Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves intoParametric models with coverage ofConcept of maximum likelihood estimate (MLE) of a probability distribution parameterMLE of the survival functionCommon probability distributions and their analysisAnalysis of exponential distribution as a survival functionAnalysis of Weibull distribution as a survival functionDerivation of Gumbel distribution as a survival function from WeibullNon-parametric models includingKaplan-Meier (KM) estimator, a derivation of expression using MLEFitting KM estimator with an example dataset, Python code and plotting curvesGreenwood's formula and its derivationModels with covariates explainingThe concept of time shift and the accelerated failure time (AFT) modelWeibull-AFT model and derivation of parameters by MLEProportional Hazard (PH) modelCox-PH model and Breslow's methodSignificance of covariatesSelection of covariatesThe Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.