Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- IPython -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentation, and Multi-lines -- Quotations and Comments -- Saving Your Code in a Module -- Some Standard Modules -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types -- Working with Numbers, Working with Other Bases -- The chr() Function -- The round() Function -- Formatting Numbers -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings -- Uninitialized Variables and the Value None -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Search and Replace a String in Other Strings -- Remove Leading and Trailing Characters -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Converting Strings to Dates -- Exception Handling, Handling User Input -- Command-line Arguments -- Summary -- Chapter 2: Working with Data -- Dealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data Versus Continuous Data -- Binning Continuous Data -- Scaling Numeric Data via Normalization -- Scaling Numeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- Mapping Categorical Data to Numeric Values -- Working with Dates -- Working with Currency, Working with Outliers and Anomalies -- Outlier Detection/Removal -- Finding Outliers with NumPy -- Finding Outliers with Pandas -- Calculating Z-scores to Find Outliers -- Finding Outliers with SkLearn (Optional) -- Working with Missing Data -- Imputing Values: When is Zero a Valid Value? -- Dealing with Imbalanced Datasets -- What is SMOTE? -- SMOTE extensions -- The Bias-Variance Tradeoff -- Types of Bias in Data -- Analyzing Classifiers (Optional) -- What is LIME? -- What is ANOVA? -- Summary -- Chapter 3: Introduction to Probability and Statistics -- What is a Probability?, and Calculating the Expected Value -- Random Variables -- Discrete versus Continuous Random Variables -- Well-known Probability Distributions -- Fundamental Concepts in Statistics -- The Mean -- The Median -- The Mode -- The Variance and Standard Deviation -- Population, Sample, and Population Variance -- Chebyshev's Inequality -- What is a p-value? -- The Moments of a Function (Optional) -- What is Skewness? -- What is Kurtosis? -- Data and Statistics -- The Central Limit Theorem -- Correlation versus Causation -- Statistical Inferences -- Statistical Terms: RSS, TSS, R^2, and F1 Score
Summary
This book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. --
