Python Data Science Cookbook (eBook, ePUB)
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For data scientists who need immediate solutions, this book walks through common Python challenges and shows how to transform messy data into actionable insights. Beginning with nested JSON flattening and imputation of missing values, it guides through outlier clipping, feature scaling, duplicate removal, and efficient data structures. Exploratory techniques illustrate how to compute descriptive statistics, generate histograms and correlation heatmaps, build pivot tables, and visualize time-series trends. Feature engineering recipes cover polynomial transformations, scaling strategies, rolling...
For data scientists who need immediate solutions, this book walks through common Python challenges and shows how to transform messy data into actionable insights. Beginning with nested JSON flattening and imputation of missing values, it guides through outlier clipping, feature scaling, duplicate removal, and efficient data structures. Exploratory techniques illustrate how to compute descriptive statistics, generate histograms and correlation heatmaps, build pivot tables, and visualize time-series trends. Feature engineering recipes cover polynomial transformations, scaling strategies, rolling averages, dimensionality reduction, and encoding of complex categorical fields.
The later chapters integrate these steps into automated workflows that include imputation, scaling, feature selection, and custom transformations. Statistical testing and machine-learning sections demonstrate hypothesis evaluation, regression modeling, decision-tree construction, clustering, and performance metrics. A final troubleshooting section addresses common library errors and ensures consistent plotting and integration across pandas, NumPy, and visualization tools.
The later chapters integrate these steps into automated workflows that include imputation, scaling, feature selection, and custom transformations. Statistical testing and machine-learning sections demonstrate hypothesis evaluation, regression modeling, decision-tree construction, clustering, and performance metrics. A final troubleshooting section addresses common library errors and ensures consistent plotting and integration across pandas, NumPy, and visualization tools.
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