| Nota di contenuto |
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 -- Quotation 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 in Python -- Formatting Numbers in Python -- Working with Fractions -- Unicode and UTF-8 -- Working with Unicode -- Working with Strings -- Comparing Strings -- Formatting Strings in Python -- 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 Alignmet -- Working with Dates -- Converting Strings to Dates -- Exception Handling -- Handling User Input -- Command-Line Arguments -- Summary -- Chapter 2: Working with DataDealing with Data: What Can Go Wrong? -- What is Data Drift? -- What are Datasets? -- Data Preprocessing -- Data Types -- Preparing Datasets -- Discrete Data vs. Continuous Data -- "Binning" Continuous Data -- Scaling Numeric Data via Normalization -- ScalingNumeric Data via Standardization -- Scaling Numeric Data via Robust Standardization -- What to Look for in Categorical Data -- MappingCategorical 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 Pandas -- What is Pandas? -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- A Pandas Data Frame Example -- Describing a Pandas Data Frame -- Pandas Boolean Data Frames -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Converting Categorical Data to Numeric Data -- Merging and Splitting Columns in Pandas -- Combining Pandas Data Frames -- Data Manipulation with Pandas Data Frames -- Pandas Data Frames and CSV Files -- Useful Options for the Pandas read_csv() Function -- Reading Selected Rows from CSV Files -- Pandas Data Frames and Excel Spreadsheets -- Useful Options for Reading Excel Spreadsheets -- Select, Add, and Delete Columns in Data Frames -- Handling Outliers in Pandas -- Pandas Data Frames and Simple Statistics -- Finding Duplicate Rows in Pandas -- Finding Missing Values in Pandas -- Missing Values in an Iris-Based Dataset -- Sorting Data Frames in Pandas -- Working with groupby() in Pandas -- Aggregate Operations with the titanic.csv Dataset -- Working with apply() and mapapply() in Pandas -- Working with JSON-based Data -- Python Dictionary and JSON -- Python, Pandas, and JSON -- Summary -- Chapter 4: RDBMS and SQL -- What is an RDBMS? -- What Relationships Do Tables Have in an RDBMS? -- Features of an RDBMS -- What is ACID? -- When Do We Need an RDBMS? -- The Importance of Normalization -- A Four-Table RDBMS -- Detailed Table Descriptions -- The customers Table -- The purchase_orders Table -- The line_items Table -- The item_desc Table -- What is SQL? -- DCL, DDL, DQL, DML, and TCL -- SQL Privileges -- Properties of SQL Statements -- The CREATE Keyword -- What about MariaDB? -- Summary -- Index.
|
Info
Data Wrangling Using Pandas, SQL, and Java
