Mastering Python Data Analysis

Description

Mastering Python Data Analysis Mastering Python Data Analysis Credits About the Authors About the Reviewer www.PacktPub.com Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Tools of the Trade Before you start Using the notebook interface Imports An example using the Pandas library Summary 2. Exploring Data The General Social Survey Obtaining the data Reading the data Univariate data Histograms Making things pretty Characterization Concept of statistical inference Numeric summaries and boxplots Relationships between variables – scatterplots Summary 3. Learning About Models Models and experiments The cumulative distribution function Working with distributions The probability density function Where do models come from? Multivariate distributions Summary 4. Regression Introducing linear regression Getting the dataset Testing with linear regression Multivariate regression Adding economic indicators Taking a step back Logistic regression Some notes Summary 5. Clustering Introduction to cluster finding Starting out simple – John Snow on cholera K-means clustering Suicide rate versus GDP versus absolute latitude Hierarchical clustering analysis Reading in and reducing the data Hierarchical cluster algorithm Summary 6. Bayesian Methods The Bayesian method Credible versus confidence intervals Bayes formula Python packages U.S. air travel safety record Getting the NTSB database Binning the data Bayesian analysis of the data Binning by month Plotting coordinates Cartopy Mpl toolkits – basemap Climate change - CO2 in the atmosphere Getting the data Creating and sampling the model Summary 7. Supervised and Unsupervised Learning Introduction to machine learning Scikit-learn Linear regression Climate data Checking with Bayesian analysis and OLS Clustering Seeds classification Visualizing the data Feature selection Classifying the data The SVC linear kernel The SVC Radial Basis Function The SVC polynomial K-Nearest Neighbour Random Forest Choosing your classifier Summary 8. Time Series Analysis Introduction Pandas and time series data Indexing and slicing Resampling, smoothing, and other estimates Stationarity Patterns and components Decomposing components Differencing Time series models Autoregressive – AR Moving average – MA Selecting p and q Automatic function The (Partial) AutoCorrelation Function Autoregressive Integrated Moving Average – ARIMA Summary A. More on Jupyter Notebook and matplotlib Styles Jupyter Notebook Useful keyboard shortcuts Command mode shortcuts Edit mode shortcuts Markdown cells Notebook Python extensions Installing the extensions Codefolding Collapsible headings Help panel Initialization cells NbExtensions menu item Ruler Skip-traceback Table of contents Other Jupyter Notebook tips External connections Export Additional file types Matplotlib styles Useful resources General resources Packages Data repositories Visualization of data Summary