning A peek at 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 Summary Chapter 5: Social Network Analysis Introduction to graph theory Graph algorithms Graph loading, dumping, and sampling Summary Chapter 6: Visualization, Insights, and Results Introducing the basics of matplotlib Curve plotting Using panels Scatterplots for relationships in data Histograms Bar graphs Image visualization Selected graphical examples with pandas Boxplots and histograms Scatterplots Parallel coordinates Wrapping up matplotlib's commands Introducing Seaborn Enhancing your EDA capabilities Interactive visualizations with Bokeh Advanced data-learning representations Learning curves Validation curves Feature importance for RandomForests GBT partial dependence plots Creating a prediction server for ML-AAS Summary Appendix: Strengthen Your Python Foundations Your learning list Lists Dictionaries Defining functions Classes, objects, and OOP Exceptions Iterators and generators Conditionals Comprehensions for lists and dictionaries Learn by watching, reading, and doing MOOCs PyCon and PyData Interactive Jupyter Don't be shy, take a real challenge Index